path: root/gcc/doc/tree-ssa.texi

@c Copyright (c) 2004 Free Software Foundation, Inc.
@c Free Software Foundation, Inc.
@c This is part of the GCC manual.
@c For copying conditions, see the file gcc.texi.

@c ---------------------------------------------------------------------
@c Tree SSA
@c ---------------------------------------------------------------------

@node Tree SSA
@chapter Analysis and Optimization of GIMPLE Trees
@cindex Tree SSA
@cindex Optimization infrastructure for GIMPLE

GCC uses three main intermediate languages to represent the program
during compilation: GENERIC, GIMPLE and RTL@.  GENERIC is a
language-independent representation generated by each front end.  It
is used to serve as an interface between the parser and optimizer.
GENERIC is a common representation that is able to represent programs
written in all the languages supported by GCC@.

GIMPLE and RTL are used to optimize the program.  GIMPLE is used for
target and language independent optimizations (e.g., inlining,
constant propagation, tail call elimination, redundancy elimination,
etc).  Much like GENERIC, GIMPLE is a language independent, tree based
representation. However, it differs from GENERIC in that the GIMPLE
grammar is more restrictive: expressions contain no more than 3
operands (except function calls), it has no control flow structures
and expressions with side-effects are only allowed on the right hand
side of assignments.  See the chapter describing GENERIC and GIMPLE
for more details.

This chapter describes the data structures and functions used in the
GIMPLE optimizers (also known as ``tree optimizers'' or ``middle
end'').  In particular, it focuses on all the macros, data structures,
functions and programming constructs needed to implement optimization
passes for GIMPLE@.

@menu
* GENERIC::		A high-level language-independent representation.
* GIMPLE::              A lower-level factored tree representation.
* Annotations::		Attributes for statements and variables.
* Statement Operands::	Variables referenced by GIMPLE statements.
* SSA::			Static Single Assignment representation.
* Alias analysis::	Representing aliased loads and stores.
@end menu

@node GENERIC
@section GENERIC
@cindex GENERIC

The purpose of GENERIC is simply to provide a language-independent way of
representing an entire function in trees.  To this end, it was necessary to
add a few new tree codes to the back end, but most everything was already
there.  If you can express it with the codes in @code{gcc/tree.def}, it's
GENERIC.

Early on, there was a great deal of debate about how to think about
statements in a tree IL.  In GENERIC, a statement is defined as any
expression whose value, if any, is ignored.  A statement will always
have @code{TREE_SIDE_EFFECTS} set (or it will be discarded), but a
non-statement expression may also have side effects.  A
@code{CALL_EXPR}, for instance.

It would be possible for some local optimizations to work on the
GENERIC form of a function; indeed, the adapted tree inliner works
fine on GENERIC, but the current compiler performs inlining after
lowering to GIMPLE (a restricted form described in the next section).
Indeed, currently the frontends perform this lowering before handing
off to @code{tree_rest_of_compilation}, but this seems inelegant.

If necessary, a front end can use some language-dependent tree codes
in its GENERIC representation, so long as it provides a hook for
converting them to GIMPLE and doesn't expect them to work with any
(hypothetical) optimizers that run before the conversion to GIMPLE.
The intermediate representation used while parsing C and C++ looks
very little like GENERIC, but the C and C++ gimplifier hooks are
perfectly happy to take it as input and spit out GIMPLE.

@node GIMPLE
@section GIMPLE
@cindex GIMPLE

GIMPLE is a simplified subset of GENERIC for use in optimization.  The
particular subset chosen (and the name) was heavily influenced by the
SIMPLE IL used by the McCAT compiler project at McGill University,
though we have made some different choices.  For one thing, SIMPLE
doesn't support @code{goto}; a production compiler can't afford that
kind of restriction.

GIMPLE retains much of the structure of the parse trees: lexical
scopes are represented as containers, rather than markers.  However,
expressions are broken down into a 3-address form, using temporary
variables to hold intermediate values.  Also, control structures are
lowered to gotos.

In GIMPLE no container node is ever used for its value; if a
@code{COND_EXPR} or @code{BIND_EXPR} has a value, it is stored into a
temporary within the controlled blocks, and that temporary is used in
place of the container.

The compiler pass which lowers GENERIC to GIMPLE is referred to as the
@samp{gimplifier}.  The gimplifier works recursively, replacing complex
statements with sequences of simple statements.  

@c Currently, the only way to
@c tell whether or not an expression is in GIMPLE form is by recursively
@c examining it; in the future there will probably be a flag to help avoid
@c redundant work.  FIXME FIXME

@menu
* Interfaces::
* Temporaries::
* GIMPLE Expressions::
* Statements::
* GIMPLE Example::
* Rough GIMPLE Grammar::
@end menu

@node Interfaces
@subsection Interfaces
@cindex gimplification

The tree representation of a function is stored in
@code{DECL_SAVED_TREE}.  It is lowered to GIMPLE by a call to
@code{gimplify_function_tree}.

