[ghc-hetmet.git] / docs / rts / rts.verb

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\title{The STG runtime system (revised)}
\author{Simon Peyton Jones \\ Glasgow University and Oregon Graduate Institute \and
Alastair Reid \\ Yale University} 

\maketitle

\tableofcontents
\newpage

\section{Introduction}

This document describes the GHC/Hugs run-time system.  It serves as 
a Glasgow/Yale/Nottingham ``contract'' about what the RTS does.

\subsection{New features compared to GHC 2.04}

\begin{itemize}
\item The RTS supports mixed compiled/interpreted execution, so
that a program can consist of a mixture of GHC-compiled and Hugs-interpreted
code.

\item CAFs are only retained if they are
reachable.  Since they are referred to by implicit references buried
in code, this means that the garbage collector must traverse the whole
accessible code tree.  This feature eliminates a whole class of painful
space leaks.

\item A running thread has only one stack, which contains a mixture
of pointers and non-pointers.  Section~\ref{sect:stacks} describes how
we find out which is which.  (GHC has used two stacks for some while.
Using one stack instead of two reduces register pressure, reduces the
size of update frames, and eliminates
``stack-stubbing'' instructions.)

\end{itemize} 

\subsection{Wish list}

Here's a list of things we'd like to support in the future.
\begin{itemize}
\item Interrupts, speculative computation.

\item
The SM could tune the size of the allocation arena, the number of
generations, etc taking into account residency, GC rate and page fault
rate.

\item
There should be no need to specify the amnount of stack/heap space to
allocate when you started a program - let it just take as much or as
little as it wants.  (It might be useful to be able to specify maximum
sizes and to be able to suggest an initial size.)

\item 
We could trigger a GC when all threads are blocked waiting for IO if
the allocation arena (or some of the generations) are nearly full.

\end{itemize}

\subsection{Configuration}

Some of the above features are expensive or less portable, so we
envision building a number of different configurations supporting
different subsets of the above features.

You can make the following choices:
\begin{itemize}
\item
Support for concurrency or parallelism.  There are four 
mutually-exclusive choices.

\begin{description}
\item[@SEQUENTIAL@] No concurrency or parallelism support.
  This configuration might not support interrupt recovery.
\item[@CONCURRENT@]  Support for concurrency but not for parallelism.
\item[@CONCURRENT@+@GRANSIM@] Concurrency support and simulated parallelism.
\item[@CONCURRENT@+@PARALLEL@]     Concurrency support and real parallelism.
\end{description}

\item @PROFILING@ adds cost-centre profiling.

\item @TICKY@ gathers internal statistics (often known as ``ticky-ticky'' code).

\item @DEBUG@ does internal consistency checks.

\item Persistence. (well, not yet).

\item
Which garbage collector to use.  At the moment we
only anticipate one, however.
\end{itemize}

\subsection{Terminology}

\begin{itemize}

\item A {\em word} is (at least) 32 bits and can hold either a signed
or an unsigned int.

\item A {\em pointer} is (at least) 32 bits and big enough to hold a
function pointer or a data pointer.  

\item A {\em closure} is a (representation of) a value of a {\em pointed}
 type.  It may be in HNF or it may be unevaluated --- in either case, you can
 try to evaluate it again.

\item A {\em thunk} is a (representation of) a value of a {\em pointed}
 type which is {\em not} in HNF.

\end{itemize}

Occasionally, a field of a data structure must hold either a word or a
pointer.  In such circumstances, it is {\em not safe} to assume that
words and pointers are the same size.

% Todo:
% More terminology to mention.
% unboxed, unpointed

There are a few other system invariants which need to be mentioned ---
though not necessarily here:

\begin{itemize}

\item The garbage collector never expands an object when it promotes
it to the old generation.  This is important because the GC avoids
performing heap overflow checks by assuming that the amount added to
the old generation is no bigger than the current new generation.

\end{itemize}


\section{The Scheduler}

The Scheduler is the heart of the run-time system.  A running program
consists of a single running thread, and a list of runnable and
blocked threads.  The running thread returns to the scheduler when any
of the following conditions arises:

\begin{itemize}
\item A heap check fails, and a garbage collection is required
\item Compiled code needs to switch to interpreted code, and vice
versa.
\item The thread becomes blocked.
\item The thread is preempted.
\end{itemize}

A running system has a global state, consisting of

\begin{itemize}
\item @Hp@, the current heap pointer, which points to the next
available address in the Heap.
\item @HpLim@, the heap limit pointer, which points to the end of the
heap.
\item The Thread Preemption Flag, which is set whenever the currently
running thread should be preempted at the next opportunity.
\end{itemize}

Each thread has a thread-local state, which consists of

\begin{itemize}
\item @TSO@, the Thread State Object for this thread.  This is a heap
object that is used to store the current thread state when the thread
is blocked or sleeping.
\item @Sp@, the current stack pointer.
\item @Su@, the current stack update frame pointer.  This register
points to the most recent update frame on the stack, and is used to
calculate the number of arguments available when entering a function.
\item @SpLim@, the stack limit pointer.  This points to the end of the
current stack chunk.
\item Several general purpose registers, used for passing arguments to
functions.
\end{itemize}

\noindent and various other bits of information used in specialised
circumstances, such as profiling and parallel execution.  These are
described in the appropriate sections.

The following is pseudo-code for the inner loop of the scheduler
itself.

@
while (threads_exist) {
  // handle global problems: GC, parallelism, etc
  if (need_gc) gc();  
  if (external_message) service_message();
  // deal with other urgent stuff

  pick a runnable thread;
  do {
    switch (thread->whatNext) {
      case (RunGHC  pc): status=runGHC(pc);  break;
      case (RunHugs bc): status=runHugs(bc); break;
    }
    switch (status) {  // handle local problems
      case (StackOverflow): enlargeStack; break;
      case (Error e)      : error(thread,e); break;
      case (ExitWith e)   : exit(e); break;
      case (Yield)        : break;
    }
  } while (thread_runnable);
}
@

Optimisations to avoid excess trampolining from Hugs into itself.
How do we invoke GC, ccalls, etc.
General ccall (@ccall-GC@) and optimised ccall.

\section{Evaluation}

This section describes the framework in which compiled code evaluates
expressions.  Only at certain points will compiled code need to be
able to talk to the interpreted world; these are discussed in Section
\ref{sec:hugs-ghc-interaction}.

\subsection{Calling conventions}

\subsubsection{The call/return registers}

One of the problems in designing a virtual machine is that we want it
abstract away from tedious machine details but still reveal enough of
the underlying hardware that we can make sensible decisions about code
generation.  A major problem area is the use of registers in
call/return conventions.  On a machine with lots of registers, it's
cheaper to pass arguments and results in registers than to pass them
on the stack.  On a machine with very few registers, it's cheaper to
pass arguments and results on the stack than to use ``virtual
registers'' in memory.  We therefore use a hybrid system: the first
$n$ arguments or results are passed in registers; and the remaining
arguments or results are passed on the stack.  For register-poor
architectures, it is important that we allow $n=0$.

We'll label the arguments and results \Arg{1} \ldots \Arg{m} --- with
the understanding that \Arg{1} \ldots \Arg{n} are in registers and
\Arg{n+1} \ldots \Arg{m} are on top of the stack.

Note that the mapping of arguments \Arg{1} \ldots \Arg{n} to machine
registers depends on the {\em kinds} of the arguments.  For example,
if the first argument is a Float, we might pass it in a different
register from if it is an Int.  In fact, we might find that a given
architecture lets us pass varying numbers of arguments according to
their types.  For example, if a CPU has 2 Int registers and 2 Float
registers then we could pass between 2 and 4 arguments in machine
registers --- depending on whether they all have the same kind or they
have different kinds.

\subsubsection{Entering closures}

To evaluate a closure we jump to the entry code for the closure
passing a pointer to the closure in \Arg{1} so that the entry code can
access its environment.

\subsubsection{Function call}

The function-call mechanism is obviously crucial.  There are five different
cases to consider:
\begin{enumerate}

\item {\em Known combinator (function with no free variables) and enough arguments.}  

A fast call can be made: push excess arguments onto stack and jump to
function's {\em fast entry point} passing arguments in \Arg{1} \ldots
\Arg{m}.  

The {\em fast entry point} is only called with exactly the right
number of arguments (in \Arg{1} \ldots \Arg{m}) so it can instantly
start doing useful work without first testing whether it has enough
registers or having to pop them off the stack first.

\item {\em Known combinator and insufficient arguments.}

A slow call can be made: push all arguments onto stack and jump to
function's {\em slow entry point}.

Any unpointed arguments which are pushed on the stack must be tagged.
This means pushing an extra word on the stack below the unpointed
words, containing the number of unpointed words above it.

%Todo: forward ref about tagging?
%Todo: picture?

The {\em slow entry point} might be called with insufficient arguments
and so it must test whether there are enough arguments on the stack.
This {\em argument satisfaction check} consists of checking that
@Su-Sp@ is big enough to hold all the arguments (including any tags).

\begin{itemize} 

\item If the argument satisfaction check fails, it is because there is
one or more update frames on the stack before the rest of the
arguments that the function needs.  In this case, we construct a PAP
(partial application, section~\ref{sect:PAP}) containing the arguments
which are on the stack.  The PAP construction code will return to the
update frame with the address of the PAP in \Arg{1}.

\item If the argument satisfaction check succeeds, we jump to the fast
entry point with the arguments in \Arg{1} \ldots \Arg{arity}.

If the fast entry point expects to receive some of \Arg{i} on the
stack, we can reduce the amount of movement required by making the
stack layout for the fast entry point look like the stack layout for
the slow entry point.  Since the slow entry point is entered with the
first argument on the top of the stack and with tags in front of any
unpointed arguments, this means that if \Arg{i} is unpointed, there
should be space below it for a tag and that the highest numbered
argument should be passed on the top of the stack.

We usually arrange that the fast entry point is placed immediately
after the slow entry point --- so we can just ``fall through'' to the
fast entry point without performing a jump.

\end{itemize}


\item {\em Known function closure (function with free variables) and enough arguments.}

A fast call can be made: push excess arguments onto stack and jump to
function's {\em fast entry point} passing a pointer to closure in
\Arg{1} and arguments in \Arg{2} \ldots \Arg{m+1}.

Like the fast entry point for a combinator, the fast entry point for a
closure is only called with appropriate values in \Arg{1} \ldots
\Arg{m+1} so we can start work straight away.  The pointer to the
closure is used to access the free variables of the closure.


\item {\em Known function closure and insufficient arguments.}

A slow call can be made: push all arguments onto stack and jump to the
closure's slow entry point passing a pointer to the closure in \Arg{1}.

Again, the slow entry point performs an argument satisfaction check
and either builds a PAP or pops the arguments off the stack into
\Arg{2} \ldots \Arg{m+1} and jumps to the fast entry point.


\item {\em Unknown function closure or thunk.}

Sometimes, the function being called is not statically identifiable.
Consider, for example, the @compose@ function:
@
  compose f g x = f (g x)
@
Since @f@ and @g@ are passed as arguments to @compose@, the latter has
to make a heap call.  In a heap call the arguments are pushed onto the
stack, and the closure bound to the function is entered.  In the
example, a thunk for @(g x)@ will be allocated, (a pointer to it)
pushed on the stack, and the closure bound to @f@ will be
entered. That is, we will jump to @f@s entry point passing @f@ in
\Arg{1}.  If \Arg{1} is passed on the stack, it is pushed on top of
the thunk for @(g x)@.

The {\em entry code} for an updateable thunk (which must have arity 0)
pushes an update frame on the stack and starts executing the body of
the closure --- using \Arg{1} to access any free variables.  This is
described in more detail in section~\ref{sect:data-updates}.

The {\em entry code} for a non-updateable closure is just the
closure's slow entry point.

\end{enumerate}

In addition to the above considerations, if there are \emph{too many}
arguments then the extra arguments are simply pushed on the stack with
appropriate tags.

To summarise, a closure's standard (slow) entry point performs the following:
\begin{description}
\item[Argument satisfaction check.] (function closure only)
\item[Stack overflow check.]
\item[Heap overflow check.]
\item[Copy free variables out of closure.] %Todo: why?
\item[Eager black holing.] (updateable thunk only) %Todo: forward ref.
\item[Push update frame.]
\item[Evaluate body of closure.]
\end{description}


\subsection{Case expressions and return conventions}
\label{sect:return-conventions}

The {\em evaluation} of a thunk is always initiated by
a @case@ expression.  For example:
@
  case x of (a,b) -> E
@

The code for a @case@ expression looks like this:

\begin{itemize}
\item Push the free variables of the branches on the stack (fv(@E@) in
this case).
\item  Push a \emph{return address} on the stack.
\item  Evaluate the scrutinee (@x@ in this case).
\end{itemize}

Once evaluation of the scrutinee is complete, execution resumes at the
return address, which points to the code for the expression @E@.

When execution resumes at the return point, there must be some {\em
return convention} that defines where the components of the pair, @a@
and @b@, can be found.  The return convention varies according to the
type of the scrutinee @x@:

\begin{itemize}

\item 

(A space for) the return address is left on the top of the stack.
Leaving the return address on the stack ensures that the top of the
stack contains a valid activation record
(section~\ref{sect:activation-records}) --- should a garbage collection
be required.

\item If @x@ has a pointed type (e.g.~a data constructor or a function),
a pointer to @x@ is returned in \Arg{1}.

