Data Structures
struct	target_gcse

Variables
struct target_gcse	default_target_gcse
struct target_gcse *	this_target_gcse

Variable Documentation

struct target_gcse default_target_gcse

@verbatim

This file is part of GCC.

GCC is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.

GCC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with GCC; see the file COPYING3. If not see http://www.gnu.org/licenses/.

   TODO
   - reordering of memory allocation and freeing to be more space efficient
   - calc rough register pressure information and use the info to drive all
     kinds of code motion (including code hoisting) in a unified way.

   References searched while implementing this.

   Compilers Principles, Techniques and Tools
   Aho, Sethi, Ullman
   Addison-Wesley, 1988

   Global Optimization by Suppression of Partial Redundancies
   E. Morel, C. Renvoise
   communications of the acm, Vol. 22, Num. 2, Feb. 1979

   A Portable Machine-Independent Global Optimizer - Design and Measurements
   Frederick Chow
   Stanford Ph.D. thesis, Dec. 1983

   A Fast Algorithm for Code Movement Optimization
   D.M. Dhamdhere
   SIGPLAN Notices, Vol. 23, Num. 10, Oct. 1988

   A Solution to a Problem with Morel and Renvoise's
   Global Optimization by Suppression of Partial Redundancies
   K-H Drechsler, M.P. Stadel
   ACM TOPLAS, Vol. 10, Num. 4, Oct. 1988

   Practical Adaptation of the Global Optimization
   Algorithm of Morel and Renvoise
   D.M. Dhamdhere
   ACM TOPLAS, Vol. 13, Num. 2. Apr. 1991

   Efficiently Computing Static Single Assignment Form and the Control
   Dependence Graph
   R. Cytron, J. Ferrante, B.K. Rosen, M.N. Wegman, and F.K. Zadeck
   ACM TOPLAS, Vol. 13, Num. 4, Oct. 1991

   Lazy Code Motion
   J. Knoop, O. Ruthing, B. Steffen
   ACM SIGPLAN Notices Vol. 27, Num. 7, Jul. 1992, '92 Conference on PLDI

   What's In a Region?  Or Computing Control Dependence Regions in Near-Linear
   Time for Reducible Flow Control
   Thomas Ball
   ACM Letters on Programming Languages and Systems,
   Vol. 2, Num. 1-4, Mar-Dec 1993

   An Efficient Representation for Sparse Sets
   Preston Briggs, Linda Torczon
   ACM Letters on Programming Languages and Systems,
   Vol. 2, Num. 1-4, Mar-Dec 1993

   A Variation of Knoop, Ruthing, and Steffen's Lazy Code Motion
   K-H Drechsler, M.P. Stadel
   ACM SIGPLAN Notices, Vol. 28, Num. 5, May 1993

   Partial Dead Code Elimination
   J. Knoop, O. Ruthing, B. Steffen
   ACM SIGPLAN Notices, Vol. 29, Num. 6, Jun. 1994

   Effective Partial Redundancy Elimination
   P. Briggs, K.D. Cooper
   ACM SIGPLAN Notices, Vol. 29, Num. 6, Jun. 1994

   The Program Structure Tree: Computing Control Regions in Linear Time
   R. Johnson, D. Pearson, K. Pingali
   ACM SIGPLAN Notices, Vol. 29, Num. 6, Jun. 1994

   Optimal Code Motion: Theory and Practice
   J. Knoop, O. Ruthing, B. Steffen
   ACM TOPLAS, Vol. 16, Num. 4, Jul. 1994

   The power of assignment motion
   J. Knoop, O. Ruthing, B. Steffen
   ACM SIGPLAN Notices Vol. 30, Num. 6, Jun. 1995, '95 Conference on PLDI

   Global code motion / global value numbering
   C. Click
   ACM SIGPLAN Notices Vol. 30, Num. 6, Jun. 1995, '95 Conference on PLDI

   Value Driven Redundancy Elimination
   L.T. Simpson
   Rice University Ph.D. thesis, Apr. 1996