If a front end wants to include language-specific tree codes in the tree
representation which it provides to the back end, it must provide a
definition of @code{LANG_HOOKS_GIMPLIFY_EXPR} which knows how to
convert the front end trees to GIMPLE.  Usually such a hook will involve
much of the same code for expanding front end trees to RTL.  This function
can return fully lowered GIMPLE, or it can return GENERIC trees and let the
main gimplifier lower them the rest of the way; this is often simpler.

The C and C++ front ends currently convert directly from front end
trees to GIMPLE, and hand that off to the back end rather than first
converting to GENERIC.  Their gimplifier hooks know about all the
@code{_STMT} nodes and how to convert them to GENERIC forms.  There
was some work done on a genericization pass which would run first, but
the existence of @code{STMT_EXPR} meant that in order to convert all
of the C statements into GENERIC equivalents would involve walking the
entire tree anyway, so it was simpler to lower all the way.  This
might change in the future if someone writes an optimization pass
which would work better with higher-level trees, but currently the
optimizers all expect GIMPLE.

A front end which wants to use the tree optimizers (and already has
some sort of whole-function tree representation) only needs to provide
a definition of @code{LANG_HOOKS_GIMPLIFY_EXPR}, call
@code{gimplify_function_tree} to lower to GIMPLE, and then hand off to
@code{tree_rest_of_compilation} to compile and output the function.

You can tell the compiler to dump a C-like representation of the GIMPLE
form with the flag @code{-fdump-tree-gimple}.

@node Temporaries
@subsection Temporaries
@cindex Temporaries

When gimplification encounters a subexpression which is too complex, it
creates a new temporary variable to hold the value of the subexpression,
and adds a new statement to initialize it before the current statement.
These special temporaries are known as @samp{expression temporaries}, and are
allocated using @code{get_formal_tmp_var}.  The compiler tries to
always evaluate identical expressions into the same temporary, to simplify
elimination of redundant calculations.

We can only use expression temporaries when we know that it will not be
reevaluated before its value is used, and that it will not be otherwise
modified@footnote{These restrictions are derived from those in Morgan 4.8.}.
Other temporaries can be allocated using
@code{get_initialized_tmp_var} or @code{create_tmp_var}.

Currently, an expression like @code{a = b + 5} is not reduced any
further.  We tried converting it to something like
@smallexample
  T1 = b + 5;
  a = T1;
@end smallexample
but this bloated the representation for minimal benefit.  However, a
variable which must live in memory cannot appear in an expression; its
value is explicitly loaded into a temporary first.  Similarly, storing
the value of an expression to a memory variable goes through a
temporary.

@node GIMPLE Expressions
@subsection Expressions
@cindex GIMPLE Expressions

In general, expressions in GIMPLE consist of an operation and the
appropriate number of simple operands; these operands must either be a
GIMPLE rvalue (@code{is_gimple_val}), i.e. a constant or a register
variable.  More complex operands are factored out into temporaries, so
that
@smallexample
  a = b + c + d 
@end smallexample
becomes
@smallexample
  T1 = b + c;
  a = T1 + d;
@end smallexample

The same rule holds for arguments to a @code{CALL_EXPR}.

The target of an assignment is usually a variable, but can also be an
@code{INDIRECT_REF} or a compound lvalue as described below.

@menu
* Compound Expressions::
* Compound Lvalues::
* Conditional Expressions::
* Logical Operators::
@end menu

@node Compound Expressions
@subsubsection Compound Expressions
@cindex Compound Expressions

The left-hand side of a C comma expression is simply moved into a separate
statement.

@node Compound Lvalues
@subsubsection Compound Lvalues
@cindex Compound Lvalues

Currently compound lvalues involving array and structure field references
are not broken down; an expression like @code{a.b[2] = 42} is not reduced
any further (though complex array subscripts are).  This restriction is a
workaround for limitations in later optimizers; if we were to convert this
to

@smallexample
  T1 = &a.b;
  T1[2] = 42;
@end smallexample

alias analysis would not remember that the reference to @code{T1[2]} came
by way of @code{a.b}, so it would think that the assignment could alias
another member of @code{a}; this broke @code{struct-alias-1.c}.  Future
optimizer improvements may make this limitation unnecessary.

@node Conditional Expressions
@subsubsection Conditional Expressions
@cindex Conditional Expressions

A C @code{?:} expression is converted into an @code{if} statement with
each branch assigning to the same temporary.  So,

@smallexample
  a = b ? c : d;
@end smallexample
becomes
@smallexample
  if (b)
    T1 = c;
  else
    T1 = d;
  a = T1;
@end smallexample

Note that in GIMPLE, @code{if} statements are also represented using
@code{COND_EXPR}, as described below.