\ToDo{Warn that components of E should be extracted as soon as
possible to avoid a space leak.}

\item If @x@ is an unpointed type (e.g.~@Int#@ or @Float#@), @x@ is
returned in \Arg{1}

\item If @x@ is an unpointed tuple constructor, the components of @x@
are returned in \Arg{1} \ldots \Arg{n} but no object is constructed in
the heap.  Unboxed tuple constructors are useful for functions which
want to return multiple values such as those used in an (explicitly
encoded) state monad:

\ToDo{Move stuff about unboxed tuples to a separate section}

@
data unpointed AAndState a = AnS a State
type S a = State -> AAndState a

bindS m k s0 = case m s0 of { AnS s1 a -> k s1 a }
returnS a s  = AnS a s
@

Note that unboxed datatypes can only have one constructor and that
thunks never have unboxed types --- so we'll never try to update an
unboxed constructor.  Unboxed tuples are \emph{never} built on the
heap.

When passing an unboxed tuple to a function, the components are
flattened out and passed in \Arg{1} \ldots \Arg{n} as usual.

\end{itemize}

\subsection{Vectored Returns}

Many algebraic data types have more than one constructor.  For
example, the @Maybe@ type is defined like this:
@
  data Maybe a = Nothing | Just a
@
How does the return convention encode which of the two constructors is
being returned?  A @case@ expression scrutinising a value of @Maybe@
type would look like this: 
@
  case E of 
    Nothing -> ...
    Just a  -> ...
@
Rather than pushing a return address before evaluating the scrutinee,
@E@, the @case@ expression pushes (a pointer to) a {\em return
vector}, a static table consisting of two code pointers: one for the
@Just@ alternative, and one for the @Nothing@ alternative.  

\begin{itemize}

\item

The constructor @Nothing@ returns by jumping to the first item in the
return vector with a pointer to a (statically built) Nothing closure
in \Arg{1}.  

It might seem that we could avoid loading \Arg{1} in this case since the
first item in the return vector will know that @Nothing@ was returned
(and can easily access the Nothing closure in the (unlikely) event
that it needs it.  The only reason we load \Arg{1} is in case we have to
perform an update (section~\ref{sect:data-updates}).

\item 

The constructor @Just@ returns by jumping to the second element of the
return vector with a pointer to the closure in \Arg{1}.  

\end{itemize}

In this way no test need be made to see which constructor returns;
instead, execution resumes immediately in the appropriate branch of
the @case@.

\subsection{Direct Returns}

When a datatype has a large number of constructors, it may be
inappropriate to use vectored returns.  The vector tables may be
large and sparse, and it may be better to identify the constructor
using a test-and-branch sequence on the tag.  For this reason, we
provide an alternative return convention, called a \emph{direct
return}.

In a direct return, the return address pushed on the stack really is a
code pointer.  The returning code loads a pointer to the closure being
returned in \Arg{1} as usual, and also loads the tag into \Arg{2}.
The code at the return address will test the tag and jump to the
appropriate code for the case branch.

The choice of whether to use a vectored return or a direct return is
made on a type-by-type basis --- up to a certain maximum number of
constructors imposed by the update mechanism
(section~\ref{sect:data-updates}).

Single-constructor data types also use direct returns, although in
that case there is no need to return a tag in \Arg{2}.

\ToDo{Say whether we pop the return address before returning}

\ToDo{Stack stubbing?}

\subsection{Updates}
\label{sect:data-updates}

The entry code for an updatable thunk (which must also be of arity 0):

\begin{itemize}
\item copies the free variables out of the thunk into registers or
  onto the stack.
\item pushes an {\em update frame} onto the stack.

An update frame is a small activation record consisting of
\begin{center}
\begin{tabular}{|l|l|l|}
\hline
{\em Fixed header} & {\em Update Frame link} & {\em Updatee} \\
\hline
\end{tabular}
\end{center}

\note{In the semantics part of the STG paper (section 5.6), an update
frame consists of everything down to the last update frame on the
stack.  This would make sense too --- and would fit in nicely with
what we're going to do when we add support for speculative
evaluation.}
\ToDo{I think update frames contain cost centres sometimes}

\item 
If we are doing ``eager blackholing,'' we then overwrite the thunk
with a black hole.  Otherwise, we leave it to the garbage collector to
black hole the thunk.

\item 
Start evaluating the body of the expression.

\end{itemize}

When the expression finishes evaluation, it will enter the update
frame on the top of the stack.  Since the returner doesn't know
whether it is entering a normal return address/vector or an update
frame, we follow exactly the same conventions as return addresses and
return vectors.  That is, on entering the update frame:

\begin{itemize} 
\item The value of the thunk is in \Arg{1}.  (Recall that only thunks
are updateable and that thunks return just one value.)

\item If the data type is a direct-return type rather than a
vectored-return type, then the tag is in \Arg{2}.

\item The update frame is still on the stack.
\end{itemize}

We can safely share a single statically-compiled update function
between all types.  However, the code must be able to handle both
vectored and direct-return datatypes.  This is done by arranging that
the update code looks like this:

@
                |       ^       |
                | return vector |
                |---------------|
                |  fixed-size   |
                |  info table   |
                |---------------|  <- update code pointer
                |  update code  |
                |       v       |
@

Each entry in the return vector (which is large enough to cover the
largest vectored-return type) points to the update code.

The update code:
\begin{itemize}
\item overwrites the {\em updatee} with an indirection to \Arg{1};
\item loads @Su@ from the Update Frame link;
\item removes the update frame from the stack; and 
\item enters \Arg{1}.
\end{itemize}

We enter \Arg{1} again, having probably just come from there, because
it knows whether to perform a direct or vectored return.  This could
be optimised by compiling special update code for each slot in the
return vector, which performs the correct return.

\subsection{Semi-tagging}
\label{sect:semi-tagging}

When a @case@ expression evaluates a variable that might be bound
to a thunk it is often the case that the scrutinee is already evaluated.
In this case we have paid the penalty of (a) pushing the return address (or
return vector address) on the stack, (b) jumping through the info pointer
of the scrutinee, and (c) returning by an indirect jump through the
return address on the stack.

If we knew that the scrutinee was already evaluated we could generate
(better) code which simply jumps to the appropriate branch of the @case@
with a pointer to the scrutinee in \Arg{1}.

An obvious idea, therefore, is to test dynamically whether the heap
closure is a value (using the tag in the info table).  If not, we
enter the closure as usual; if so, we jump straight to the appropriate
alternative.  Here, for example, is pseudo-code for the expression
@(case x of { (a,_,c) -> E }@:
@
      \Arg{1} = <pointer to x>;
      tag = \Arg{1}->entry->tag;
      if (isWHNF(tag)) {
          Sp--;  \\ insert space for return address
          goto ret;
      }
      push(ret);           
      goto \Arg{1}->entry;
      
      <info table for return address goes here>
ret:  a = \Arg{1}->data1; \\ suck out a and c to avoid space leak
      c = \Arg{1}->data3;
      <code for E2>
@
and here is the code for the expression @(case x of { [] -> E1; x:xs -> E2 }@:
@
      \Arg{1} = <pointer to x>;
      tag = \Arg{1}->entry->tag;
      if (isWHNF(tag)) {
          Sp--;  \\ insert space for return address
          goto retvec[tag];
      }
      push(retinfo);          
      goto \Arg{1}->entry;
      
      .addr ret2
      .addr ret1
retvec:           \\ reversed return vector
      <return info table for case goes here>
retinfo:
      panic("Direct return into vectored case");
      
ret1: <code for E1>

ret2: x  = \Arg{1}->head;
      xs = \Arg{1}->tail;
      <code for E2>
@
There is an obvious cost in compiled code size (but none in the size
of the bytecodes).  There is also a cost in execution time if we enter
more thunks than data constructors.

Both the direct and vectored returns are easily modified to chase chains
of indirections too.  In the vectored case, this is most easily done by
making sure that @IND = TAG_1 - 1@, and adding an extra field to every
return vector.  In the above example, the indirection code would be
@
ind:  \Arg{1} = \Arg{1}->next;
      goto ind_loop;
@
where @ind_loop@ is the second line of code.

Note that we have to leave space for a return address since the return
address expects to find one.  If the body of the expression requires a
heap check, we will actually have to write the return address before
entering the garbage collector.


\subsection{Heap and Stack Checks}

\note{I reckon these deserve a subsection of their own}

Don't move heap pointer before success occurs.
Talk about how stack check looks ahead into the branches of case expressions.

\subsection{Handling interrupts/signals}

@
May have to keep C stack pointer in register to placate OS?
May have to revert black holes - ouch!
@

\section{Switching Worlds}
\label{sect:switching-worlds}

Because this is a combined compiled/interpreted system, the
interpreter will sometimes encounter compiled code, and vice-versa.

All world-switches go via the scheduler, ensuring that the world is in
a known state ready to enter either compiled code or the interpreter.
When a thread is run from the scheduler, the @whatNext@ field is
checked to find out how to execute the thread.

\begin{itemize}
\item If @whatNext@ is set to @RunGHC@, we load up the required
registers from the TSO and jump to the address at the top of the user
stack.
\item If @whatNext@ is set to @RunHugs@, we execute the byte-code
object pointed to by the top word of the stack.
\end{itemize}

Sometimes instead of returning to the address at the top of the stack,
we need to enter a closure instead.  This is achieved by pushing a
pointer to the closure to be entered on the stack, followed by a
pointer to a canned code sequence called @ghc_entertop@, or the dual
byte-code object @hugs_entertop@.  Both code sequences do the following:

\begin{itemize}
\item pop the top word (either @ghc_entertop@ or @hugs_entertop@) from
the stack.
\item pop the next word off the stack and enter it.
\end{itemize}

There are six cases we need to consider:

\begin{enumerate}
\item A GHC thread enters a Hugs-built closure.
\item A GHC thread calls a Hugs-compiled function.
\item A GHC thread returns to a Hugs-compiled return address.
\item A Hugs thread enters a GHC-built closure.
\item A Hugs thread calls a GHC-compiled function.
\item A Hugs thread returns to a Hugs-compiled return address.
\end{enumerate}

We now examine the various cases one by one and describe how the
switch happens in each situation.

\subsection{A GHC thread enters a Hugs-built closure}

All Hugs-built closures look like this:

\begin{center}
\begin{tabular}{|l|l|}
\hline
\emph{Hugs} & \emph{Hugs-specific payload} \\
\hline 
\end{tabular}
\end{center}

\noindent where \emph{Hugs} is a pointer to a small statically
compiled-piece of code that does the following:

\begin{itemize}
\item Push the address of this thunk on the stack.
\item Push @hugs_entertop@ on the stack.
\item Save the current state of the thread in the TSO.
\item Return to the scheduler, with @whatNext@ set to @RunHugs@.
\end{itemize}

\ToDo{What about static thunks?  If all code lives on the heap, we'll
need an extra level of indirection for GHC references to Hugs
closures.}

\subsection{A GHC thread calls a Hugs-compiled function}

In order to call the fast entry point for a function, GHC needs arity
information from the defining module's interface file.  Hugs doesn't
supply this information, so GHC will always call the slow entry point
for functions in Hugs-compiled modules.

When a GHC module is linked into a running system, the calls to
external Hugs-compiled functions will be resolved to point to
dynamically-generated code that does the following:

\begin{itemize}
\item Push a pointer to the Hugs byte code object for the function on
the stack.
\item Push @hugs_entertop@ on the stack.
\item Save the current thread state in the TSO.
\item Return to the scheduler with @whatNext@ set to @RunHugs@
\end{itemize}

Ok, but how does Hugs find the byte code object for the function?
These live on the heap, and can therefore move around.  One solution
is to use a jump table, where each element in the table has two
elements:

\begin{itemize}
\item A call instruction pointing to the code fragment above.
\item A pointer to the byte-code object for the function.
\end{itemize}

When GHC jumps to the address in the jump table, the call takes it to
the statically-compiled code fragment, leaving a pointer to a pointer
to the byte-code object on the C stack, which can then be retrieved.

\subsection{A GHC thread returns to a Hugs-compiled return address}

When Hugs pushes return addresses on the stack, they look like this:

@
        |               |
        |_______________|
        |               |  -----> bytecode object
        |_______________|
        |               |  _____
        |_______________|       | 
                                |       _____
                                |       |    | Info Table
                                |       |    | 
                                |_____\ |____| hugs_return
                                      / .    .
                                        .    . Code
                                        .    .
@

If GHC is returning, it will return to the address at the top of the
stack.  This address a pointer to a statically compiled code fragment
called @hugs_return@, which:

\begin{itemize}
\item pops the return address off the user stack.
\item saves the thread state in the TSO
\item returns to the scheduler with @whatNext@ set to @RunHugs@.
\end{itemize}

\subsection{A Hugs thread enters a GHC-compiled closure}

When Hugs is called on to enter a GHC closure (these are recognisable
by the lack of a \emph{Hugs} pointer at the front), the following
sequence of instructions is executed:

\begin{itemize}
\item Push the address of the thunk on the stack.
\item Push @ghc_entertop@ on the stack.
\item Save the current state of the thread in the TSO.
\item Return to the scheduler, with the @whatNext@ field set to
@RunGHC@.
\end{itemize}

\subsection{A Hugs thread calls a GHC-compiled function}

Hugs never calls GHC-functions directly, it only enters closures
(which point to the slow entry point for the function).  Hence in this
case, we just push the arguments on the stack and proceed as above.