   Value Numbering
   L.T. Simpson
   Massively Scalar Compiler Project, Rice University, Sep. 1996

   High Performance Compilers for Parallel Computing
   Michael Wolfe
   Addison-Wesley, 1996

   Advanced Compiler Design and Implementation
   Steven Muchnick
   Morgan Kaufmann, 1997

   Building an Optimizing Compiler
   Robert Morgan
   Digital Press, 1998

   People wishing to speed up the code here should read:
     Elimination Algorithms for Data Flow Analysis
     B.G. Ryder, M.C. Paull
     ACM Computing Surveys, Vol. 18, Num. 3, Sep. 1986

     How to Analyze Large Programs Efficiently and Informatively
     D.M. Dhamdhere, B.K. Rosen, F.K. Zadeck
     ACM SIGPLAN Notices Vol. 27, Num. 7, Jul. 1992, '92 Conference on PLDI

   People wishing to do something different can find various possibilities
   in the above papers and elsewhere.

   We support GCSE via Partial Redundancy Elimination.  PRE optimizations
   are a superset of those done by classic GCSE.

   Two passes of copy/constant propagation are done around PRE or hoisting
   because the first one enables more GCSE and the second one helps to clean
   up the copies that PRE and HOIST create.  This is needed more for PRE than
   for HOIST because code hoisting will try to use an existing register
   containing the common subexpression rather than create a new one.  This is
   harder to do for PRE because of the code motion (which HOIST doesn't do).

   Expressions we are interested in GCSE-ing are of the form
   (set (pseudo-reg) (expression)).
   Function want_to_gcse_p says what these are.

   In addition, expressions in REG_EQUAL notes are candidates for GCSE-ing.
   This allows PRE to hoist expressions that are expressed in multiple insns,
   such as complex address calculations (e.g. for PIC code, or loads with a
   high part and a low part).

   PRE handles moving invariant expressions out of loops (by treating them as
   partially redundant).

   **********************

   We used to support multiple passes but there are diminishing returns in
   doing so.  The first pass usually makes 90% of the changes that are doable.
   A second pass can make a few more changes made possible by the first pass.
   Experiments show any further passes don't make enough changes to justify
   the expense.

   A study of spec92 using an unlimited number of passes:
   [1 pass] = 1208 substitutions, [2] = 577, [3] = 202, [4] = 192, [5] = 83,
   [6] = 34, [7] = 17, [8] = 9, [9] = 4, [10] = 4, [11] = 2,
   [12] = 2, [13] = 1, [15] = 1, [16] = 2, [41] = 1

   It was found doing copy propagation between each pass enables further
   substitutions.

   This study was done before expressions in REG_EQUAL notes were added as
   candidate expressions for optimization, and before the GIMPLE optimizers
   were added.  Probably, multiple passes is even less efficient now than
   at the time when the study was conducted.

   PRE is quite expensive in complicated functions because the DFA can take
   a while to converge.  Hence we only perform one pass.

   **********************

   The steps for PRE are:

   1) Build the hash table of expressions we wish to GCSE (expr_hash_table).

   2) Perform the data flow analysis for PRE.

   3) Delete the redundant instructions

   4) Insert the required copies [if any] that make the partially
      redundant instructions fully redundant.

   5) For other reaching expressions, insert an instruction to copy the value
      to a newly created pseudo that will reach the redundant instruction.

   The deletion is done first so that when we do insertions we
   know which pseudo reg to use.

   Various papers have argued that PRE DFA is expensive (O(n^2)) and others
   argue it is not.  The number of iterations for the algorithm to converge
   is typically 2-4 so I don't view it as that expensive (relatively speaking).

   PRE GCSE depends heavily on the second CPROP pass to clean up the copies
   we create.  To make an expression reach the place where it's redundant,
   the result of the expression is copied to a new register, and the redundant
   expression is deleted by replacing it with this new register.  Classic GCSE
   doesn't have this problem as much as it computes the reaching defs of
   each register in each block and thus can try to use an existing
   register.

   GCSE global vars.

struct target_gcse* this_target_gcse

Data Structures

Variables

Variable Documentation