@node Logical Operators
@subsubsection Logical Operators
@cindex Logical Operators

Except when they appear in the condition operand of a @code{COND_EXPR},
logical `and' and `or' operators are simplified as follows:
@code{a = b && c} becomes

@smallexample
  T1 = (bool)b;
  if (T1)
    T1 = (bool)c;
  a = T1;
@end smallexample

Note that @code{T1} in this example cannot be an expression temporary,
because it has two different assignments.

@node Statements
@subsection Statements
@cindex Statements

Most statements will be assignment statements, represented by
@code{MODIFY_EXPR}.  A @code{CALL_EXPR} whose value is ignored can
also be a statement.  No other C expressions can appear at statement level;
a reference to a volatile object is converted into a @code{MODIFY_EXPR}.

There are also several varieties of complex statements.

@menu
* Blocks::
* Statement Sequences::
* Empty Statements::
* Loops::
* Selection Statements::
* Jumps::
* Cleanups::
* GIMPLE Exception Handling::
@end menu

@node Blocks
@subsubsection Blocks
@cindex Blocks

Block scopes and the variables they declare in GENERIC and GIMPLE are
expressed using the @code{BIND_EXPR} code, which in previous versions of
GCC was primarily used for the C statement-expression extension.

Variables in a block are collected into @code{BIND_EXPR_VARS} in
declaration order.  Any runtime initialization is moved out of 
@code{DECL_INITIAL} and into a statement in the controlled block.  When
gimplifying from C or C++, this initialization replaces the
@code{DECL_STMT}.

Variable-length arrays (VLAs) complicate this process, as their size often
refers to variables initialized earlier in the block.  To handle this, we
currently split the block at that point, and move the VLA into a new, inner
@code{BIND_EXPR}.  This strategy may change in the future.

@code{DECL_SAVED_TREE} for a GIMPLE function will always be a 
@code{BIND_EXPR} which contains declarations for the temporary variables
used in the function.

A C++ program will usually contain more @code{BIND_EXPR}s than there are
syntactic blocks in the source code, since several C++ constructs have
implicit scopes associated with them.  On the other hand, although the C++
front end uses pseudo-scopes to handle cleanups for objects with
destructors, these don't translate into the GIMPLE form; multiple
declarations at the same level use the same BIND_EXPR.

@node Statement Sequences
@subsubsection Statement Sequences
@cindex Statement Sequences

Multiple statements at the same nesting level are collected into a
@code{STATEMENT_LIST}.  Statement lists are modified and traversed
using the interface in @samp{tree-iterator.h}.

@node Empty Statements
@subsubsection Empty Statements
@cindex Empty Statements

Whenever possible, statements with no effect are discarded.  But if they
are nested within another construct which cannot be discarded for some
reason, they are instead replaced with an empty statement, generated by
@code{build_empty_stmt}.  Initially, all empty statements were shared,
after the pattern of the Java front end, but this caused a lot of trouble in
practice.

An empty statement is represented as @code{(void)0}.

@node Loops
@subsubsection Loops
@cindex Loops

At one time loops were expressed in GIMPLE using @code{LOOP_EXPR}, but
now they are lowered to explicit gotos.

@node Selection Statements
@subsubsection Selection Statements
@cindex Selection Statements

A simple selection statement, such as the C @code{if} statement, is
expressed in GIMPLE using a void @code{COND_EXPR}.  If only one branch is
used, the other is filled with an empty statement.

Normally, the condition expression is reduced to a simple comparison.  If
it is a shortcut (@code{&&} or @code{||}) expression, however, we try to
break up the @code{if} into multiple @code{if}s so that the implied shortcut
is taken directly, much like the transformation done by @code{do_jump} in
the RTL expander.

A @code{SWITCH_EXPR} in GIMPLE contains the condition and a
@code{TREE_VEC} of @code{CASE_LABEL_EXPR}s describing the case values
and corresponding @code{LABEL_DECL}s to jump to.  The body of the
@code{switch} is moved after the @code{SWITCH_EXPR}.

@node Jumps
@subsubsection Jumps
@cindex Jumps

Other jumps are expressed by either @code{GOTO_EXPR} or @code{RETURN_EXPR}.

The operand of a @code{GOTO_EXPR} must be either a label or a variable
containing the address to jump to.

The operand of a @code{RETURN_EXPR} is either @code{NULL_TREE} or a
@code{MODIFY_EXPR} which sets the return value.  It would be nice to
move the @code{MODIFY_EXPR} into a separate statement, but the special
return semantics in @code{expand_return} make that difficult.  It may
still happen in the future, perhaps by moving most of that logic into
@code{expand_assignment}.

@node Cleanups
@subsubsection Cleanups
@cindex Cleanups

Destructors for local C++ objects and similar dynamic cleanups are
represented in GIMPLE by a @code{TRY_FINALLY_EXPR}.  When the controlled
block exits, the cleanup is run.

