From be9169be1a7ced0480e2dda14cde2e1ead653bca Mon Sep 17 00:00:00 2001 From: "Yount, Chuck" Date: Mon, 8 Apr 2024 11:47:50 -0700 Subject: [PATCH 1/3] Remove GA tuner script. --- src/common/common_utils.cpp | 2 +- utils/bin/yask_tuner.pl | 1530 ------------------------ utils/bin/yask_tuner_summary.csh | 44 - utils/lib/AI/Changes | 29 - utils/lib/AI/Genetic.pm | 799 ------------- utils/lib/AI/Genetic/Defaults.pm | 128 -- utils/lib/AI/Genetic/IndBitVector.pm | 45 - utils/lib/AI/Genetic/IndListVector.pm | 47 - utils/lib/AI/Genetic/IndRangeVector.pm | 50 - utils/lib/AI/Genetic/Individual.pm | 256 ---- utils/lib/AI/Genetic/OpCrossover.pm | 182 --- utils/lib/AI/Genetic/OpMutation.pm | 161 --- utils/lib/AI/Genetic/OpSelection.pm | 266 ---- utils/lib/AI/MANIFEST | 6 - utils/lib/AI/Makefile.PL | 11 - utils/lib/AI/README | 389 ------ utils/lib/AI/test.pl | 17 - 17 files changed, 1 insertion(+), 3961 deletions(-) delete mode 100755 utils/bin/yask_tuner.pl delete mode 100755 utils/bin/yask_tuner_summary.csh delete mode 100644 utils/lib/AI/Changes delete mode 100644 utils/lib/AI/Genetic.pm delete mode 100644 utils/lib/AI/Genetic/Defaults.pm delete mode 100644 utils/lib/AI/Genetic/IndBitVector.pm delete mode 100644 utils/lib/AI/Genetic/IndListVector.pm delete mode 100644 utils/lib/AI/Genetic/IndRangeVector.pm delete mode 100644 utils/lib/AI/Genetic/Individual.pm delete mode 100644 utils/lib/AI/Genetic/OpCrossover.pm delete mode 100644 utils/lib/AI/Genetic/OpMutation.pm delete mode 100644 utils/lib/AI/Genetic/OpSelection.pm delete mode 100644 utils/lib/AI/MANIFEST delete mode 100644 utils/lib/AI/Makefile.PL delete mode 100644 utils/lib/AI/README delete mode 100644 utils/lib/AI/test.pl diff --git a/src/common/common_utils.cpp b/src/common/common_utils.cpp index 3b044ffd..6084941b 100644 --- a/src/common/common_utils.cpp +++ b/src/common/common_utils.cpp @@ -47,7 +47,7 @@ namespace yask { // https://semver.org/. // Format: "major.minor.patch[-alpha|-beta]". - const string version = "4.04.09"; + const string version = "4.05.00"; string yask_get_version_string() { return version; diff --git a/utils/bin/yask_tuner.pl b/utils/bin/yask_tuner.pl deleted file mode 100755 index d3da9452..00000000 --- a/utils/bin/yask_tuner.pl +++ /dev/null @@ -1,1530 +0,0 @@ -#!/usr/bin/env perl - -############################################################################## -## YASK: Yet Another Stencil Kit -## Copyright (c) 2014-2023, Intel Corporation -## -## Permission is hereby granted, free of charge, to any person obtaining a copy -## of this software and associated documentation files (the "Software"), to -## deal in the Software without restriction, including without limitation the -## rights to use, copy, modify, merge, publish, distribute, sublicense, and/or -## sell copies of the Software, and to permit persons to whom the Software is -## furnished to do so, subject to the following conditions: -## -## * The above copyright notice and this permission notice shall be included in -## all copies or substantial portions of the Software. -## -## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -## IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -## FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -## AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -## LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING -## FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS -## IN THE SOFTWARE. -############################################################################## - -# Purpose: Use a Genetic Algorithm to explore workload compile and/or run options. -# Or, sweep through all possible settings if -sweep option is given. - -use strict; -use File::Basename; -use File::Path; -use lib dirname($0)."