\subsection{A Hugs thread returns to a GHC-compiled return address}

The return address at the top of the stack is recognisable as a
GHC-return address by virtue of not being @hugs_return@.  In this
case, hugs recognises that it needs to do a world-switch and performs
the following sequence:

\begin{itemize}
\item save the state of the thread in the TSO.
\item return to the scheduler, setting @whatNext@ to @RunGHC@.
\end{itemize}

\section{Heap objects}
\label{sect:fixed-header}

\ToDo{Fix this picture}

\begin{figure}
\begin{center}
\input{closure}
\end{center}
\caption{A closure}
\label{fig:closure}
\end{figure}

Every {\em heap object}, or {\em closure} is a contiguous block
of memory, consisting of a fixed-format {\em header} followed
by zero or more {\em data words}.
The header consists of
the following fields:
\begin{itemize}
\item A one-word {\em info pointer}, which points to
the object's static {\em info table}.
\item Zero or more {\em admin words} that support
\begin{itemize}
\item Profiling (notably a {\em cost centre} word).
  \note{We could possibly omit the cost centre word from some 
  administrative objects.}
\item Parallelism (e.g. GranSim keeps the object's global address here,
though GUM keeps a separate hash table).
\item Statistics (e.g. a word to track how many times a thunk is entered.).

We add a Ticky word to the fixed-header part of closures.  This is
used to record indicate if a closure has been updated but not yet
entered. It is set when the closure is updated and cleared when
subsequently entered.

NB: It is {\em not} an ``entry count'', it is an
``entries-after-update count.''  The commoning up of @CONST@,
@CHARLIKE@ and @INTLIKE@ closures is turned off(?) if this is
required. This has only been done for 2s collection.



\end{itemize}
\end{itemize}
Most of the RTS is completely insensitive to the number of admin words.
The total size of the fixed header is @FIXED_HS@.

Many heap objects contain fields allowing them to be inserted onto lists
during evaluation or during garbage collection. The lists required by
the evaluator and storage manager are as follows.

\begin{itemize}
\item 2 lists of threads: runnable threads and sleeping threads.

\item The {\em static object list} is a list of all statically
allocated objects which might contain pointers into the heap.
(Section~\ref{sect:static-objects}.)

\item The {\em updated thunk list} is a list of all thunks in the old
generation which have been updated with an indirection.  
(Section~\ref{sect:IND_OLDGEN}.)

\item The {\em mutables list} is a list of all other objects in the
old generation which might contain pointers into the new generation.
Most of the object on this list are ``mutable.''
(Section~\ref{sect:mutables}.)

\item The {\em Foreign Object list} is a list of all foreign objects
 which have not yet been deallocated. (Section~\ref{sect:FOREIGN}.)

\item The {\em Spark pool} is a doubly(?) linked list of Spark objects
maintained by the parallel system.  (Section~\ref{sect:SPARK}.)

\item The {\em Blocked Fetch list} (or
lists?). (Section~\ref{sect:BLOCKED_FETCH}.)

\item For each thread, there is a list of all update frames on the
stack.  (Section~\ref{sect:data-updates}.)


\end{itemize}

\ToDo{The links for these fields are usually inserted immediately
after the fixed header except ...}

\subsection{Info Tables}

An {\em info table} is a contiguous block of memory, {\em laid out
backwards}.  That is, the first field in the list that follows
occupies the highest memory address, and the successive fields occupy
successive decreasing memory addresses.

\begin{center}
\begin{tabular}{|c|}
   \hline Parallelism Info 
\\ \hline Profile Info 
\\ \hline Debug Info 
\\ \hline Tag/bytecode pointer
\\ \hline Static reference table 
\\ \hline Storage manager layout info
\\ \hline Closure type 
\\ \hline entry code \ldots
\\ \hline
\end{tabular}
\end{center}
An info table has the following contents (working backwards in memory
addresses):
\begin{itemize}
\item The {\em entry code} for the closure.
This code appears literally as the (large) last entry in the
info table, immediately preceded by the rest of the info table.
An {\em info pointer} always points to the first byte of the entry code.

\item A one-word {\em closure type field}, @INFO_TYPE@, identifies what kind
of closure the object is.  The various types of closure are described
in Section~\ref{sect:closures}.
In some configurations, some useful properties of 
closures (is it a HNF?  can it be sparked?)
are represented as high-order bits so they can be tested quickly.

\item A single pointer or word --- the {\em storage manager info field},
@INFO_SM@, contains auxiliary information describing the closure's
precise layout, for the benefit of the garbage collector and the code
that stuffs graph into packets for transmission over the network.

\item A one-pointer {\em Static Reference Table (SRT) pointer}, @INFO_SRT@, points to
a table which enables the garbage collector to identify all accessible
code and CAFs.  They are fully described in Section~\ref{sect:srt}.

\item A one-pointer {\em tag/bytecode-pointer} field, @INFO_TAG@ or @INFO_BC@.  
For data constructors this field contains the constructor tag, in the
range $0..n-1$ where $n$ is the number of constructors.

For other objects that can be entered this field points to the byte
codes for the object.  For the constructor case you can think of the
tag as the name of a a suitable bytecode sequence but it can also be used to
implement semi-tagging (section~\ref{sect:semi-tagging}).

One awkward question (which may not belong here) is ``how does the
bytecode interpreter know whether to do a vectored return?''  The
answer is it examines the @INFO_TYPE@ field of the return address:
@RET_VEC_@$sz$ requires a vectored return and @RET_@$sz$ requires a
direct return.

\item {\em Profiling info\/}

Closure category records are attached to the info table of the
closure. They are declared with the info table. We put pointers to
these ClCat things in info tables.  We need these ClCat things because
they are mutable, whereas info tables are immutable.  Hashing will map
similar categories to the same hash value allowing statistics to be
grouped by closure category.

Cost Centres and Closure Categories are hashed to provide indexes
against which arbitrary information can be stored. These indexes are
memoised in the appropriate cost centre or category record and
subsequent hashes avoided by the index routine (it simply returns the
memoised index).

There are different features which can be hashed allowing information
to be stored for different groupings. Cost centres have the cost
centre recorded (using the pointer), module and group. Closure
categories have the closure description and the type
description. Records with the same feature will be hashed to the same
index value.

The initialisation routines, @init_index_<feature>@, allocate a hash
table in which the cost centre / category records are stored. The
lower bound for the table size is taken from @max_<feature>_no@. They
return the actual table size used (the next power of 2). Unused
locations in the hash table are indicated by a 0 entry. Successive
@init_index_<feature>@ calls just return the actual table size.

Calls to @index_<feature>@ will insert the cost centre / category
record in the @<feature>@ hash table, if not already inserted. The hash
index is memoised in the record and returned. 

CURRENTLY ONLY ONE MEMOISATION SLOT IS AVILABLE IN EACH RECORD SO
HASHING CAN ONLY BE DONE ON ONE FEATURE FOR EACH RECORD. This can be
easily relaxed at the expense of extra memoisation space or continued
rehashing.

The initialisation routines must be called before initialisation of
the stacks and heap as they require to allocate storage. It is also
expected that the caller may want to allocate additional storage in
which to store profiling information based on the return table size
value(s).

\begin{center}
\begin{tabular}{|l|}
   \hline Hash Index
\\ \hline Selected
\\ \hline Kind
\\ \hline Description String
\\ \hline Type String
\\ \hline
\end{tabular}
\end{center}

\begin{description}
\item[Hash Index] Memoised copy
\item[Selected] 
  Is this category selected (-1 == not memoised, selected? 0 or 1)
\item[Kind]
One of the following values (defined in CostCentre.lh):

\begin{description}
\item[@CON_K@]
A constructor.
\item[@FN_K@]
A literal function.
\item[@PAP_K@]
A partial application.
\item[@THK_K@]
A thunk, or suspension.
\item[@BH_K@]
A black hole.
\item[@ARR_K@]
An array.
\item[@ForeignObj_K@]
A Foreign object (non-Haskell heap resident).
\item[@SPT_K@]
The Stable Pointer table.  (There should only be one of these but it
represents a form of weak space leak since it can't shrink to meet
non-demand so it may be worth watching separately? ADR)
\item[@INTERNAL_KIND@]
Something internal to the runtime system.
\end{description}


\item[Description] Source derived string detailing closure description.
\item[Type] Source derived string detailing closure type.
\end{description}

\item {\em Parallelism info\/}
\ToDo{}

\item {\em Debugging info\/}
\ToDo{}

\end{itemize}



\section{Kinds of Heap Object}
\label{sect:closures}

Heap objects can be classified in several ways, but one useful one is
this:
\begin{itemize}
\item 
{\em Static closures} occupy fixed, statically-allocated memory
locations, with globally known addresses.

\item 
{\em Dynamic closures} are individually allocated in the heap.

\item 
{\em Stack closures} are closures allocated within a thread's stack
(which is itself a heap object).  Unlike other closures, there are
never any pointers to stack closures.  Stack closures are discussed in
Section~\ref{sect:stacks}.

\end{itemize}
A second useful classification is this:
\begin{itemize}
\item 
{\em Executive closures}, such as thunks and data constructors,
participate directly in a program's execution.  They can be subdivided into
two kinds of objects according to their type:
\begin{itemize}
\item 
{\em Pointed objects}, represent values of a {\em pointed} type
(<.pointed types launchbury.>) --i.e.~a type that includes $\bottom$ such as @Int@ or @Int# -> Int#@.

\item {\em Unpointed objects}, represent values of a {\em unpointed} type --i.e.~a type that does not include $\bottom$ such as @Int#@ or @Array#@.

\item {\em Activation frames}, represent ``continuations''.  They are
always stored on the stack and are never pointed to by heap objects or
passed as arguments.  \note{It's not clear if this will still be true
once we support speculative evaluation.}

\end{itemize}

\item {\em Administrative closures}, such as stack objects and thread
state objects, do not represent values in the original program.
\end{itemize}

Only pointed objects can be entered.  All pointed objects share a
common header format: the ``pointed header''; while all unpointed
objects share a common header format: the ``unpointed header''.
\ToDo{Describe the difference and update the diagrams to mention
an appropriate header type.}

This section enumerates all the kinds of heap objects in the system.
Each is identified by a distinct @INFO_TYPE@ tag in its info table.

\ToDo{Check this table very carefully}

\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|}
\hline

closure kind          & HNF & UPD & NS & STA & THU & MUT & UPT & BH & IND & Section \\

\hline                                                              
{\em Pointed} \\ 
\hline 

@CONSTR@              & 1   &     & 1  &     &     &     &     &    &     & \ref{sect:CONSTR}    \\
@CONSTR_STATIC@       & 1   &     & 1  & 1   &     &     &     &    &     & \ref{sect:CONSTR}    \\
@CONSTR_STATIC_NOCAF@ & 1   &     & 1  & 1   &     &     &     &    &     & \ref{sect:CONSTR}    \\
                                                                                                 
@FUN@                 & 1   &     & ?  &     &     &     &     &    &     & \ref{sect:FUN}       \\
@FUN_STATIC@          & 1   &     & ?  & 1   &     &     &     &    &     & \ref{sect:FUN}       \\
                                                                                                 
@THUNK@               & 1   & 1   &    &     & 1   &     &     &    &     & \ref{sect:THUNK}     \\
@THUNK_STATIC@        & 1   & 1   &    & 1   & 1   &     &     &    &     & \ref{sect:THUNK}     \\
@THUNK_SELECTOR@      & 1   & 1   & 1  &     & 1   &     &     &    &     & \ref{sect:THUNK_SEL}     \\
                                                                                                 
@PAP@                 & 1   &     & ?  &     &     &     &     &    &     & \ref{sect:PAP}       \\
                                                                                                 
@IND@                 &     &     & 1  &     & ?   &     &     &    & 1   & \ref{sect:IND}       \\
@IND_OLDGEN@          & 1   &     & 1  &     & ?   &     &     &    & 1   & \ref{sect:IND}       \\
@IND_PERM@            &     &     & 1  &     & ?   &     &     &    & 1   & \ref{sect:IND}       \\
@IND_OLDGEN_PERM@     & 1   &     & 1  &     & ?   &     &     &    & 1   & \ref{sect:IND}       \\
@IND_STATIC@          & ?   &     & 1  & 1   & ?   &     &     &    & 1   & \ref{sect:IND}       \\

\hline
{\em Unpointed} \\ 
\hline


@ARR_WORDS@           & 1   &     & 1  &     &     & 1   & 1   &    &     & \ref{sect:ARR_WORDS1},\ref{sect:ARR_WORDS2} \\
@ARR_PTRS@            & 1   &     & 1  &     &     & 1   & 1   &    &     & \ref{sect:ARR_PTRS}  \\
@MUTVAR@              & 1   &     & 1  &     &     & 1   & 1   &    &     & \ref{sect:MUTVAR}    \\
@MUTARR_PTRS@         & 1   &     & 1  &     &     & 1   & 1   &    &     & \ref{sect:MUTARR_PTRS} \\
@MUTARR_PTRS_FROZEN@  & 1   &     & 1  &     &     & 1   & 1   &    &     & \ref{sect:MUTARR_PTRS_FROZEN} \\

@FOREIGN@             & 1   &     & 1  &     &     &     & 1   &    &     & \ref{sect:FOREIGN}   \\