@code{TRY_FINALLY_EXPR} complicates the flow graph, since the cleanup
needs to appear on every edge out of the controlled block; this
reduces our freedom to move code across these edges.  Therefore, the
EH lowering pass which runs before most of the optimization passes
eliminates these expressions by explicitly adding the cleanup to each
edge.

@node GIMPLE Exception Handling
@subsubsection Exception Handling
@cindex GIMPLE Exception Handling

Other exception handling constructs are represented using
@code{TRY_CATCH_EXPR}.  The handler operand of a @code{TRY_CATCH_EXPR} 
can be a normal statement to be executed if the controlled block throws an
exception, or it can have one of two special forms:

@enumerate
@item A @code{CATCH_EXPR} executes its handler if the thrown exception
  matches one of the allowed types.  Multiple handlers can be
  expressed by a sequence of @code{CATCH_EXPR} statements.
@item An @code{EH_FILTER_EXPR} executes its handler if the thrown
  exception does not match one of the allowed types.
@end enumerate

Currently throwing an exception is not directly represented in GIMPLE,
since it is implemented by calling a function.  At some point in the future
we will want to add some way to express that the call will throw an
exception of a known type.

Just before running the optimizers, the compiler lowers the high-level
EH constructs above into a set of @samp{goto}s, magic labels, and EH
regions.  Continuing to unwind at the end of a cleanup is represented
with a @code{RESX_EXPR}.

@node GIMPLE Example
@subsection GIMPLE Example
@cindex GIMPLE Example

@smallexample
struct A @{ A(); ~A(); @};

int i;
int g();
void f()
@{
  A a;
  int j = (--i, i ? 0 : 1);

  for (int x = 42; x > 0; --x)
    @{
      i += g()*4 + 32;
    @}
@}
@end smallexample

becomes

@smallexample
void f()
@{
  int i.0;
  int T.1;
  int iftmp.2;
  int T.3;
  int T.4;
  int T.5;
  int T.6;

  @{
    struct A a;
    int j;

    __comp_ctor (&a);
    try
      @{
        i.0 = i;
        T.1 = i.0 - 1;
        i = T.1;
        i.0 = i;
        if (i.0 == 0)
          iftmp.2 = 1;
        else
          iftmp.2 = 0;
        j = iftmp.2;
        @{
          int x;

          x = 42;
          goto test;
          loop:;

          T.3 = g ();
          T.4 = T.3 * 4;
          i.0 = i;
          T.5 = T.4 + i.0;
          T.6 = T.5 + 32;
          i = T.6;
          x = x - 1;

          test:;
          if (x > 0)
            goto loop;
          else
            goto break_;
          break_:;
        @}
      @}
    finally
      @{
        __comp_dtor (&a);
      @}
  @}
@}
@end smallexample

@node Rough GIMPLE Grammar
@subsection Rough GIMPLE Grammar
@cindex Rough GIMPLE Grammar

@smallexample
function:
  FUNCTION_DECL
    DECL_SAVED_TREE -> block
block:
  BIND_EXPR
    BIND_EXPR_VARS -> DECL chain
    BIND_EXPR_BLOCK -> BLOCK
    BIND_EXPR_BODY 
      -> compound-stmt
compound-stmt:
  COMPOUND_EXPR
    op0 -> non-compound-stmt
    op1 -> stmt
stmt: compound-stmt
  | non-compound-stmt
non-compound-stmt:
  block
  | if-stmt
  | switch-stmt
  | jump-stmt
  | label-stmt
  | try-stmt
  | modify-stmt
  | call-stmt
if-stmt:
  COND_EXPR
    op0 -> condition
    op1 -> stmt
    op2 -> stmt
switch-stmt:
  SWITCH_EXPR
    op0 -> val
    op1 -> NULL_TREE
    op2 -> TREE_VEC of CASE_LABEL_EXPRs
jump-stmt:
    GOTO_EXPR
      op0 -> LABEL_DECL | '*' ID
  | RETURN_EXPR
      op0 -> modify-stmt
             | NULL_TREE
label-stmt:
  LABEL_EXPR
      op0 -> LABEL_DECL
try-stmt:
  TRY_CATCH_EXPR
    op0 -> stmt
    op1 -> handler
  | TRY_FINALLY_EXPR
    op0 -> stmt
    op1 -> stmt
handler:
  catch-seq
  | EH_FILTER_EXPR
  | stmt
catch-seq:
  CATCH_EXPR
  | COMPOUND_EXPR
      op0 -> CATCH_EXPR
      op1 -> catch-seq
modify-stmt:
  MODIFY_EXPR
    op0 -> lhs
    op1 -> rhs
call-stmt: CALL_EXPR
  op0 -> _DECL | '&' _DECL
  op1 -> arglist
arglist:
  NULL_TREE
  | TREE_LIST
      op0 -> val
      op1 -> arglist

varname : compref | _DECL
lhs: varname | '*' _DECL
pseudo-lval: _DECL | '*' _DECL
compref :
  COMPONENT_REF
    op0 -> compref | pseudo-lval
  | ARRAY_REF
    op0 -> compref | pseudo-lval
    op1 -> val