/lib"; -use lib dirname($0)."/../lib"; -use AI::Genetic; -use English; -use FileHandle; -use Text::ParseWords; -use Sys::Hostname; -use List::Util qw(first max maxstr min minstr reduce shuffle sum); -use YaskUtils; -use POSIX; -use Digest::MD5 qw(md5 md5_hex md5_base64); - -# constants. -my @dirs = qw(x y z); # not including t. - -# command-line options. -my $outDir = 'logs'; # dir for output. -my $stencil; # type of stencil. -my $arch; # target architecture. -my $sweep = 0; # if true, sweep instead of search. -my $testing = 0; # if true, don't run real trials. -my $checking = 0; # if true, don't run at all. -my $host; # set to run on a different host. -my $sde = 0; # run under sde (for testing). -my $sim = 0; # run under any simulator/emulator. -my $radius; # stencil radius. -my $zVec = 0; # Force 1D vectorization in 'z' direction. -my $folding = 1; # 2D & 3D folding allowed. -my $dp; # double precision. -my $makeArgs = ''; # extra make arguments. -my $makePrefix = ''; # prefix for make. -my $makeTimeout = 60 * 10; # max secs for make to run. -my $runArgs = ''; # extra run arguments. -my $maxGB = 32; # max mem usage. -my $minGB = 0; # min mem usage. -my $nranks; # num ranks. -my $debugCheck = 0; # print each initial check result. -my $doBuild = 1; # do compiles. -my $doVal = 0; # do validation runs. -my @folds = (); # folding variations to explore. -my $killCmd = '/usr/bin/timeout'; # command to kill runaway jobs. - -# Set default arch. -$arch = `bash src/kernel/yask.sh -show_arch`; -chomp $arch; - -sub usage { - my $msg = shift; # error message or undef. - - warn "error: $msg\n" if defined $msg; - warn - "usage: $0 [options]\n". - "\nhigh-level control options:\n". - " -val Run validation before each performance test.\n". - " -outDir Directory to write output into (default is '$outDir').\n". - " -check Print the settings, sanity-check the initial population, and exit.\n". - " -test Run search with dummy fitness values.\n". - " -debugCheck Print detailed results of sanity-checks.\n". - " -killCmd= Command to kill runaway jobs (default is '$killCmd').\n". - "\ntarget options:\n". - " -arch= Specify target architecture: avx512, avx3, ... (default is '$arch').\n". - " -host= Run binary on host using ssh.\n". - " -sde Run binary on SDE (for testing only).\n". - " -makePrefix= Prefix make command with .*\n". - " -makeArgs= Pass additional to make command.*\n". - " -makeTimeout= Max secs to allow make to run (default is $makeTimeout).\n". - " -runArgs= Pass additional to bin/yask.sh command.\n". - " -ranks= Number of ranks to use on host (default uses setting in 'yask.sh').\n". - "\nstencil options:\n". - " -stencil= Specify stencil: iso3dfd, awp, etc. (required).\n". - " -dp|-sp Specify FP precision (default is SP).*\n". - " -radius= Specify stencil radius for stencils that use this option (default is 8).*\n". - "\nsearch-space options:\n". - " -noBuild Do not compile kernel; kernel binary must already exist.\n". - " Disables all build-related genes.\n". - " -sweep Use exhausitive search instead of GA.\n". - " -= Force to fixed value .