@BH@                  & ?   & 0/1 & 1  &     & ?   & ?   &     & 1  & ?   & \ref{sect:BH}        \\
@MVAR@                &     &     &    &     &     &     &     &    &     & \ref{sect:MVAR}      \\
@IVAR@                &     &     &    &     &     &     &     &    &     & \ref{sect:IVAR}      \\
@FETCHME@             &     &     &    &     &     &     &     &    &     & \ref{sect:FETCHME}   \\
\hline
\end{tabular}

Activation frames do not live (directly) on the heap --- but they have
a similar organisation.  The classification bits are all zero in
activation frames.

\begin{tabular}{|l|l|}\hline
closure kind            & Section                       \\ \hline
@RET_SMALL@             & \ref{sect:activation-records} \\
@RET_VEC_SMALL@         & \ref{sect:activation-records} \\
@RET_BIG@               & \ref{sect:activation-records} \\
@RET_VEC_BIG@           & \ref{sect:activation-records} \\
@UPDATE_FRAME@          & \ref{sect:activation-records} \\
\hline
\end{tabular}

There are also a number of administrative objects.  The classification bits are
all zero in administrative objects.

\begin{tabular}{|l|l|}\hline
closure kind            & Section                       \\ \hline
@TSO@                   & \ref{sect:TSO}                \\
@STACK_OBJECT@          & \ref{sect:STACK_OBJECT}       \\
@STABLEPTR_TABLE@       & \ref{sect:STABLEPTR_TABLE}    \\
@SPARK_OBJECT@          & \ref{sect:SPARK}              \\
@BLOCKED_FETCH@         & \ref{sect:BLOCKED_FETCH}      \\
\hline
\end{tabular}

\ToDo{I guess the parallel system has something like a stable ptr
table.  Is there any opportunity for sharing code/data structures
here?}


\subsection{Classification bits}

The top bits of the @INFO_TYPE@ tag tells what sort of animal the
closure is.

\begin{tabular}{|l|l|l|}                                                        \hline
Abbrev & Bit & Interpretation                                                   \\ \hline
HNF    & 0   & 1 $\Rightarrow$ Head normal form                                 \\
UPD    & 4   & 1 $\Rightarrow$ May be updated (inconsistent with being a HNF)   \\ 
NS     & 1   & 1 $\Rightarrow$ Don't spark me  (Any HNF will have this set to 1)\\
STA    & 2   & 1 $\Rightarrow$ This is a static closure                         \\
THU    & 8   & 1 $\Rightarrow$ Is a thunk                                       \\
MUT    & 3   & 1 $\Rightarrow$ Has mutable pointer fields                       \\ 
UPT    & 5   & 1 $\Rightarrow$ Has an unpointed type (eg a primitive array)     \\
BH     & 6   & 1 $\Rightarrow$ Is a black hole                                  \\
IND    & 7   & 1 $\Rightarrow$ Is an indirection                                \\
\hline
\end{tabular}

Updatable structures (@_UP@) are thunks that may be shared.  Primitive
arrays (@_BM@ -- Big Mothers) are structures that are always held
in-memory (basically extensions of a closure).  Because there may be
offsets into these arrays, a primitive array cannot be handled as a
FetchMe in the parallel system, but must be shipped in its entirety if
its parent closure is shipped.

The other bits in the info-type field simply give a unique bit-pattern
to identify the closure type.

\iffalse
@
#define _NF                     0x0001  /* Normal form  */
#define _NS                     0x0002  /* Don't spark  */
#define _ST                     0x0004  /* Is static    */
#define _MU                     0x0008  /* Is mutable   */
#define _UP                     0x0010  /* Is updatable (but not mutable) */
#define _BM                     0x0020  /* Is a "primitive" array */
#define _BH                     0x0040  /* Is a black hole */
#define _IN                     0x0080  /* Is an indirection */
#define _TH                     0x0100  /* Is a thunk */



SPEC    
SPEC_N          SPEC | _NF | _NS
SPEC_S          SPEC | _TH
SPEC_U          SPEC | _UP | _TH
                
GEN     
GEN_N           GEN | _NF | _NS
GEN_S           GEN | _TH
GEN_U           GEN | _UP | _TH
                
DYN             _NF | _NS
TUPLE           _NF | _NS | _BM
DATA            _NF | _NS | _BM
MUTUPLE         _NF | _NS | _MU | _BM
IMMUTUPLE       _NF | _NS | _BM
STATIC          _NS | _ST
CONST           _NF | _NS
CHARLIKE        _NF | _NS
INTLIKE         _NF | _NS

BH              _NS | _BH
BH_N            BH
BH_U            BH | _UP
                
BQ              _NS | _MU | _BH
IND             _NS | _IN
CAF             _NF | _NS | _ST | _IN

FM              
FETCHME         FM | _MU
FMBQ            FM | _MU | _BH

TSO             _MU

STKO    
STKO_DYNAMIC    STKO | _MU
STKO_STATIC     STKO | _ST
                
SPEC_RBH        _NS | _MU | _BH
GEN_RBH         _NS | _MU | _BH
BF              _NS | _MU | _BH
INTERNAL        

@
\fi

Notes:

An indirection either points to HNF (post update); or is result of
overwriting a FetchMe, in which case the thing fetched is either
under evaluation (BH), or by now an HNF.  Thus, indirections get NoSpark flag.



\subsection{Pointed Objects}

All pointed objects can be entered.

\subsubsection{Function closures}\label{sect:FUN}

Function closures represent lambda abstractions.  For example,
consider the top-level declaration:
@
  f = \x -> let g = \y -> x+y
            in g x
@
Both @f@ and @g@ are represented by function closures.  The closure
for @f@ is {\em static} while that for @g@ is {\em dynamic}.

The layout of a function closure is as follows:
\begin{center}
\begin{tabular}{|l|l|l|l|}\hline
{\em Fixed header}  & {\em Pointers} & {\em Non-pointers} \\ \hline
\end{tabular}
\end{center}
The data words (pointers and non-pointers) are the free variables of
the function closure.  
The number of pointers
and number of non-pointers are stored in the @INFO_SM@ word, in the least significant
and most significant half-word respectively.

There are several different sorts of function closure, distinguished
by their @INFO_TYPE@ field:
\begin{itemize}
\item @FUN@: a vanilla, dynamically allocated on the heap. 

\item $@FUN_@p@_@np$: to speed up garbage collection a number of
specialised forms of @FUN@ are provided, for particular $(p,np)$ pairs,
where $p$ is the number of pointers and $np$ the number of non-pointers.

\item @FUN_STATIC@.  Top-level, static, function closures (such as
@f@ above) have a different
layout than dynamic ones:
\begin{center}
\begin{tabular}{|l|l|l|}\hline
{\em Fixed header}  & {\em Static object link} \\ \hline
\end{tabular}
\end{center}
Static function closurs have no free variables.  (However they may refer to other 
static closures; these references are recorded in the function closure's SRT.)
They have one field that is not present in dynamic closures, the {\em static object
link} field.  This is used by the garbage collector in the same way that to-space
is, to gather closures that have been determined to be live but that have not yet
been scavenged.
\note{Static function closures that have no static references, and hence
a null SRT pointer, don't need the static object link field.  Is it worth
taking advantage of this?  See @CONSTR_STATIC_NOCAF@.}
\end{itemize}

Each lambda abstraction, $f$, in the STG program has its own private info table.
The following labels are relevant:
\begin{itemize}
\item $f$@_info@  is $f$'s info table.
\item $f$@_entry@ is $f$'s slow entry point (i.e. the entry code of its
info table; so it will label the same byte as $f$@_info@).
\item $f@_fast_@k$ is $f$'s fast entry point.  $k$ is the number of arguments
$f$ takes; encoding this number in the fast-entry label occasionally catches some nasty
code-generation errors.
\end{itemize}

\subsubsection{Data Constructors}\label{sect:CONSTR}

Data-constructor closures represent values constructed with
algebraic data type constructors.
The general layout of data constructors is the same as that for function
closures.  That is
\begin{center}
\begin{tabular}{|l|l|l|l|}\hline
{\em Fixed header}  & {\em Pointers} & {\em Non-pointers} \\ \hline
\end{tabular}
\end{center}

The SRT pointer in a data constructor's info table is never used --- the
code for a constructor does not make any static references.
\note{Use it for something else??  E.g. tag?}

There are several different sorts of constructor:
\begin{itemize}
\item @CONSTR@: a vanilla, dynamically allocated constructor.
\item @CONSTR_@$p$@_@$np$: just like $@FUN_@p@_@np$.
\item @CONSTR_INTLIKE@.
A dynamically-allocated heap object that looks just like an @Int@.  The 
garbage collector checks to see if it can common it up with one of a fixed
set of static int-like closures, thus getting it out of the dynamic heap
altogether.

\item @CONSTR_CHARLIKE@:  same deal, but for @Char@.

\item @CONSTR_STATIC@ is similar to @FUN_STATIC@, with the complication that
the layout of the constructor must mimic that of a dynamic constructor,
because a static constructor might be returned to some code that unpacks it.
So its layout is like this:
\begin{center}
\begin{tabular}{|l|l|l|l|l|}\hline
{\em Fixed header}  & {\em Pointers} & {\em Non-pointers} & {\em Static object link}\\ \hline
\end{tabular}
\end{center}
The static object link, at the end of the closure, serves the same purpose
as that for @FUN_STATIC@.  The pointers in the static constructor can point
only to other static closures.

The static object link occurs last in the closure so that static
constructors can store their data fields in exactly the same place as
dynamic constructors.

\item @CONSTR_STATIC_NOCAF@.  A statically allocated data constructor
that guarantees not to point (directly or indirectly) to any CAF
(section~\ref{sect:CAF}).  This means it does not need a static object
link field.  Since we expect that there might be quite a lot of static
constructors this optimisation makes sense.  Furthermore, the @NOCAF@
tag allows the compiler to indicate that no CAFs can be reached
anywhere {\em even indirectly}.


\end{itemize}

For each data constructor $Con$, two
info tables are generated:
\begin{itemize}
\item $Con$@_info@ labels $Con$'s dynamic info table, 
shared by all dynamic instances of the constructor.
\item $Con$@_static@ labels $Con$'s static info table, 
shared by all static instances of the constructor.
\end{itemize}

\subsubsection{Thunks}
\label{sect:THUNK}
\label{sect:THUNK_SEL}

A thunk represents an expression that is not obviously in head normal 
form.  For example, consider the following top-level definitions:
@
  range = between 1 10
  f = \x -> let ys = take x range
            in sum ys
@
Here the right-hand sides of @range@ and @ys@ are both thunks; the former
is static while the latter is dynamic.

The layout of a thunk is the same as that for a function closure,
except that it may have some words of ``slop'' at the end to make sure
that it has 
at least @MIN_UPD_PAYLOAD@ words in addition to its
fixed header.
\begin{center}
\begin{tabular}{|l|l|l|l|l|}\hline
{\em Fixed header}  & {\em Pointers} & {\em Non-pointers} & {\em Slop} \\ \hline
\end{tabular}
\end{center}
The @INFO_SM@ word contains the same information as for function
closures; that is, number of pointers and number of non-pointers (excluding slop).

A thunk differs from a function closure in that it can be updated.

There are several forms of thunk:
\begin{itemize}
\item @THUNK@: a vanilla, dynamically allocated thunk.
The garbage collection code for thunks whose
pointer + non-pointer words is less than @MIN_UPD_PAYLOAD@ differs from
that for function closures and data constructors, because the GC code
has to account for the slop.
\item $@THUNK_@p@_@np$.  Similar comments apply.
\item @THUNK_STATIC@.  A static thunk is also known as 
a {\em constant applicative form}, or {\em CAF}.

\begin{center}
\begin{tabular}{|l|l|l|l|l|}\hline
{\em Fixed header}  & {\em Pointers} & {\em Non-pointers} & {\em Slop} & {\em Static object link}\\ \hline
\end{tabular}
\end{center}

\item @THUNK_SELECTOR@ is a (dynamically allocated) thunk
whose entry code performs a simple selection operation from
a data constructor drawn from a single-constructor type.  For example,
the thunk
@
        x = case y of (a,b) -> a
@
is a selector thunk.  A selector thunk is laid out like this:
\begin{center}
\begin{tabular}{|l|l|l|l|}\hline
{\em Fixed header}  & {\em Selectee pointer} \\ \hline
\end{tabular}
\end{center}
The @INFO_SM@ word contains the byte offset of the desired word in
the selectee.  Note that this is different from all other thunks.

The garbage collector ``peeks'' at the selectee's
tag (in its info table).  If it is evaluated, then it goes ahead and do
the selection, and then behaves just as if the selector thunk was an
indirection to the selected field.
If it is not
evaluated, it treats the selector thunk like any other thunk of that
shape.  [Implementation notes.  
Copying: only the evacuate routine needs to be special.
Compacting: only the PRStart (marking) routine needs to be special.]
\end{itemize}


The only label associated with a thunk is its info table:
\begin{description}
\item[$f$@_info@] is $f$'s info table.
\end{description}


\subsubsection{Partial applications (PAPs)}\label{sect:PAP}

A partial application (PAP) represents a function applied to too few arguments.
It is only built as a result of updating after an argument-satisfaction
check failure.  A PAP has the following shape:
\begin{center}
\begin{tabular}{|l|l|l|l|}\hline
{\em Fixed header}  & {\em No of arg words} & {\em Function closure} & {\em Arg stack} \\ \hline
\end{tabular}
\end{center}
The ``arg stack'' is a copy of of the chunk of stack above the update
frame; ``no of arg words'' tells how many words it consists of.  The
function closure is (a pointer to) the closure for the function whose
argument-satisfaction check failed.