condition : val | val relop val
val : _DECL | CONST

rhs: varname | CONST
   | '*' _DECL
   | '&' varname
   | call_expr
   | unop val
   | val binop val
   | '(' cast ')' val

unop: '+' | '-' | '!' | '~'

binop: relop | '-' | '+' | '/' | '*'
  | '%' | '&' | '|' | '<<' | '>>' | '^'

relop: All tree codes of class '<'
@end smallexample

@node Annotations
@section Annotations
@cindex annotations

The optimizers need to associate attributes with statements and
variables during the optimization process.  For instance, we need to
know what basic block does a statement belong to or whether a variable
has aliases.  All these attributes are stored in data structures
called annotations which are then linked to the field @code{ann} in
@code{struct tree_common}.

Presently, we define annotations for statements (@code{stmt_ann_t}),
variables (@code{var_ann_t}) and SSA names (@code{ssa_name_ann_t}).
Annotations are defined and documented in @file{tree-flow.h}.


@node Statement Operands
@section Statement Operands
@cindex operands
@cindex virtual operands
@cindex real operands
@findex get_stmt_operands
@findex modify_stmt

Almost every GIMPLE statement will contain a reference to a variable
or memory location.  Since statements come in different shapes and
sizes, their operands are going to be located at various spots inside
the statement's tree.  To facilitate access to the statement's
operands, they are organized into arrays associated inside each
statement's annotation.  Each element in an operand array is a pointer
to a @code{VAR_DECL}, @code{PARM_DECL} or @code{SSA_NAME} tree node.
This provides a very convenient way of examining and replacing
operands.  

Data flow analysis and optimization is done on all tree nodes
representing variables.  Any node for which @code{SSA_VAR_P} returns
nonzero is considered when scanning statement operands.  However, not
all @code{SSA_VAR_P} variables are processed in the same way.  For the
purposes of optimization, we need to distinguish between references to
local scalar variables and references to globals, statics, structures,
arrays, aliased variables, etc.  The reason is simple, the compiler
can gather complete data flow information for a local scalar.  On the
other hand, a global variable may be modified by a function call, it
may not be possible to keep track of all the elements of an array or
the fields of a structure, etc.

The operand scanner gathers two kinds of operands: @dfn{real} and
@dfn{virtual}.  An operand for which @code{is_gimple_reg} returns true
is considered real, otherwise it is a virtual operand.  We also
distinguish between uses and definitions.  An operand is used if its
value is loaded by the statement (e.g., the operand at the RHS of an
assignment).  If the statement assigns a new value to the operand, the
operand is considered a definition (e.g., the operand at the LHS of
an assignment).

Virtual and real operands also have very different data flow
properties.  Real operands are unambiguous references to the
full object that they represent.  For instance, given

@smallexample
@{
  int a, b;
  a = b
@}
@end smallexample

Since @code{a} and @code{b} are non-aliased locals, the statement
@code{a = b} will have one real definition and one real use because
variable @code{b} is completely modified with the contents of
variable @code{a}.  Real definition are also known as @dfn{killing
definitions}.  Similarly, the use of @code{a} reads all its bits.

In contrast, virtual operands represent partial or ambiguous
references to a variable.  For instance, given

@smallexample
@{
  int a, b, *p;

  if (...)
    p = &a;
  else
    p = &b;
  *p = 5;
  return *p;
@}
@end smallexample

The assignment @code{*p = 5} may be a definition of @code{a} or
@code{b}.  If we cannot determine statically where @code{p} is
pointing to at the time of the store operation, we create virtual
definitions to mark that statement as a potential definition site for
@code{a} and @code{b}.  Memory loads are similarly marked with virtual
use operands.  Virtual operands are shown in tree dumps right before
the statement that contains them.  To request a tree dump with virtual
operands, use the @option{-vops} option to @option{-fdump-tree}:

@smallexample
@{
  int a, b, *p;

  if (...)
    p = &a;
  else
    p = &b;
  # a = VDEF <a>
  # b = VDEF <b>
  *p = 5;

  # VUSE <a>
  # VUSE <b>
  return *p;
@}
@end smallexample

Notice that @code{VDEF} operands have two copies of the referenced
variable.  This indicates that this is not a killing definition of
that variable.  In this case we refer to it as a @dfn{may definition}
or @dfn{aliased store}.  The presence of the second copy of the
variable in the @code{VDEF} operand will become important when the
function is converted into SSA form.  This will be used to link all
the non-killing definitions to prevent optimizations from making
incorrect assumptions about them.