\n". - " Run with -check for list of genes and default ranges.\n". - " Setting local-domain size (l) also sets upper block and mega-block sizes.\n". - " Leave off 'x', 'y', 'z' suffix to set these 3 vars to same val.\n". - " Examples: '-l=512' Set local-domain size to 512^3.\n". - " '-bx=64' Set block size to 64 in 'x' dim.\n". - " '-ep=0' Disable extra padding.\n". - " '-Mb=0' Allow only one Mega-block (Mb=0 => Mb=local-domain size).\n". - " -=- Restrict between and , inclusive.\n". - " Example: '-bx=8-128'.\n". - " See the notes above on specification.\n". - " -=-: Restrict between and with stride .\n". - " Example: '-bx=8-128:4'.\n". - " See the notes above on specification.\n". - " -folds= Comma separated list of folds to use.*\n". - " Examples: '-folds=4 4 1', '-folds=1 1 16, 4 4 1, 1 4 4'.\n". - " Can only specify 3D folds.\n". - " -mem=- Set allowable est. memory usage between and GiB (default is $minGB-$maxGB).\n". - " Ignored if all problem-size vars are set to a fixed value.\n". - " -noPrefetch Disable any prefetching (shortcut for '-pfd_l1=0 -pfd_l2=0').*\n". - " -noFolding Allow only 1D vectorization (in any direction).*\n". - " -zVec Force 1D vectorization in 'z' direction (traditional 'inline' style).*\n". - "\n". - "* indicates options that are invalid if -noBuild is used.\n". - "\n". - "examples:\n". - " $0 -stencil=iso3dfd -l=768 -Mb=0 -noPrefetch\n". - " $0 -stencil=awp -lx=512 -ly=512 -lz=256 -b=4-512:4\n". - " $0 -stencil=3axis -mem=8-10 -noBuild\n"; - - exit(defined $msg ? 1 : 0); -} - -# Restrict gene $geneRanges{key} -# to $geneRanges{key}[0] -# or $geneRanges{key}[0]-$geneRanges{key}[1] -# or $geneRanges{key}[0]-$geneRanges{key}[1] by $geneRanges{key}[2]. -my %geneRanges; -my $autoKey = 'def_'; # prefix for default setting. - -# control tiles. -# TODO: make an option. -my $showTiles = 0; - -# autoflush. -$| = 1; - -# process args. -my $invo = join(' ', map { s/\"/\\\"/g; '"'.$_.'"' } $0, @ARGV); -print "Invocation: $invo\n"; -for my $origOpt (@ARGV) { - my $opt = lc $origOpt; - my ($lhs, $rhs) = split /=/, $origOpt, 2; - - if ($opt eq '-h' || $opt eq '-help') { - usage(); - } - elsif ($opt eq '-test') { - $testing = 1; - } - elsif ($opt eq '-check') { - $checking = 1; - } - elsif ($opt eq '-debugcheck') { - $debugCheck = 1; - } - elsif ($opt =~ /^-?killcmd=(.*)$/i) { - $killCmd = $rhs; - } - elsif ($opt =~ /^-?outdir=(.*)$/i) { - $outDir = $rhs; - } - elsif ($opt =~ /^-?makeargs=(.*)$/i) { - $makeArgs = $rhs; - } - elsif ($opt =~ /^-?makeprefix=(.*)$/i) { - $makePrefix = $rhs; - } - elsif ($opt =~ '^-?maketimeout=(\d+)$') { - $makeTimeout = $rhs; - } - elsif ($opt =~ /^-?runargs=(.*)$/i) { - $runArgs = $rhs; - } - elsif ($opt eq '-sde') { - $sde = 1; - $sim = 1; - } - elsif ($opt =~ '^-?ranks=(\d+)$') { - $nranks = $rhs; - } - elsif ($opt =~ '^-?arch=(\S+)$') { - $arch = $rhs; - } - elsif ($opt =~ /^-?host=(\S+)$/) { - $host = $rhs; - } - elsif ($opt eq '-dp') { - $dp = 1; - } - elsif ($opt eq '-sp') { - $dp = 0; - } - elsif ($opt =~ '^-?mem=([.\d]+)-([.\d]+)$') { - $minGB = $1; - $maxGB = $2; - } - elsif ($opt =~ '^-?radius=(\d+)$') { - $radius = $rhs; - } - elsif ($opt =~ '^-?stencil=(\w+)$') { - $stencil = $rhs; - } - elsif ($opt eq '-noprefetch') { - $geneRanges{$autoKey.'pfd_l1'} = [ 0 ]; - $geneRanges{$autoKey.'pfd_l2'} = [ 0 ]; - } - elsif ($opt eq '-zvec') { - $zVec = 1; - $folding = 0; - } - elsif ($opt eq '-nofolding') { - $folding = 0; - } - elsif ($opt eq '-nobuild') { - $doBuild = 0; - } - elsif ($opt eq '-val') { - $doVal = 1; - } - elsif ($opt eq '-sweep') { - $sweep = 1; - print "Sweeping all values instead of searching with GA.