There is just one standard form of PAP with @INFO_TYPE@ = @PAP@.
There is just one info table too, called @PAP_info@.
Its entry code simply copies the arg stack chunk back on top of the
stack and enters the function closure.  (It has to do a stack overflow test first.)

There are no static PAPs.

\subsubsection{Indirections}
\label{sect:IND}
\label{sect:IND_OLDGEN}

Indirection closures just point to other closures. They are introduced
when a thunk is updated to point to its value. 
The entry code for all indirections simply enters the closure it points to.

There are several forms of indirection:
\begin{description}
\item[@IND@] is the vanilla, dynamically-allocated indirection.
It is removed by the garbage collector. It has the following
shape:
\begin{center}
\begin{tabular}{|l|l|l|}\hline
{\em Fixed header} & {\em Target closure} \\ \hline
\end{tabular}
\end{center}

\item[@IND_OLDGEN@] is the indirection used to update an old-generation
thunk. Its shape is like this:
\begin{center}
\begin{tabular}{|l|l|l|}\hline
{\em Fixed header} & {\em Mutable link field} & {\em Target closure} \\ \hline
\end{tabular}
\end{center}
It contains a {\em mutable link field} that is used to string together
all old-generation indirections that might have a pointer into
the new generation.


\item[@IND_PERMANENT@ and @IND_OLDGEN_PERMANENT@.]
for lexical profiling, it is necessary to maintain cost centre
information in an indirection, so ``permanent indirections'' are
retained forever.  Otherwise they are just like vanilla indirections.
\note{If a permanent indirection points to another permanent
indirection or a @CONST@ closure, it is possible to elide the indirection
since it will have no effect on the profiler.}
\note{Do we still need @IND@ and @IND_OLDGEN@
in the profiling build, or can we just make
do with one pair whose behaviour changes when profiling is built?}

\item[@IND_STATIC@] is used for overwriting CAFs when they have been
evaluated.  Static indirections are not removed by the garbage
collector; and are statically allocated outside the heap (and should
stay there).  Their static object link field is used just as for
@FUN_STATIC@ closures.

\begin{center}
\begin{tabular}{|l|l|l|}
\hline
{\em Fixed header} & {\em Target closure} & {\em Static object link} \\
\hline
\end{tabular}
\end{center}

\end{description}

\subsubsection{Activation Records}

Activation records are parts of the stack described by return address
info tables (closures with @INFO_TYPE@ values of @RET_SMALL@,
@RET_VEC_SMALL@, @RET_BIG@ and @RET_VEC_BIG@). They are described in
section~\ref{sect:activation-records}.


\subsubsection{Black holes, MVars and IVars}
\label{sect:BH}
\label{sect:MVAR}
\label{sect:IVAR}

Black hole closures are used to overwrite closures currently being
evaluated. They inform the garbage collector that there are no live
roots in the closure, thus removing a potential space leak.  

Black holes also become synchronization points in the threaded world.
They contain a pointer to a list of blocked threads to be awakened
when the black hole is updated (or @NULL@ if the list is empty).
\begin{center}
\begin{tabular}{|l|l|l|}
\hline 
{\em Fixed header} & {\em Mutable link} & {\em Blocked thread link} \\
\hline
\end{tabular}
\end{center}
The {\em Blocked thread link} points to the TSO of the first thread
waiting for the value of this thunk.  All subsequent TSOs in the list
are linked together using their @TSO_LINK@ field.

When the blocking queue is non-@NULL@, the black hole must be added to
the mutables list since the TSOs on the list may contain pointers into
the new generation.  There is no need to clutter up the mutables list
with black holes with empty blocking queues.

\ToDo{MVars}


\subsubsection{FetchMes}\label{sect:FETCHME}

In the parallel systems, FetchMes are used to represent pointers into
the global heap.  When evaluated, the value they point to is read from
the global heap.

\ToDo{Describe layout}


\subsection{Unpointed Objects}

A variable of unpointed type is always bound to a {\em value}, never to a {\em thunk}.
For this reason, unpointed objects cannot be entered.

A {\em value} may be:
\begin{itemize}
\item {\em Boxed}, i.e.~represented indirectly by a pointer to a heap object (e.g.~foreign objects, arrays); or
\item {\em Unboxed}, i.e.~represented directly by a bit-pattern in one or more registers (e.g.~@Int#@ and @Float#@).
\end{itemize}
All {\em pointed} values are {\em boxed}.  

\subsubsection{Immutable Objects}
\label{sect:ARR_WORDS1}
\label{sect:ARR_PTRS}

\begin{description}
\item[@ARR_WORDS@] is a variable-sized object consisting solely of
non-pointers.  It is used for arrays of all
sorts of things (bytes, words, floats, doubles... it doesn't matter).
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
{\em Fixed Hdr} & {\em No of non-pointers} & {\em Non-pointers\ldots}   \\ \hline
\end{tabular}
\end{center}

\item[@ARR_PTRS@] is an immutable, variable sized array of pointers.
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
{\em Fixed Hdr} & {\em Mutable link} & {\em No of pointers} & {\em Pointers\ldots}      \\ \hline
\end{tabular}
\end{center}
The mutable link is present so that we can easily freeze and thaw an
array (by changing the header and adding/removing the array to the
mutables list).

\end{description}

\subsubsection{Mutable Objects}
\label{sect:mutables}
\label{sect:ARR_WORDS2}
\label{sect:MUTVAR}
\label{sect:MUTARR_PTRS}
\label{sect:MUTARR_PTRS_FROZEN}

Some of these objects are {\em mutable}; they represent objects which
are explicitly mutated by Haskell code through the @ST@ monad.
They're not used for thunks which are updated precisely once.
Depending on the garbage collector, mutable closures may contain extra
header information which allows a generational collector to implement
the ``write barrier.''

\begin{description}

\item[@ARR_WORDS@] is also used to represent {\em mutable} arrays of
bytes, words, floats, doubles, etc.  It's possible to use the same
object type because even generational collectors don't need to
distinguish them.

\item[@MUTVAR@] is a mutable variable.
\begin{center}
\begin{tabular}{|c|c|c|}
\hline
{\em Fixed Hdr} & {\em Mutable link} & {\em Pointer} \\ \hline
\end{tabular}
\end{center}

\item[@MUTARR_PTRS@] is a mutable array of pointers.
Such an array may be {\em frozen}, becoming an @SM_MUTARR_PTRS_FROZEN@, with a
different info-table.
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
{\em Fixed Hdr} & {\em Mutable link} & {\em No of ptrs} & {\em Pointers\ldots} \\ \hline
\end{tabular}
\end{center}

\item[@MUTARR_PTRS_FROZEN@] is a frozen @MUTARR_PTRS@ closure.
The garbage collector converts @MUTARR_PTRS_FROZEN@ to @ARR_PTRS@ as it removes them from
the mutables list.

\end{description}


\subsubsection{Foreign Objects}\label{sect:FOREIGN}

Here's what a ForeignObj looks like:

\begin{center}
\begin{tabular}{|l|l|l|l|}
\hline 
{\em Fixed header} & {\em Data} & {\em Free Routine} & {\em Foreign object link} \\
\hline
\end{tabular}
\end{center}

The @FreeRoutine@ is a reference to the finalisation routine to call
when the @ForeignObj@ becomes garbage.  If @freeForeignObject@ is
called on a Foreign Object, the @FreeRoutine@ is set to zero and the
garbage collector will not attempt to call @FreeRoutine@ when the 
object becomes garbage.

The Foreign object link is a link to the next foreign object in the
list.  This list is traversed at the end of garbage collection: if an
object is about to be deallocated (e.g.~it was not marked or
evacuated), the free routine is called and the object is deleted from
the list.  


The remaining objects types are all administrative --- none of them may be entered.

\subsection{Thread State Objects (TSOs)}\label{sect:TSO}

In the multi-threaded system, the state of a suspended thread is
packed up into a Thread State Object (TSO) which contains all the
information needed to restart the thread and for the garbage collector
to find all reachable objects.  When a thread is running, it may be
``unpacked'' into machine registers and various other memory locations
to provide faster access.

Single-threaded systems don't really {\em need\/} TSOs --- but they do
need some way to tell the storage manager about live roots so it is
convenient to use a single TSO to store the mutator state even in
single-threaded systems.

Rather than manage TSOs' alloc/dealloc, etc., in some {\em ad hoc}
way, we instead alloc/dealloc/etc them in the heap; then we can use
all the standard garbage-collection/fetching/flushing/etc machinery on
them.  So that's why TSOs are ``heap objects,'' albeit very special
ones.
\begin{center}
\begin{tabular}{|l|l|}
   \hline {\em Fixed header}
\\ \hline @TSO_LINK@
\\ \hline @TSO_WHATNEXT@
\\ \hline @TSO_WHATNEXT_INFO@ 
\\ \hline @TSO_STACK@ 
\\ \hline {\em Exception Handlers}
\\ \hline {\em Ticky Info}
\\ \hline {\em Profiling Info}
\\ \hline {\em Parallel Info}
\\ \hline {\em GranSim Info}
\\ \hline
\end{tabular}
\end{center}
The contents of a TSO are:
\begin{itemize}

\item A pointer (@TSO_LINK@) used to maintain a list of threads with a similar
  state (e.g.~all runable, all sleeping, all blocked on the same black
  hole, all blocked on the same MVar, etc.)

\item A word (@TSO_WHATNEXT@) which is in suspended threads to record
 how to awaken it.  This typically requires a program counter which is stored
 in the pointer @TSO_WHATNEXT_INFO@

\item A pointer (@TSO_STACK@) to the top stack block.

\item Optional information for ``Ticky Ticky'' statistics: @TSO_STK_HWM@ is
  the maximum number of words allocated to this thread.

\item Optional information for profiling: 
  @TSO_CCC@ is the current cost centre.

\item Optional information for parallel execution:
\begin{itemize}

\item The types of threads (@TSO_TYPE@):
\begin{description}
\item[@T_MAIN@]     Must be executed locally.
\item[@T_REQUIRED@] A required thread  -- may be exported.
\item[@T_ADVISORY@] An advisory thread -- may be exported.
\item[@T_FAIL@]     A failure thread   -- may be exported.
\end{description}

\item I've no idea what else

\end{itemize}

\item Optional information for GranSim execution:
\begin{itemize}
\item locked         
\item sparkname  
\item started at         
\item exported   
\item basic blocks       
\item allocs     
\item exectime   
\item fetchtime  
\item fetchcount         
\item blocktime  
\item blockcount         
\item global sparks      
\item local sparks       
\item queue              
\item priority   
\item clock          (gransim light only)
\end{itemize}


Here are the various queues for GrAnSim-type events.
@
Q_RUNNING   
Q_RUNNABLE  
Q_BLOCKED   
Q_FETCHING  
Q_MIGRATING 
@

\end{itemize}

\subsection{Other weird objects}
\label{sect:SPARK}
\label{sect:BLOCKED_FETCH}

\begin{description}
\item[@BlockedFetch@ heap objects (`closures')] (parallel only)

@BlockedFetch@s are inbound fetch messages blocked on local closures.
They arise as entries in a local blocking queue when a fetch has been
received for a local black hole.  When awakened, we look at their
contents to figure out where to send a resume.

A @BlockedFetch@ closure has the form:
\begin{center}
\begin{tabular}{|l|l|l|l|l|l|}\hline
{\em Fixed header} & link & node & gtid & slot & weight \\ \hline
\end{tabular}
\end{center}

\item[Spark Closures] (parallel only)

Spark closures are used to link together all closures in the spark pool.  When
the current processor is idle, it may choose to speculatively evaluate some of
the closures in the pool.  It may also choose to delete sparks from the pool.
\begin{center}
\begin{tabular}{|l|l|l|l|l|l|}\hline
{\em Fixed header} & {\em Spark pool link} & {\em Sparked closure} \\ \hline
\end{tabular}
\end{center}


\end{description}


\subsection{Stack Objects}
\label{sect:STACK_OBJECT}
\label{sect:stacks}

These are ``stack objects,'' which are used in the threaded world as
the stack for each thread is allocated from the heap in smallish
chunks.  (The stack in the sequential world is allocated outside of
the heap.)

Each reduction thread has to have its own stack space.  As there may
be many such threads, and as any given one may need quite a big stack,
a naive give-'em-a-big-stack-and-let-'em-run approach will cost a {\em
lot} of memory.

Our approach is to give a thread a small stack space, and then link
on/off extra ``chunks'' as the need arises.  Again, this is a
storage-management problem, and, yet again, we choose to graft the
whole business onto the existing heap-management machinery.  So stack
objects will live in the heap, be garbage collected, etc., etc..

A stack object is laid out like this:

\begin{center}
\begin{tabular}{|l|}
\hline
{\em Fixed header} 
\\ \hline
{\em Link to next stack object (0 for last)}
\\ \hline
{\em N, the payload size in words}
\\ \hline
{\em @Sp@ (byte offset from head of object)}
\\ \hline
{\em @Su@ (byte offset from head of object)}
\\ \hline
{\em Payload (N words)}
\\ \hline
\end{tabular}
\end{center}

\ToDo{Are stack objects on the mutable list?}

The stack grows downwards, towards decreasing
addresses.  This makes it easier to print out the stack
when debugging, and it means that a return address is
at the lowest address of the chunk of stack it ``knows about''
just like an info pointer on a closure.