Operands are collected by @file{tree-ssa-operands.c}.  They are stored
inside each statement's annotation and can be accessed with
@code{DEF_OPS}, @code{USE_OPS}, @code{VDEF_OPS} and @code{VUSE_OPS}.
The following are all the accessor macros available to access USE
operands.  To access all the other operand arrays, just change the
name accordingly:

@defmac USE_OPS (@var{ann})
Returns the array of operands used by the statement with annotation
@var{ann}.
@end defmac

@defmac STMT_USE_OPS (@var{stmt})
Alternate version of USE_OPS that takes the statement @var{stmt} as
input.
@end defmac

@defmac NUM_USES (@var{ops})
Return the number of USE operands in array @var{ops}.
@end defmac

@defmac USE_OP_PTR (@var{ops}, @var{i})
Return a pointer to the @var{i}th operand in array @var{ops}.
@end defmac

@defmac USE_OP (@var{ops}, @var{i})
Return the @var{i}th operand in array @var{ops}.
@end defmac

The following function shows how to print all the operands of a given
statement:

@smallexample
void
print_ops (tree stmt)
@{
  vuse_optype vuses;
  vdef_optype vdefs;
  def_optype defs;
  use_optype uses;
  stmt_ann_t ann;
  size_t i;

  get_stmt_operands (stmt);
  ann = stmt_ann (stmt);
  
  defs = DEF_OPS (ann);
  for (i = 0; i < NUM_DEFS (defs); i++)
    print_generic_expr (stderr, DEF_OP (defs, i), 0);

  uses = USE_OPS (ann);
  for (i = 0; i < NUM_USES (uses); i++)
    print_generic_expr (stderr, USE_OP (uses, i), 0);
  
  vdefs = VDEF_OPS (ann);
  for (i = 0; i < NUM_VDEFS (vdefs); i++)
    print_generic_expr (stderr, VDEF_OP (vdefs, i), 0);
  
  vuses = VUSE_OPS (ann);
  for (i = 0; i < NUM_VUSES (vuses); i++)
    print_generic_expr (stderr, VUSE_OP (vuses, i), 0);
@}
@end smallexample

To collect the operands, you first need to call
@code{get_stmt_operands}.  Since that is a potentially expensive
operation, statements are only scanned if they have been marked
modified by a call to @code{modify_stmt}.  So, if your pass replaces
operands in a statement, make sure to call @code{modify_stmt}.


@node SSA
@section Static Single Assignment
@cindex SSA
@cindex static single assignment

Most of the tree optimizers rely on the data flow information provided
by the Static Single Assignment (SSA) form.  We implement the SSA form
as described in @cite{R. Cytron, J. Ferrante, B. Rosen, M. Wegman, and
K. Zadeck. Efficiently Computing Static Single Assignment Form and the
Control Dependence Graph. ACM Transactions on Programming Languages
and Systems, 13(4):451-490, October 1991}.

The SSA form is based on the premise that program variables are
assigned in exactly one location in the program.  Multiple assignments
to the same variable create new versions of that variable.  Naturally,
actual programs are seldom in SSA form initially because variables
tend to be assigned multiple times.  The compiler modifies the program
representation so that every time a variable is assigned in the code,
a new version of the variable is created.  Different versions of the
same variable are distinguished by subscripting the variable name with
its version number.  Variables used in the right-hand side of
expressions are renamed so that their version number matches that of
the most recent assignment.

We represent variable versions using @code{SSA_NAME} nodes.  The
renaming process in @file{tree-ssa.c} wraps every real and
virtual operand with an @code{SSA_NAME} node which contains
the version number and the statement that created the
@code{SSA_NAME}.  Only definitions and virtual definitions may
create new @code{SSA_NAME} nodes.

Sometimes, flow of control makes it impossible to determine what is the
most recent version of a variable.  In these cases, the compiler
inserts an artificial definition for that variable called
@dfn{PHI function} or @dfn{PHI node}.  This new definition merges
all the incoming versions of the variable to create a new name
for it.  For instance,

@smallexample
if (...)
  a_1 = 5;
else if (...)
  a_2 = 2;
else
  a_3 = 13;

# a_4 = PHI <a_1, a_2, a_3>
return a_4;
@end smallexample

Since it is not possible to determine which of the three branches
will be taken at runtime, we don't know which of @code{a_1},
@code{a_2} or @code{a_3} to use at the return statement.  So, the
SSA renamer creates a new version @code{a_4} which is assigned
the result of ``merging'' @code{a_1}, @code{a_2} and @code{a_3}.
Hence, PHI nodes mean ``one of these operands.  I don't know
which''.