\n"; - } - elsif ($opt =~ '^-?folds=(\s*\d+\s+\d+\s+\d+\s*(,\s*\d+\s+\d+\s+\d+\s*)*)$') { - my $val = $rhs; - $val =~ tr/ //s; - $val =~ s/^\s+|\s+$//g; - $val =~ s/,\s+/,/g; - $val =~ s/\s+,/,/g; - @folds = split(',',$val); - } - - # Assume a gene name if nothing else matches. - elsif ($opt =~ /^-?(\S+)=(\d+)(-(\d+))?(:(\d+))?$/) { - my ($key, $min, $max, $stride) = ($1, $2, $4, $6); - $max = $min if !defined $max; - $stride = 1 if !defined $stride; - $geneRanges{$key} = [ $min, $max, $stride ]; - usage("min value $min for '$key' > max value $max.") - if ($min > $max); - - # special case for local-domain size: also set default for other max sizes. - if ($key =~ /^l[xyz]?$/ && $max > 0) { - my @szs = qw(Mb b mb nb); - push @szs, qw(b_tile mb_tile nb_tile) if $showTiles; - for my $i (@szs) { - my $key2 = $key; - $key2 =~ s/^l/$i/; - $geneRanges{$autoKey.$key2} = [ 1, $max ]; - } - } - } - else { - usage("unrecognized option '$opt' (be sure to use '=' for options that need a value)."); - } -} - -usage("must specify stencil.") if !defined $stencil; -usage("must specify arch.") if !defined $arch; - -# precision. -my $realBytes = $dp ? 8 : 4; - -# vector. -my $vbits = ($arch =~ /^(k|sk)/) ? 512 : 256; -my $velems = $vbits / 8 / $realBytes; - -# radius. -$radius = 8 if !defined $radius; - -# dir name. -my $searchTypeStr = $sweep ? 'sweep' : 'tuner'; -my $hostStr = defined $host ? $host : hostname(); -my $timeStamp=`date +%Y-%m-%d_%H-%M`; -chomp $timeStamp; -my $baseName = "yask_$searchTypeStr.$stencil.$arch.$hostStr.${timeStamp}_p$$"; -$outDir = '.' if !$outDir; -$outDir .= "/$baseName"; -print "Output will be saved in '$outDir'.\n"; - -# open output. -mkpath($outDir,1); -if (!$checking) { - my $outTxtFile = "$outDir/$baseName.txt"; - my $outTFH = new FileHandle; - $outTFH->open(">$outTxtFile") or die "error: cannot write to '$outTxtFile'\n"; - print $outTFH "$invo\n"; - $outTFH->close(); -} - -my $outFile = "$outDir/$baseName.csv"; -my $outFH = new FileHandle; -$outFH->open(">$outFile") or die "error: cannot write to '$outFile'\n" - unless $checking; - -# things to get from the run. -if ($showTiles) { - push @YaskUtils::log_keys, - 'block-tile-size', - 'micro-block-tile-size'; - 'nano-block-tile-size'; -} - -# how many individuals to create randomly and then keep at any given time. -my $popSize = 200; - -# experiment ends when any one of the following is reached. -my $numGens = 1000; # stop when this many generations are run. Set to 1 for purely random search. -my $maxOkRuns = 10000; # stop when this many individuals are successfully run. -my $maxFlatGens = 4; # stop when this many generations do not improve the fitness. - -my $secPerTrial = 60; # used to estimate time required. - -# prefetching. -my $maxPfd_l1 = 4; -my $maxPfd_l2 = 8; - -# dimension-related vars. -my $minDim = 128; # min dimension on any axis. -my $maxDim = 2 * $YaskUtils::oneKi; # max dimension on any axis. -my $maxPad = 3; -my $maxTimeBlock = 10; # max temporal blocking. -my $minPoints; -my $maxPoints; -my $minVecsInBlock = 10; -my $minBlocksInMegaBlock = 10; - -# Threads. -my $minOuterThreads = 8; -my $maxOuterThreads = 64; -my $minInnerThreads = 1; -my $maxInnerThreads = 4; - -# List of possible loop orders. -my @loopOrders = - ('123', '132', '213', '231', '312', '321'); - -# Possible space-filling curve modifiers. -my @pathNamesIncreasing = (''); -push @pathNamesIncreasing, 'tiled' if $showTiles; -my @pathNames = - (@pathNamesIncreasing, 'serpentine', 'square_wave serpentine', 'square_wave'); - -# List of folds. -if ( !