The garbage collector needs to be able to find all the
pointers in a stack.  How does it do this?

\begin{itemize}

\item Within the stack there are return addresses, pushed
by @case@ expressions.  Below a return address (i.e. at higher
memory addresses, since the stack grows downwards) is a chunk
of stack that the return address ``knows about'', namely the
activation record of the currently running function.

\item Below each such activation record is a {\em pending-argument
section}, a chunk of
zero or more words that are the arguments to which the result
of the function should be applied.  The return address does not
statically
``know'' how many pending arguments there are, or their types.
(For example, the function might return a result of type $\alpha$.)

\item Below each pending-argument section is another return address,
and so on.  Actually, there might be an update frame instead, but we
can consider update frames as a special case of a return address with
a well-defined activation record.

\ToDo{Doesn't it {\em have} to be an update frame?  After all, the arg
satisfaction check is @Su - Sp >= ...@.}

\end{itemize}

The game plan is this.  The garbage collector
walks the stack from the top, traversing pending-argument sections and
activation records alternately.  Next we discuss how it finds
the pointers in each of these two stack regions.


\subsubsection{Activation records}\label{sect:activation-records}

An {\em activation record} is a contiguous chunk of stack,
with a return address as its first word, followed by as many
data words as the return address ``knows about''.  The return
address is actually a fully-fledged info pointer.  It points
to an info table, replete with:

\begin{itemize}
\item entry code (i.e. the code to return to).
\item @INFO_TYPE@ is either @RET_SMALL/RET_VEC_SMALL@ or @RET_BIG/RET_VEC_BIG@, depending
on whether the activation record has more than 32 data words (\note{64 for 8-byte-word architectures}) and on whether 
to use a direct or a vectored return.
\item @INFO_SM@ for @RET_SMALL@ is a bitmap telling the layout
of the activation record, one bit per word.  The least-significant bit
describes the first data word of the record (adjacent to the fixed
header) and so on.  A ``@1@'' indicates a non-pointer, a ``@0@''
indicates
a pointer.  We don't need to indicate exactly how many words there
are,
because when we get to all zeros we can treat the rest of the 
activation record as part of the next pending-argument region.

For @RET_BIG@ the @INFO_SM@ field points to a block of bitmap
words, starting with a word that tells how many words are in
the block.

\item @INFO_SRT@ is the Static Reference Table for the return
address (Section~\ref{sect:srt}).
\end{itemize}

The activation record is a fully fledged closure too.
As well as an info pointer, it has all the other attributes of
a fixed header (Section~\ref{sect:fixed-header}) including a saved cost
centre which is reloaded when the return address is entered.

In other words, all the attributes of closures are needed for
activation records, so it's very convenient to make them look alike.


\subsubsection{Pending arguments}

So that the garbage collector can correctly identify pointers
in pending-argument sections we explicitly tag all non-pointers.
Every non-pointer in a pending-argument section is preceded
(at the next lower memory word) by a one-word byte count that
says how many bytes to skip over (excluding the tag word).

The garbage collector traverses a pending argument section from 
the top (i.e. lowest memory address).  It looks at each word in turn:

\begin{itemize}
\item If it is less than or equal to a small constant @MAX_STACK_TAG@
then
it treats it as a tag heralding zero or more words of non-pointers,
so it just skips over them.

\item If it points to the code segment, it must be a return
address, so we have come to the end of the pending-argument section.

\item Otherwise it must be a bona fide heap pointer.
\end{itemize}


\subsection{The Stable Pointer Table}\label{sect:STABLEPTR_TABLE}

A stable pointer is a name for a Haskell object which can be passed to
the external world.  It is ``stable'' in the sense that the name does
not change when the Haskell garbage collector runs---in contrast to
the address of the object which may well change.

A stable pointer is represented by an index into the
@StablePointerTable@.  The Haskell garbage collector treats the
@StablePointerTable@ as a source of roots for GC.

In order to provide efficient access to stable pointers and to be able
to cope with any number of stable pointers (eg $0 \ldots 100000$), the
table of stable pointers is an array stored on the heap and can grow
when it overflows.  (Since we cannot compact the table by moving
stable pointers about, it seems unlikely that a half-empty table can
be reduced in size---this could be fixed if necessary by using a
hash table of some sort.)

In general a stable pointer table closure looks like this:

\begin{center}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|}
\hline
{\em Fixed header} & {\em No of pointers} & {\em Free} & $SP_0$ & \ldots & $SP_{n-1}$ 
\\\hline
\end{tabular}
\end{center}

The fields are:
\begin{description}

\item[@NPtrs@:] number of (stable) pointers.

\item[@Free@:] the byte offset (from the first byte of the object) of the first free stable pointer.

\item[$SP_i$:] A stable pointer slot.  If this entry is in use, it is
an ``unstable'' pointer to a closure.  If this entry is not in use, it
is a byte offset of the next free stable pointer slot.

\end{description}

When a stable pointer table is evacuated
\begin{enumerate}
\item the free list entries are all set to @NULL@ so that the evacuation
  code knows they're not pointers;

\item The stable pointer slots are scanned linearly: non-@NULL@ slots
are evacuated and @NULL@-values are chained together to form a new free list.
\end{enumerate}

There's no need to link the stable pointer table onto the mutable
list because we always treat it as a root.



\section{The Storage Manager}

The generational collector remembers the depth of the last generation
collected and the value of the heap pointer at the end of the last GC.
If the mutator has not moved the heap pointer, that means that the
amount of space recovered is insufficient to satisfy even one request
and it is time to collect an older generation or report a heap overflow.

A deeper collection is also triggered when a minor collection fails to
recover at least @...@ bytes of space.

When can a GC happen?

@
- During updates (ie during returns)
- When a heap check fails
- When a stack check fails (concurrent system only)
- When a context switch happens (concurrent system only)

When do heap checks occur?
- Immediately after entering a thunk
- Immediately after entering a case alternative

When do stack checks occur?
- We calculate the worst-case stack usage of an entire
  thunk so there's no need to put a check inside each alternative.
- Immediately after entering a thunk
  We can't make a similar worst-case calculation for heap usage
  because the heap isn't used in a stacklike manner so any
  evaluation inside a case might steal some of the heap we've
  checked for.

Concurrency
- Threads can be blocked
- Threads can be put to sleep
  - Heap may move while we sleep
  - Black holing may happen while we sleep (ie during GC)
@

\subsection{The SM state}

Contains @Hp@, @HpLim@, @StablePtrTable@ plus version-specific info.

\begin{itemize}

\item Static Object list 
\item Foreign Object list
\item Stable Pointer Table

\end{itemize}

In addition, the generational collector requires:

\begin{itemize}

\item Old Generation Indirection list
\item Mutables list --- list of mutable objects in the old generation.
\item @OldLim@ --- the boundary between the generations
\item Old Foreign Object list --- foreign objects in the old generation

\end{itemize}

It is passed a table of {\em roots\/} containing

\begin{itemize}

\item All runnable TSOs

\end{itemize}


In the parallel system, there must be some extra magic associated with
global GC.

\subsection{The SM interface}

@initSM@ finalizes any runtime parameters of the storage manager.

@exitSM@ does any cleaning up required by the storage manager before
the program is executed. Its main purpose is to print any summary
statistics.

@initHeap@ allocates the heap. It initialises the @hp@ and @hplim@
fields of @sm@ to represent an empty heap for the compiled-in garbage
collector.  It also initialises @CAFlist@ to be the empty list. If we
are using Appel's collector it also initialises the @OldLim@ field.
It also initialises the stable pointer table and the @ForeignObjList@
(and @OldForeignObjList@) fields.

@collectHeap@ invokes the garbage collector.  @collectHeap@ requires
all the fields of @sm@ to be initialised appropriately (from the
STG-machine registers).  The following are identified as heap roots:
\begin{itemize}
\item The updated CAFs recorded in @CAFlist@.
\item Any pointers found on the stack.
\item All runnable and sleeping TSOs.
\item The stable pointer table.
\end{itemize}

There are two possible results from a garbage collection:
\begin{description} 
\item[@GC_FAIL@] 
The garbage collector is unable to free up any more space.

\item[@GC_SUCCESS@]
The garbage collector managed to free up more space.

\begin{itemize} 
\item @hp@ and @hplim@ will indicate the new space available for
allocation.

\item The elements of @CAFlist@ and the stable pointers will be
updated to point to the new locations of the closures they reference.

\item Any members of @ForeignObjList@ which became garbage should have
been reported (by calling their finalising routines; and the
@(Old)ForeignObjList@ updated to contain only those Foreign objects
which are still live.  

\end{itemize}

\end{description}


%************************************************************************
%*                                                                      *
\subsection{``What really happens in a garbage collection?''}
%*                                                                      *
%************************************************************************

This is a brief tutorial on ``what really happens'' going to/from the
storage manager in a garbage collection.

\begin{description}
%------------------------------------------------------------------------
\item[The heap check:]

[OLD-ISH: WDP]

If you gaze into the C output of GHC, you see many macros calls like:
\begin{verbatim}
HEAP_CHK_2PtrsLive((_FHS+2));
\end{verbatim}

This expands into the C (roughly speaking...):
@
Hp = Hp + (_FHS+2);     /* optimistically move heap pointer forward */

GC_WHILE_OR_IF (HEAP_OVERFLOW_OP(Hp, HpLim) OR_INTERVAL_EXPIRED) {
        STGCALL2_GC(PerformGC, <liveness-bits>, (_FHS+2));
}
@

In the parallel world, where we will need to re-try the heap check,
@GC_WHILE_OR_IF@ will be a ``while''; in the sequential world, it will
be an ``if''.

The ``heap lookahead'' checks, which are similar and used for
multi-precision @Integer@ ops, have some further complications.  See
the commentary there (@StgMacros.lh@).

%------------------------------------------------------------------------
\item[Into @callWrapper_GC@...:]

When we failed the heap check (above), we were inside the
GCC-registerised ``threaded world.''  @callWrapper_GC@ is all about
getting in and out of the threaded world.  On SPARCs, with register
windows, the name of the game is not shifting windows until we have
what we want out of the old one.  In tricky cases like this, it's best
written in assembly language.

Performing a GC (potentially) means giving up the thread of control.
So we must fill in the thread-state-object (TSO) [and its associated
stk object] with enough information for later resumption:
\begin{enumerate}
\item
Save the return address in the TSO's PC field.
\item
Save the machine registers used in the STG threaded world in their
corresponding TSO fields.  We also save the pointer-liveness
information in the TSO.
\item
The registers that are not thread-specific, notably @Hp@ and
@HpLim@, are saved in the @StorageMgrInfo@ structure.
\item
Call the routine it was asked to call; in this example, call
@PerformGC@ with arguments @<liveness>@ and @_FHS+2@ (some constant)...

\end{enumerate}

%------------------------------------------------------------------------
\item[Into the heap overflow wrapper, @PerformGC@ [parallel]:]

Most information has already been saved in the TSO.

\begin{enumerate}
\item
The first argument (@<liveness>@, in our example) say what registers
are live, i.e., are ``roots'' the storage manager needs to know.
\begin{verbatim}
StorageMgrInfo.rootno   = 2;
StorageMgrInfo.roots[0] = (P_) Ret1_SAVE;
StorageMgrInfo.roots[1] = (P_) Ret2_SAVE;
\end{verbatim}

\item
We move the heap-pointer back [we had optimistically
advanced it, in the initial heap check]

\item 
We load up the @smInfo@ data from the STG registers' @*_SAVE@ locations.

\item
We mark on the scheduler's big ``blackboard'' that a GC is
required.

\item
We reschedule, i.e., this thread gives up control.  (The scheduler
will presumably initiate a garbage-collection, but it may have to do
any number of other things---flushing, for example---before ``normal
execution'' resumes; and it most certainly may not be this thread that
resumes at that point!)
\end{enumerate}

IT IS AT THIS POINT THAT THE WORLD IS COMPLETELY TIDY.

%------------------------------------------------------------------------
\item[Out of @callWrapper_GC@ [parallel]:]

When this thread is finally resumed after GC (and who knows what
else), it will restart by the normal enter-TSO/enter-stack-object
sequence, which has the effect of re-loading the registers, etc.,
(i.e., restoring the state).

Because the address we saved in the TSO's PC field was that at the end
of the heap check, and because the check is a while-loop in the
parallel system, we will now loop back around, and make sure there is
enough space before continuing.
\end{description}



\subsection{Static Reference Tables (SRTs)}
\label{sect:srt}
\label{sect:CAF}
\label{sect:static-objects}

In the above, we assumed that objects always contained pointers to all
their free variables.  In fact, this isn't quite true: GHC omits
pointers to top-level objects and allocates their closures in static
memory.  This optimisation reduces the number of free variables in
heap objects - reducing memory usage and the effort needed to put them
into heap objects.  However, this optimisation comes at a cost: we
need to complicate the garbage collector with machinery for tracing
these static references.

Early versions of GHC used a very simple algorithm: it treated all
static objects as roots.  This is safe in the sense that no object is
ever deallocated if there's a chance that it might be required later
but can lead to some terrible space leaks.  For example, this program
ought to be able to run in constant space but, because @xs@ is never
deallocated, it runs in linear space.

@
main = print xs
xs = [1..]
@

The correct behaviour is for the garbage collector to keep a static
object alive iff it might be required later in execution.  That is, if
it is reachable from any live heap objects {\em or\/} from any return
addresses found on the stack or from the current program counter.
Since it is obviously infeasible for the garbage collector to scan
machine code looking for static references, the code generator must
generate a table of all static references in any piece of code (and we
must place a pointer to this table next to any piece of code we
generate).