The following macros can be used to examine PHI nodes

@defmac	PHI_RESULT (@var{phi})
Returns the @code{SSA_NAME} created by PHI node @var{phi} (i.e.,
@var{phi}'s LHS).
@end defmac

@defmac	PHI_NUM_ARGS (@var{phi})
Returns the number of arguments in @var{phi}.  This number is exactly
the number of incoming edges to the basic block holding @var{phi}@.
@end defmac

@defmac	PHI_ARG_ELT (@var{phi}, @var{i})
Returns a tuple representing the @var{i}th argument of @var{phi}@.
Each element of this tuple contains an @code{SSA_NAME} @var{var} and
the incoming edge through which @var{var} flows.
@end defmac

@defmac	PHI_ARG_EDGE (@var{phi}, @var{i})
Returns the incoming edge for the @var{i}th argument of @var{phi}.
@end defmac

@defmac	PHI_ARG_DEF (@var{phi}, @var{i})
Returns the @code{SSA_NAME} for the @var{i}th argument of @var{phi}.
@end defmac


@subsection Preserving the SSA form
@findex vars_to_rename
@cindex preserving SSA form
Some optimization passes make changes to the function that
invalidate the SSA property.  This can happen when a pass has
added new variables or changed the program so that variables that
were previously aliased aren't anymore.

Whenever something like this happens, the affected variables must
be renamed into SSA form again.  To do this, you should mark the
new variables in the global bitmap @code{vars_to_rename}.  Once
your pass has finished, the pass manager will invoke the SSA
renamer to put the program into SSA once more.

@subsection Examining @code{SSA_NAME} nodes
@cindex examining SSA_NAMEs

The following macros can be used to examine @code{SSA_NAME} nodes

@defmac SSA_NAME_DEF_STMT (@var{var})
Returns the statement @var{s} that creates the @code{SSA_NAME}
@var{var}.  If @var{s} is an empty statement (i.e., @code{IS_EMPTY_STMT
(@var{s})} returns @code{true}), it means that the first reference to
this variable is a USE or a VUSE@.
@end defmac

@defmac SSA_NAME_VERSION (@var{var})
Returns the version number of the @code{SSA_NAME} object @var{var}.
@end defmac


@subsection Walking use-def chains

@deftypefn {Tree SSA function} void walk_use_def_chains (@var{var}, @var{fn}, @var{data})

Walks use-def chains starting at the @code{SSA_NAME} node @var{var}.
Calls function @var{fn} at each reaching definition found.  Function
@var{FN} takes three arguments: @var{var}, its defining statement
(@var{def_stmt}) and a generic pointer to whatever state information
that @var{fn} may want to maintain (@var{data}).  Function @var{fn} is
able to stop the walk by returning @code{true}, otherwise in order to
continue the walk, @var{fn} should return @code{false}.  

Note, that if @var{def_stmt} is a @code{PHI} node, the semantics are
slightly different.  For each argument @var{arg} of the PHI node, this
function will:

@enumerate
@item	Walk the use-def chains for @var{arg}.
@item	Call @code{FN (@var{arg}, @var{phi}, @var{data})}.
@end enumerate

Note how the first argument to @var{fn} is no longer the original
variable @var{var}, but the PHI argument currently being examined.
If @var{fn} wants to get at @var{var}, it should call
@code{PHI_RESULT} (@var{phi}).
@end deftypefn

@subsection Walking the dominator tree

@deftypefn {Tree SSA function} void walk_dominator_tree (@var{walk_data}, @var{bb})

This function walks the dominator tree for the current CFG calling a
set of callback functions defined in @var{struct dom_walk_data} in
@file{domwalk.h}.  The call back functions you need to define give you
hooks to execute custom code at various points during traversal:

@enumerate
@item Once to initialize any local data needed while processing
      @var{bb} and its children.  This local data is pushed into an
      internal stack which is automatically pushed and popped as the
      walker traverses the dominator tree.

@item Once before traversing all the statements in the @var{bb}.

@item Once for every statement inside @var{bb}.

@item Once after traversing all the statements and before recursing
      into @var{bb}'s dominator children.

@item It then recurses into all the dominator children of @var{bb}.

@item After recursing into all the dominator children of @var{bb} it
      can, optionally, traverse every statement in @var{bb} again
      (i.e., repeating steps 2 and 3).

@item Once after walking the statements in @var{bb} and @var{bb}'s
      dominator children.  At this stage, the block local data stack
      is popped.
@end enumerate
@end deftypefn

@node Alias analysis
@section Alias analysis
@cindex alias
@cindex flow-sensitive alias analysis
@cindex flow-insensitive alias analysis

Alias analysis proceeds in 3 main phases:

@enumerate
@item	Points-to and escape analysis.

This phase walks the use-def chains in the SSA web looking for
three things:

	@itemize @bullet
	@item	Assignments of the form @code{P_i = &VAR}
	@item	Assignments of the form P_i = malloc()
	@item	Pointers and ADDR_EXPR that escape the current function.
	@end itemize

The concept of `escaping' is the same one used in the Java world.
When a pointer or an ADDR_EXPR escapes, it means that it has been
exposed outside of the current function.  So, assignment to
global variables, function arguments and returning a pointer are
all escape sites.

This is where we are currently limited.  Since not everything is
renamed into SSA, we lose track of escape properties when a
pointer is stashed inside a field in a structure, for instance.
In those cases, we are assuming that the pointer does escape.