@folds ) { - - # start with inline in z only. - @folds = "1 1 $velems"; - - # add more 1D options if not z-vec. - push @folds, ("1 $velems 1", "$velems 1 1") if !$zVec; - - # add remaining options if folding. - push @folds, ($velems == 8) ? - ("4 2 1", "4 1 2", - "2 4 1", "2 1 4", - "1 4 2", "1 2 4") : - ($velems == 16) ? - ("8 2 1", "8 1 2", - "4 4 1", "4 2 2", "4 1 4", - "2 8 1", "2 4 2", "2 2 4", "2 1 8", - "1 8 2", "1 4 4", "1 2 8") : - # velems == 4 - ("2 2 1", "2 1 2", "1 2 2") - if $folding; -} - -# Data structure to describe each gene in the genome. -# 2-D array. Each outer array element contains the following elements: -# 0. min allowed value; '0' is a special case handled by YASK. -# 1. max allowed value. -# 2. step size between values. -# 3. name. -my @rangesAll = - ( - # rank size. - [ $minDim, $maxDim, 16, 'lx' ], - [ $minDim, $maxDim, 16, 'ly' ], - [ $minDim, $maxDim, 16, 'lz' ], - - # mega-block size. - [ 1, $maxTimeBlock, 1, 'Mbt' ], - [ 0, $maxDim, 4, 'Mbx' ], - [ 0, $maxDim, 4, 'Mby' ], - [ 0, $maxDim, 4, 'Mbz' ], - - # block size. - [ 1, $maxTimeBlock, 1, 'bt' ], - [ 0, $maxDim, 4, 'bx' ], - [ 0, $maxDim, 4, 'by' ], - [ 0, $maxDim, 4, 'bz' ], - - # micro-block size. - [ 0, $maxDim, 4, 'mbx' ], - [ 0, $maxDim, 4, 'mby' ], - [ 0, $maxDim, 4, 'mbz' ], - - # nano-block size. - [ 0, $maxDim, 4, 'nbx' ], - [ 0, $maxDim, 4, 'nby' ], - [ 0, $maxDim, 4, 'nbz' ], - - # extra padding. - [ 0, $maxPad, 1, 'epx' ], - [ 0, $maxPad, 1, 'epy' ], - [ 0, $maxPad, 1, 'epz' ], - - # threads. - [ $minOuterThreads, $maxOuterThreads, 1, 'outer_threads' ], - [ $minInnerThreads, $maxInnerThreads, 1, 'inner_threads' ], - [ 0, 1, 1, 'bind_inner_threads' ], - ); - -if ($showTiles) { - push @rangesAll, - ( - # block-tile size. - [ 0, $maxDim, 4, 'b_tilex' ], - [ 0, $maxDim, 4, 'b_tiley' ], - [ 0, $maxDim, 4, 'b_tilez' ], - - # micro-block-tile size. - [ 0, $maxDim, 4, 'mb_tilex' ], - [ 0, $maxDim, 4, 'mb_tiley' ], - [ 0, $maxDim, 4, 'mb_tilez' ], - - # nano-block-tile size. - [ 0, $maxDim, 4, 'sb_tilex' ], - [ 0, $maxDim, 4, 'sb_tiley' ], - [ 0, $maxDim, 4, 'sb_tilez' ], - ); -} - -# Add compiler genes. -if ($doBuild) { - push @rangesAll, - ( - - # Loops, from the list above. - # Each loop consists of index order and path mods. - # Block and rank paths require increasing indices. - [ 0, $#loopOrders, 1, 'nanoBlockOrder' ], - [ 0, $#pathNames, 1, 'nanoBlockPath' ], - [ 0, $#loopOrders, 1, 'microBlockOrder' ], - [ 0, $#pathNames, 1, 'microBlockPath' ], - [ 0, $#loopOrders, 1, 'blockOrder' ], - [ 0, $#pathNamesIncreasing, 1, 'blockPath' ], - [ 0, $#loopOrders, 1, 'MegaBlockOrder' ], - [ 0, $#pathNames, 1, 'MegaBlockPath' ], - [ 0, $#loopOrders, 1, 'rankOrder' ], - [ 0, $#pathNamesIncreasing, 1, 'rankPath' ], - - # how to shape vectors, from the list above. - [ 0, $#folds, 1, 'fold' ], - - # prefetch distances for l1 and l2. - # all non-pos numbers => no prefetching, so ~50% chance of being enabled. - [ -$maxPfd_l1, $maxPfd_l1, 1, 'pfd_l1' ], - [ -$maxPfd_l2, $maxPfd_l2, 1, 'pfd_l2' ], - - ); -} - -# indices