Here's what the SRT has to contain:

@
...
@

Here's how we represent it:

@
...
must be able to handle 0 references well
@

@
Other trickery:
o The CAF list
o The scavenge list
o Generational GC trickery
@

\subsection{Space leaks and black holes}
\label{sect:black-hole}

\iffalse

\ToDo{Insert text stolen from update paper}

\else

A program exhibits a {\em space leak} if it retains storage that is
sure not to be used again.  Space leaks are becoming increasingly
common in imperative programs that @malloc@ storage and fail
subsequently to @free@ it.  They are, however, also common in
garbage-collected systems, especially where lazy evaluation is
used.[.wadler leak, runciman heap profiling jfp.]

Quite a bit of experience has now accumulated suggesting that
implementors must be very conscientious about avoiding gratuitous
space leaks --- that is, ones which are an accidental artefact of some
implementation technique.[.appel book.]  The update mechanism is
a case in point, as <.jones jfp leak.> points out.  Consider a thunk for
the expression
@
  let xs = [1..1000] in last xs
@ 
where @last@ is a function that returns the last element of its
argument list.  When the thunk is entered it will call @last@, which
will consume @xs@ until it finds the last element.  Since the list
@[1..1000]@ is produced lazily one might reasonably expect the
expression to evaluate in constant space.  But {\em until the moment
of update, the thunk itself still retains a pointer to the beginning
of the list @xs@}.  So, until the update takes place the whole list
will be retained!

Of course, this is completely gratuitous.  The pointer to @xs@ in the
thunk will never be used again.  In <.peyton stock hardware.> the solution to
this problem that we advocated was to overwrite a thunk's info with a
fixed ``black hole'' info pointer, {\em at the moment of entry}.  The
storage management information attached to a black-hole info pointer
tells the garbage collector that the closure contains no pointers,
thereby plugging the space leak.

\subsubsection{Lazy black-holing}

Black-holing is a well-known idea.  The trouble is that it is
gallingly expensive.  To avoid the occasional space leak, for every
single thunk entry we have to load a full-word literal constant into a
register (often two instructions) and then store that register into a
memory location.  

Fortunately, this cost can easily be avoided.  The
idea is simple: {\em instead of black-holing every thunk on entry,
wait until the garbage collector is called, and then black-hole all
(and only) the thunks whose evaluation is in progress at that moment}.
There is no benefit in black-holing a thunk that is updated before
garbage collection strikes!  In effect, the idea is to perform the
black-holing operation lazily, only when it is needed.  This
dramatically cuts down the number of black-holing operations, as our
results show {\em forward ref}.

How can we find all the thunks whose evaluation is in progress?  They
are precisely the ones for which update frames are on the stack.  So
all we need do is find all the update frames (via the @Su@ chain) and
black-hole their thunks right at the start of garbage collection.
Notice that it is not enough to refrain from treating update frames as
roots: firstly because the thunks to which they point may need to be
moved in a copying collector, but more importantly because the thunk
might be accessible via some other route.

\subsubsection{Detecting loops}

Black-holing has a second minor advantage: evaluation of a thunk whose
value depends on itself will cause a black hole closure to be entered,
which can cause a suitable error message to be displayed. For example,
consider the definition
@
  x = 1+x
@
The code to evaluate @x@'s right hand side will evaluate @x@.  In the
absence of black-holing, the result will be a stack overflow, as the
evaluator repeatedly pushes a return address and enters @x@.  If
thunks are black-holed on entry, then this infinite loop can be caught
almost instantly.

With our new method of lazy black-holing, a self-referential program
might cause either stack overflow or a black-hole error message,
depending on exactly when garbage collection strikes.  It is quite
easy to conceal these differences, however.  If stack overflow occurs,
all we need do is examine the update frames on the stack to see if
more than one refers to the same thunk.  If so, there is a loop that
would have been detected by eager black-holing.

\subsubsection{Lazy locking}
\label{sect:lock}

In a parallel implementation, it is necessary somehow to ``lock'' a
thunk that is under evaluation, so that other parallel evaluators
cannot simultaneously evaluate it and thereby duplicate work.
Instead, an evaluator that enters a locked thunk should be blocked,
and made runnable again when the thunk is updated.

This locking is readily arranged in the same way as black-holing, by
overwriting the thunk's info pointer with a special ``locked'' info
pointer, at the moment of entry.  If another evaluator enters the
thunk before it has been updated, it will land in the entry code for
the ``locked'' info pointer, which blocks the evaluator and queues it
on the locked thunk.

The details are given by <.portable parallel trinder.>.  However, the close similarity
between locking and black holing suggests the following question: can
locking be done lazily too?  The answer is that it can, except that
locking can be postponed only until the next {\em context switch},
rather than the next {\em garbage collection}.  We are assuming here
that the parallel implementation does not use shared memory to allow
two processors to access the same closure.  If such access is
permitted then every thunk entry requires a hardware lock, and becomes
much too expensive.

Is lazy locking worth while, given that it requires extra work every
context switch?  We believe it is, because contexts switches are
relatively infrequent, and thousands of thunk-entries typically take
place between each.

{\em Measurements elsewhere.  Omit this section? If so, fix cross refs to here.}

\fi


\subsection{Squeezing identical updates}

\iffalse

\ToDo{Insert text stolen from update paper}

\else

Consider the following Haskell definition of the standard
function @partition@ that divides a list into two, those elements
that satisfy a predicate @p@ and those that do not:
@
  partition :: (a->Bool) -> [a] -> ([a],[a])
  partition p [] = ([],[])
  partition p (x:xs) = if p x then (x:ys, zs)
                              else (ys, x:zs)
                     where
                       (ys,zs) = partition p xs
@
By the time this definition has been desugared, it looks like this:
@
  partition p xs
    = case xs of
        [] -> ([],[])
        (x:xs) -> let
                    t = partition p xs
                    ys = fst t
                    zs = snd t
                  in
                  if p x then (x:ys,zs)
                         else (ys,x:zs)
@
Lazy evaluation demands that the recursive call is bound to an
intermediate variable, @t@, from which @ys@ and @zs@ are lazily
selected. (The functions @fst@ and @snd@ select the first and second
elements of a pair, respectively.)

Now, suppose that @partition@ is applied to a list @[x1,x2]@,
all of whose
elements satisfy @p@.  We can get a good idea of what will happen
at runtime by unrolling the recursion a few times in our heads.
Unrolling once, and remembering that @(p x1)@ is @True@, we get this:
@
  partition p [x1,x2]
=
  let t1 = partition [x2]
      ys1 = fst t1
      zs1 = snd t1
  in (x1:ys1, zs1)
@
Unrolling the rest of the way gives this:
@
  partition p [x1,x2]
=
  let t2  = ([],[])
      ys2 = fst t2
      zs2 = snd t2
      t1  = (x2:ys2,zs2)
      ys1 = fst t1
      zs1 = snd t1
   in (x1:ys1,zs1)
@
Now consider what happens if @zs1@ is evaluated.  It is bound to a
thunk, which will push an update frame before evaluating the
expression @snd t1@.  This expression in turn forces evaluation of
@zs2@, which pushes an update frame before evaluating @snd t2@.
Indeed the stack of update frames will grow as deep as the list is
long when @zs1@ is evaluated.  This is rather galling, since all the
thunks @zs1@, @zs2@, and so on, have the same value.

\ToDo{Describe the state-transformer case in which we get a space leak from
pending update frames.}

The solution is simple.  The garbage collector, which is going to traverse the
update stack in any case, can easily identify two update frames that are directly
on top of each other.  The second of these will update its target with the same
value as the first.  Therefore, the garbage collector can perform the update 
right away, by overwriting one update target with an indirection to the second,
and eliminate the corresponding update frame.  In this way ever-growing stacks of
update frames are reduced to a single representative at garbage collection time.
If this is done at the start of garbage collection then, if it turns out that
some of these update targets are garbage they will be collected right away.

\fi

\subsection{Space leaks and selectors}

\iffalse

\ToDo{Insert text stolen from update paper}

\else

In 1987, Wadler identified an important source of space leaks in
lazy functional programs.  Consider the Haskell function definition:
@
  f p = (g1 a, g2 b) where (a,b) = p
@
The pattern-matching in the @where@ clause is known as
{\em lazy pattern-matching}, because it is performed only if @a@
or @b@ is actually evaluated.  The desugarer translates lazy pattern matching
to the use of selectors, @fst@ and @snd@ in this case:
@
  f p = let a = fst p
            b = snd p
        in
        (b, a)
@
Now suppose that the second component of the pair @(f p)@, namely @a@,
is evaluated and discarded, but the first is not although it remains
reachable.  The garbage collector will find that the thunk for @b@ refers
to @p@ and hence to @a@.  Thus, although @a@ cannot ever be used again, its
space is retained.  It turns out that this space leak can have a very bad effect
indeed on a program's space behaviour (Section~\ref{sect:selector-results}).

Wadler's paper also proposed a solution: if the garbage collector
encounters a thunk of the form @snd p@, where @p@ is evaluated, then
the garbage collector should perform the selection and overwrite the
thunk with a pointer to the second component of the pair.  In effect, the
garbage collector thereby performs a bounded amount of as-yet-undemanded evaluation
in the hope of improving space behaviour.
We implement this idea directly, by making the garbage collector
eagerly execute all selector thunks\footnote{A word of caution: it is rather easy 
to make a mistake in the implementation, especially if the garbage collector
uses pointer reversal to traverse the reachable graph.},
with results 
reported in Section~\ref{sect:THUNK_SEL}.

One could easily imagine generalisations of this idea, with the garbage 
collector performing bounded amounts of space-saving work.  One example is
this:
@
  f x []     = (x,x)
  f x (y:ys) = f (x+1) ys
@
Most lazy evaluators will build up a chain of thunks for the accumulating
parameter, @x@, each of which increments @x@.  It is not safe to evaluate
any of these thunks eagerly, since @f@ is not strict in @x@, and we know nothing
about the value of @x@ passed in the initial call to @f@.
On the other hand, if the garbage collector found a thunk @(x+1)@ where
@x@ happened to be evaluated, then it could ``execute'' it eagerly.
If done carefully, the entire chain could be eliminated in a single
garbage collection.   We have not (yet) implemented this idea.
A very similar idea, dubbed ``stingy evaluation'', is described 
by <.stingy.>.

<.sparud lazy pattern matching.> describes another solution to the
lazy-pattern-matching
problem.  His solution involves adding code to the two thunks for
@a@ and @b@ so that if either is evaluated it arranges to update the
other as well as itself.  The garbage-collector solution is a little
more general, since it applies whether or not the selectors were
generated by lazy pattern matching, and in our setting it was easier
to implement than Sparud's.

\fi


\subsection{Internal workings of the Compacting Collector}

\subsection{Internal workings of the Copying Collector}

\subsection{Internal workings of the Generational Collector}



\section{Profiling}

Registering costs centres looks awkward - can we tidy it up?

\section{Parallelism}

Something about global GC, inter-process messages and fetchmes.

\section{Debugging}

\section{Ticky Ticky profiling}

Measure what proportion of ...:
\begin{itemize}
\item
... Enters are to data values, function values, thunks.
\item
... allocations are for data values, functions values, thunks.
\item
... updates are for data values, function values.
\item
... updates ``fit''
\item
... return-in-heap (dynamic)
\item
... vectored return (dynamic)
\item
... updates are wasted (never re-entered).
\item
... constructor returns get away without hitting an update.
\end{itemize}

%************************************************************************
%*                                                                      *
\subsubsection[ticky-stk-heap-use]{Stack and heap usage}
%*                                                                      *
%************************************************************************

Things we are interested in here:
\begin{itemize}
\item
How many times we do a heap check and move @Hp@; comparing this with
the allocations gives an indication of how many things we get per trip
to the well:

If we do a ``heap lookahead,'' we haven't really allocated any
heap, so we need to undo the effects of an @ALLOC_HEAP@:

\item
The stack high-water mark.

\item
Re-use of stack slots, and stubbing of stack slots:

\end{itemize}

%************************************************************************
%*                                                                      *
\subsubsection[ticky-allocs]{Allocations}
%*                                                                      *
%************************************************************************

We count things every time we allocate something in the dynamic heap.
For each, we count the number of words of (1)~``admin'' (header),
(2)~good stuff (useful pointers and data), and (3)~``slop'' (extra
space, in hopes it will allow an in-place update).

The first five macros are inserted when the compiler generates code
to allocate something; the categories correspond to the @ClosureClass@
datatype (manifest functions, thunks, constructors, big tuples, and
partial applications).

We may also allocate space when we do an update, and there isn't
enough space.  These macros suffice (for: updating with a partial
application and a constructor):

In the threaded world, we allocate space for the spark pool, stack objects,
and thread state objects.

The histogrammy bit is fairly straightforward; the @-2@ is: one for
0-origin C arrays; the other one because we do {\em no} one-word
allocations, so we would never inc that histogram slot; so we shift
everything over by one.

Some hard-to-account-for words are allocated by/for primitives,
includes Integer support.  @ALLOC_PRIM2@ tells us about these.  We
count everything as ``goods'', which is not strictly correct.
(@ALLOC_PRIM@ is the same sort of stuff, but we know the
admin/goods/slop breakdown.)