We use escape analysis to determine whether a variable is
call-clobbered.  Simply put, if an ADDR_EXPR escapes, then the
variable is call-clobbered.  If a pointer P_i escapes, then all
the variables pointed-to by P_i (and its memory tag) also escape.

@item	Compute flow-sensitive aliases

We have two classes of memory tags.  Memory tags associated with
the pointed-to data type of the pointers in the program.  These
tags are called ``type memory tag'' (TMT).  The other class are
those associated with SSA_NAMEs, called ``name memory tag'' (NMT).
The basic idea is that when adding operands for an INDIRECT_REF
*P_i, we will first check whether P_i has a name tag, if it does
we use it, because that will have more precise aliasing
information.  Otherwise, we use the standard type tag.

In this phase, we go through all the pointers we found in
points-to analysis and create alias sets for the name memory tags
associated with each pointer P_i.  If P_i escapes, we mark
call-clobbered the variables it points to and its tag.


@item	Compute flow-insensitive aliases

This pass will compare the alias set of every type memory tag and
every addressable variable found in the program.  Given a type
memory tag TMT and an addressable variable V@.  If the alias sets
of TMT and V conflict (as computed by may_alias_p), then V is
marked as an alias tag and added to the alias set of TMT@.
@end enumerate

For instance, consider the following function:

@example
foo (int i)
@{
  int *p, *q, a, b;

  if (i > 10)
    p = &a;
  else
    q = &b;

  *p = 3;
  *q = 5;
  a = b + 2;
  return *p;
@}
@end example

After aliasing analysis has finished, the type memory tag for
pointer @code{p} will have two aliases, namely variables @code{a} and
@code{b}.
Every time pointer @code{p} is dereferenced, we want to mark the
operation as a potential reference to @code{a} and @code{b}.

@example
foo (int i)
@{
  int *p, a, b;

  if (i_2 > 10)
    p_4 = &a;
  else
    p_6 = &b;
  # p_1 = PHI <p_4(1), p_6(2)>;

  # a_7 = VDEF <a_3>;
  # b_8 = VDEF <b_5>;
  *p_1 = 3;

  # a_9 = VDEF <a_7>
  # VUSE <b_8>
  a_9 = b_8 + 2;

  # VUSE <a_9>;
  # VUSE <b_8>;
  return *p_1;
@}
@end example

In certain cases, the list of may aliases for a pointer may grow
too large.  This may cause an explosion in the number of virtual
operands inserted in the code.  Resulting in increased memory
consumption and compilation time.

When the number of virtual operands needed to represent aliased
loads and stores grows too large (configurable with @option{--param
max-aliased-vops}), alias sets are grouped to avoid severe
compile-time slow downs and memory consumption.  The alias
grouping heuristic proceeds as follows:

@enumerate
@item Sort the list of pointers in decreasing number of contributed
virtual operands.

@item Take the first pointer from the list and reverse the role
of the memory tag and its aliases.  Usually, whenever an
aliased variable Vi is found to alias with a memory tag
T, we add Vi to the may-aliases set for T@.  Meaning that
after alias analysis, we will have:

@smallexample
may-aliases(T) = @{ V1, V2, V3, ..., Vn @}
@end smallexample

This means that every statement that references T, will get
@code{n} virtual operands for each of the Vi tags.  But, when
alias grouping is enabled, we make T an alias tag and add it
to the alias set of all the Vi variables:

@smallexample
may-aliases(V1) = @{ T @}
may-aliases(V2) = @{ T @}
...
may-aliases(Vn) = @{ T @}
@end smallexample

This has two effects: (a) statements referencing T will only get
a single virtual operand, and, (b) all the variables Vi will now
appear to alias each other.  So, we lose alias precision to
improve compile time.  But, in theory, a program with such a high
level of aliasing should not be very optimizable in the first
place.

@item Since variables may be in the alias set of more than one
memory tag, the grouping done in step (2) needs to be extended
to all the memory tags that have a non-empty intersection with
the may-aliases set of tag T@.  For instance, if we originally
had these may-aliases sets:

@smallexample
may-aliases(T) = @{ V1, V2, V3 @}
may-aliases(R) = @{ V2, V4 @}
@end smallexample

In step (2) we would have reverted the aliases for T as:

@smallexample
may-aliases(V1) = @{ T @}
may-aliases(V2) = @{ T @}
may-aliases(V3) = @{ T @}
@end smallexample

But note that now V2 is no longer aliased with R@.  We could
add R to may-aliases(V2), but we are in the process of
grouping aliases to reduce virtual operands so what we do is
add V4 to the grouping to obtain:

@smallexample
may-aliases(V1) = @{ T @}
may-aliases(V2) = @{ T @}
may-aliases(V3) = @{ T @}
may-aliases(V4) = @{ T @}
@end smallexample

@item If the total number of virtual operands due to aliasing is
still above the threshold set by max-alias-vops, go back to (2).
@end enumerate