for each element of @rangesAll. -my $minI = 0; -my $maxI = 1; -my $stepI = 2; -my $nameI = 3; -my $numIs = 4; - -# Genes w/only one value. -# These will be removed from the GA search. -my %fixedVals; - -# Fill in default values in dynamic gene ranges. -for my $key (keys %geneRanges) { - - # cleanup. - my ($min, $max, $step) = @{$geneRanges{$key}}; - $max = $min if !defined $max; - $step = 1 if !defined $step; - $geneRanges{$key} = [ $min, $max, $step ]; -} - -# Apply dynamic gene ranges and make some checks on the final ranges. -my %usedGeneRanges; -for my $i (0..$#rangesAll) { - my $r = $rangesAll[$i]; # ref to array. - my $key = $r->[$nameI]; - - # Look for match in dynamic limits. - # Try full-match of key, then w/o x,y,z suffix. - # Try each w/cmd-line specified key, then w/auto-gen key. - # E.g., for 'lx', try the following: 'lx', 'l', 'auto_lx', 'auto_l'. - my $rkey = lc $key; - my $rkey2 = $rkey; - $rkey2 =~ s/[xyz]$//; # e.g., 'lx' -> 'l'. - for my $rk ($rkey, $rkey2, $autoKey.$rkey, $autoKey.$rkey2) { - if (exists $geneRanges{$rk}) { - my ($min, $max, $step) = @{$geneRanges{$rk}}; - $r->[$minI] = $min; - $r->[$maxI] = $max; - $r->[$stepI] = $step; - last; - } - } - - # Mark all forms as used. - $usedGeneRanges{$rkey} = 1; - $usedGeneRanges{$rkey2} = 1; - $usedGeneRanges{$autoKey.$rkey} = 1; - $usedGeneRanges{$autoKey.$rkey2} = 1; - - # Check range. - die "internal error: not $numIs elements in ranges[$key]\n" - unless scalar @{$r} == $numIs; - die "error: key: max value $r->[$maxI] of '$key' < min value $r->[$minI]\n" - if $r->[$minI] > $r->[$maxI]; - - # Add to fixed-vals if only one choice. - if ($r->[$minI] == $r->[$maxI]) { - $fixedVals{$key} = $r->[$minI]; - print "Gene '$key' set to $fixedVals{$key}.\n"; - } -} - -# disable memory range if all domain sizes fixed. -if ((scalar grep { exists $fixedVals{"l$_"} } @dirs) == scalar @dirs) { - undef $minGB; - undef $maxGB; -} - -# check that all ranges were used. -for my $key (keys %geneRanges) { - die "error: gene '$key' not recognized.\n" - unless exists $usedGeneRanges{$key}; -} - -# remove fixed values from search. -my @ranges = grep { my $key = $_->[$nameI]; - !exists $fixedVals{$key} } @rangesAll; - -# keep track of GA progress. -my $run = 0; -my $okRuns = 0; -my $bestFit = 0; -my $bestGen = 0; - -sub init() { - $run = 0; - $okRuns = 0; - $bestFit = 0; - $bestGen = 0; -} - -# convert a number n so that values in [a0..a1] are mapped to [b0..b1], -# values < a0 => b0. -# values > a1 => b1. -sub adjRange($$$$$) { - my ($n, $a0, $a1, $b0, $b1) = @_; - - # outside input range? - if ($n <= $a0) { - return $b0; - } elsif ($n >= $a1) { - return $b1; - } - - # value ranges. - my $ar = $a1 - $a0; - my $br = $b1 - $b0; - - $n -= $a0; # 0..ar. - $n /= $ar; # 0..1 - $n *= $br; # 0..br. - $n += $b0; # b0..b1 - - # convert to int and double-check range. - $n = int($n); - $n = $b0 if $n < $b0; - $n = $b1 if $n > $b1; - - return $n; -} - -# print the vars. -sub printValues($) { - my $values = shift; - for my $i (0..$#$values) { - print " $ranges[$i][$nameI] = $values->[$i]\n"; - } -} - -# make a hash to access genes by name. -# input: ref to list of genes. -# output: ref to hash of genes by name. -sub makeHash($) { - my $values = shift; - - my %h; - for my $i (0..