%************************************************************************
%*                                                                      *
\subsubsection[ticky-enters]{Enters}
%*                                                                      *
%************************************************************************

We do more magical things with @ENT_FUN_DIRECT@.  Besides simply knowing
how many ``fast-entry-point'' enters there were, we'd like {\em simple}
information about where those enters were, and the properties thereof.
@
struct ent_counter {
    unsigned    registeredp:16, /* 0 == no, 1 == yes */
                arity:16,       /* arity (static info) */
                Astk_args:16,   /* # of args off A stack */
                Bstk_args:16;   /* # of args off B stack */
                                /* (rest of args are in registers) */
    StgChar     *f_str;         /* name of the thing */
    StgChar     *f_arg_kinds;   /* info about the args types */
    StgChar     *wrap_str;      /* name of its wrapper (if any) */
    StgChar     *wrap_arg_kinds;/* info about the orig wrapper's arg types */
    I_          ctr;            /* the actual counter */
    struct ent_counter *link;   /* link to chain them all together */
};
@

%************************************************************************
%*                                                                      *
\subsubsection[ticky-returns]{Returns}
%*                                                                      *
%************************************************************************

Whenever a ``return'' occurs, it is returning the constituent parts of
a data constructor.  The parts can be returned either in registers, or
by allocating some heap to put it in (the @ALLOC_*@ macros account for
the allocation).  The constructor can either be an existing one
(@*OLD*@) or we could have {\em just} figured out this stuff
(@*NEW*@).

Here's some special magic that Simon wants [edited to match names
actually used]:

@
From: Simon L Peyton Jones <simonpj>
To: partain, simonpj
Subject: counting updates
Date: Wed, 25 Mar 92 08:39:48 +0000

I'd like to count how many times we update in place when actually Node
points to the thing.  Here's how:

@RET_OLD_IN_REGS@ sets the variable @ReturnInRegsNodeValid@ to @True@;
@RET_NEW_IN_REGS@ sets it to @False@.

@RET_SEMI_???@ sets it to??? ToDo [WDP]

@UPD_CON_IN_PLACE@ tests the variable, and increments @UPD_IN_PLACE_COPY_ctr@
if it is true.

Then we need to report it along with the update-in-place info.
@


Of all the returns (sum of four categories above), how many were
vectored?  (The rest were obviously unvectored).

%************************************************************************
%*                                                                      *
\subsubsection[ticky-update-frames]{Update frames}
%*                                                                      *
%************************************************************************

These macros count up the following update information.

\begin{tabular}{|l|l|} \hline
Macro                   &       Counts                                  \\ \hline
                        &                                               \\
@UPDF_STD_PUSHED@       &       Update frame pushed                     \\
@UPDF_CON_PUSHED@       &       Constructor update frame pushed         \\
@UPDF_HOLE_PUSHED@      &       An update frame to update a black hole  \\
@UPDF_OMITTED@          &       A thunk decided not to push an update frame \\
                        &       (all subsets of @ENT_THK@)              \\
@UPDF_RCC_PUSHED@       &       Cost Centre restore frame pushed        \\
@UPDF_RCC_OMITTED@      &       Cost Centres not required -- not pushed \\\hline
\end{tabular}

%************************************************************************
%*                                                                      *
\subsubsection[ticky-updates]{Updates}
%*                                                                      *
%************************************************************************

These macros record information when we do an update.  We always
update either with a data constructor (CON) or a partial application
(PAP).

\begin{tabular}{|l|l|}\hline
Macro                   &       Where                                           \\ \hline
                        &                                                       \\
@UPD_EXISTING@          &       Updating with an indirection to something       \\
                        &       already in the heap                             \\
@UPD_SQUEEZED@          &       Same as @UPD_EXISTING@ but because              \\
                        &       of stack-squeezing                              \\
@UPD_CON_W_NODE@        &       Updating with a CON: by indirecting to Node     \\
@UPD_CON_IN_PLACE@      &       Ditto, but in place                             \\
@UPD_CON_IN_NEW@        &       Ditto, but allocating the object                \\
@UPD_PAP_IN_PLACE@      &       Same, but updating w/ a PAP                     \\
@UPD_PAP_IN_NEW@        &                                                       \\\hline
\end{tabular}

%************************************************************************
%*                                                                      *
\subsubsection[ticky-selectors]{Doing selectors at GC time}
%*                                                                      *
%************************************************************************

@GC_SEL_ABANDONED@: we could've done the selection, but we gave up
(e.g., to avoid overflowing the C stack); @GC_SEL_MINOR@: did a
selection in a minor GC; @GC_SEL_MAJOR@: ditto, but major GC.



\section{History}

We're nuking the following:

\begin{itemize}
\item
  Two stacks

\item
  Return in registers.
  This lets us remove update code pointers from info tables,
  removes the need for phantom info tables, simplifies 
  semi-tagging, etc.

\item
  Threaded GC.
  Careful analysis suggests that it doesn't buy us very much
  and it is hard to work with.

  Eliminating threaded GCs eliminates the desire to share SMReps
  so they are (once more) part of the Info table.

\item
  RetReg.
  Doesn't buy us anything on a register-poor architecture and
  isn't so important if we have semi-tagging.

@
    - Probably bad on register poor architecture 
    - Can avoid need to write return address to stack on reg rich arch.
      - when a function does a small amount of work, doesn't 
        enter any other thunks and then returns.
        eg entering a known constructor (but semitagging will catch this)
    - Adds complications
@

\item
  Update in place

  This lets us drop CONST closures and CHARLIKE closures (assuming we
  don't support Unicode).  The only point of these closures was to 
  avoid updating with an indirection.

  We also drop @MIN_UPD_SIZE@ --- all we need is space to insert an
  indirection or a black hole.

\item
  STATIC SMReps are now called CONST

\item
  @SM_MUTVAR@ is new

\item The profiling ``kind'' field is now encoded in the @INFO_TYPE@ field.
This identifies the general sort of the closure for profiling purposes.

\item Various papers describe deleting update frames for unreachable objects.
  This has never been implemented and we don't plan to anytime soon.

\end{itemize}

\section{Old tricks}

@CAF@ indirections:

These are statically defined closures which have been updated with a
heap-allocated result.  Initially these are exactly the same as a
@STATIC@ closure but with special entry code. On entering the closure
the entry code must:

\begin{itemize}
\item Allocate a black hole in the heap which will be updated with
      the result.
\item Overwrite the static closure with a special @CAF@ indirection.

\item Link the static indirection onto the list of updated @CAF@s.
\end{itemize}

The indirection and the link field require the initial @STATIC@
closure to be of at least size @MIN_UPD_SIZE@ (excluding the fixed
header).

@CAF@s are treated as special garbage collection roots.  These roots
are explicitly collected by the garbage collector, since they may
appear in code even if they are not linked with the main heap.  They
consequently represent potentially enormous space-leaks.  A @CAF@
closure retains a fixed location in statically allocated data space.
When updated, the contents of the @CAF@ indirection are changed to
reflect the new closure. @CAF@ indirections require special garbage
collection code.

\section{Old stuff about SRTs}

Garbage collection of @CAF@s is tricky.  We have to cope with explicit
collection from the @CAFlist@ as well as potential references from the
stack and heap which will cause the @CAF@ evacuation code to be
called.  They are treated like indirections which are shorted out.
However they must also be updated to point to the new location of the
new closure as the @CAF@ may still be used by references which
reside in the code.

{\bf Copying Collection}

A first scheme might use evacuation code which evacuates the reference
and updates the indirection. This is no good as subsequent evacuations
will result in an already evacuated closure being evacuated. This will
leave a forward reference in to-space!

An alternative scheme evacuates the @CAFlist@ first. The closures
referenced are evacuated and the @CAF@ indirection updated to point to
the evacuated closure. The @CAF@ evacuation code simply returns the
updated indirection pointer --- the pointer to the evacuated closure.
Unfortunately the closure the @CAF@ references may be a static
closure, in fact, it may be another @CAF@. This will cause the second
@CAF@'s evacuation code to be called before the @CAF@ has been
evacuated, returning an unevacuated pointer.

Another scheme leaves updating the @CAF@ indirections to the end of
the garbage collection.  All the references are evacuated and
scavenged as usual (including the @CAFlist@). Once collection is
complete the @CAFlist@ is traversed updating the @CAF@ references with
the result of evacuating the referenced closure again. This will
immediately return as it must be a forward reference, a static
closure, or a @CAF@ which will indirect by evacuating its reference.

The crux of the problem is that the @CAF@ evacuation code needs to
know if its reference has already been evacuated and updated. If not,
then the reference can be evacuated, updated and returned safely
(possibly evacuating another @CAF@). If it has, then the updated
reference can be returned. This can be done using two @CAF@
info-tables. At the start of a collection the @CAFlist@ is traversed
and set to an internal {\em evacuate and update} info-table. During
collection, evacution of such a @CAF@ also results in the info-table
being reset back to the standard @CAF@ info-table. Thus subsequent
evacuations will simply return the updated reference. On completion of
the collection all @CAF@s will have {\em return reference} info-tables
again.

This is the scheme we adopt. A @CAF@ indirection has evacuation code
which returns the evacuated and updated reference. During garbage
collection, all the @CAF@s are overwritten with an internal @CAF@ info
table which has evacuation code which performs this evacuate and
update and restores the original @CAF@ code. At some point during the
collection we must ensure that all the @CAF@s are indeed evacuated.

The only potential problem with this scheme is a cyclic list of @CAF@s
all directly referencing (possibly via indirections) another @CAF@!
Evacuation of the first @CAF@ will fail in an infinite loop of @CAF@
evacuations. This is solved by ensuring that the @CAF@ info-table is
updated to a {\em return reference} info-table before performing the
evacuate and update. If this {\em return reference} evacuation code is
called before the actual evacuation is complete it must be because
such a cycle of references exists. Returning the still unevacuated
reference is OK --- all the @CAF@s will now reference the same
@CAF@ which will reference itself! Construction of such a structure
indicates the program must be in an infinite loop.

{\bf Compacting Collector}

When shorting out a @CAF@, its reference must be marked. A first
attempt might explicitly mark the @CAF@s, updating the reference with
the marked reference (possibly short circuting indirections). The
actual @CAF@ marking code can indicate that they have already been
marked (though this might not have actually been done yet) and return
the indirection pointer so it is shorted out. Unfortunately the @CAF@
reference might point to an indirection which will be subsequently
shorted out. Rather than returning the @CAF@ reference we treat the
@CAF@ as an indirection, calling the mark code of the reference, which
will return the appropriately shorted reference.

Problem: Cyclic list of @CAF@s all directly referencing (possibly via
indirections) another @CAF@!

Before compacting, the locations of the @CAF@ references are
explicitly linked to the closures they reference (if they reference
heap allocated closures) so that the compacting process will update
them to the closure's new location. Unfortunately these locations'
@CAF@ indirections are static.  This causes premature termination
since the test to find the info pointer at the end of the location
list will match more than one value.  This can be solved by using an
auxiliary dynamic array (on the top of the A stack).  One location for
each @CAF@ indirection is linked to the closure that the @CAF@
references. Once collection is complete this array is traversed and
the corresponding @CAF@ is then updated with the updated pointer from
the auxiliary array.


It is possible to use an alternative marking scheme, using a similar
idea to the copying solution. This scheme avoids the need to update
the @CAF@ references explicitly. We introduce an auxillary {\em mark
and update} @CAF@ info-table which is used to update all @CAF@s at the
start of a collection. The new code marks the @CAF@ reference,
updating it with the returned reference.  The returned reference is
itself returned so the @CAF@ is shorted out.  The code also modifies the
@CAF@ info-table to be a {\em return reference}.  Subsequent attempts to
mark the @CAF@ simply return the updated reference.

A cyclic @CAF@ reference will result in an attempt to mark the @CAF@
before the marking has been completed and the reference updated. We
cannot start marking the @CAF@ as it is already being marked. Nor can
we return the reference as it has not yet been updated. Neither can we
treat the CAF as an indirection since the @CAF@ reference has been
obscured by the pointer reversal stack. All we can do is return the
@CAF@ itself. This will result in some @CAF@ references not being
shorted out.

This scheme has not been adopted but has been implemented. The code is
commented out with @#if 0@.

\subsection{The virtual register set}

We refer to any (atomic) part of the virtual machine state as a ``register.''
These ``registers'' may be shared between all threads in the system or may be
specific to each thread.

Global: 
@
  Hp
  HpLim
  Thread preemption flag
@

Thread specific:
@
  TSO - pointer to the TSO for when we have to pack thread away
  Sp
  SpLim
  Su - used to calculate number of arguments on stack
     - this is a more convenient representation
  Call/return registers (aka General purpose registers)
  Cost centre (and other debug/profile info)
  Statistic gathering (not in production system)
  Exception handlers 
    Heap overflow  - possible global?
    Stack overflow - possibly global?
    Pattern match failure
    maybe a failWith handler?
    maybe an exitWith handler?
    ...
@

Some of these virtual ``registers'' are used very frequently and should
be mapped onto machine registers if at all possible.  Others are used
very infrequently and can be kept in memory to free up registers for
other uses.

On register-poor architectures, we can play a few tricks to reduce the
number of virtual registers which need to be accessed on a regular
basis:

@
- HpLim trick
- Grow stack and heap towards each other (single-threaded system only)
- We might need to keep the C stack pointer in a register if that
  is what the OS expects when a signal occurs.
- Preemption flag trick
- If any of the frequently accessed registers cannot be mapped onto
  machine registers we should keep the TSO in a machine register to
  allow faster access to all the other non-machine registers.
@


\end{document}