$#ranges) { - $h{$ranges[$i][$nameI]} = $values->[$i]; - } - - return \%h; -} - -# Get value in this preference order: -# 1. hash parameter. -# 2. fixed values from cmd line. -sub readHash($$$) { - my $hash = shift; - my $key = shift; - my $isBuildVar = shift; - - # try hash. - my $val = $hash->{$key}; - return $val if defined $val; - - # try fixed cmd-line vals. - $val = $fixedVals{$key}; - return $val if defined $val; - - # return dummy value for build var if disabled. - return 1 if (!$doBuild && $isBuildVar); - - die "internal error: value for gene '$key' not provided.\n"; -} - -# Call readHash across 3 directions and return array. -sub readHashes($$$) { - my $hash = shift; - my $key = shift; - my $isBuildVar = shift; - - my @vals; - for my $d (@dirs) { - if ($key =~ /bg$/ && !$showTiles) { - push @vals, 1; - } else { - push @vals, readHash($hash, "$key$d", $isBuildVar); - } - } - return @vals; -} - -# multiply args. -sub mult { - - my $n = 1; - map { $n *= $_ } @_; - return $n; -} - -sub roundUp($$) { - my $n = shift; - my $mult = shift; - return int(ceil($n / $mult) * $mult); -} - -# round the first arg to a multiple of the second arg. -# make sure result is between third and forth arg. -sub roundToMult($$$$) { - my $v = shift; - my $m = shift; - my $min = shift; - my $max = shift; - - my $val = int(($v + $m/2) / $m) * $m; - $val = $min if $val < $min; - $val = $max if $val > $max; - - return $val; -} - -# round all values to those given in ranges. -sub roundValuesToMult($) { - my $values = shift; # ref to array. - - for my $i (0..$#ranges) { - $values->[$i] = roundToMult($values->[$i], $ranges[$i][$stepI], - $ranges[$i][$minI], $ranges[$i][$maxI]); - } -} - -# create the basic make command and stencil tag. -sub getMakeCmd($$) { - my $macros = shift; - my $margs = shift; - - my $tag = $stencil; - my $makeCmd = "echo 'build disabled'"; - - if ($doBuild) { - my $tagPrefix = $tag."_p".$$."_"; - $tag = $tagPrefix.md5_hex($macros, $margs, $makeArgs, $realBytes, $radius); - - # Remove binaries that haven't been used in a while. - system "find bin lib -name '*$tagPrefix*' -amin +15 | xargs --no-run-if-empty rm"; - - # Already exists? - if (-x "bin/yask_kernel.$tag.$arch.exe" && - -x "lib/libyask_kernel.$tag.$arch.so") { - $makeCmd = "echo 'binary & library exist'"; - } - else { - $makeCmd = - "$makePrefix make -j EXTRA_MACROS='$macros' YK_STENCIL=$tag ". - "stencil=$stencil arch=$arch real_bytes=$realBytes radius=$radius $margs $makeArgs"; - $makeCmd = "$makeCmd default; $makeCmd clean"; - } - } - - return ( $makeCmd, $tag ); -} - -# create the basic run command. -my $timeCmd = 'time'; -sub getRunCmd($) { - my $tag = shift; - - my $exePrefix = $timeCmd; - $exePrefix .= " sde -$arch --" if $sde; - - my $runCmd = "bin/yask.sh -log_dir $outDir/logs -log yask.$stencil.$arch.run".sprintf("%06d",$run).".log"; - $runCmd .= " -host $host" if defined $host; - $runCmd .= " -exe_prefix '$exePrefix' -stencil $tag -arch $arch -no-pre_auto_tune -no-print_suffixes"; - $runCmd .= " -ranks $nranks" if defined $nranks; - return $runCmd; -} - -# return estimate of mem footprint in bytes. -my $numSpatialVars = 0; -sub calcSize($$$) { - my $sizes = shift; # ref to size array. - my $pads = shift; # ref to pad array. - my $mults = shift; # ref to array of multiples. - - # need to determine how many XYZ vars will be allocated for this stencil. - # TODO: get info from compiler report. - if (!$numSpatialVars) { - - my ( $makeCmd, $tag ) = getMakeCmd('', 'EXTRA_CXXFLAGS=-O1'); - my $runCmd = getRunCmd($tag)." -t 0 -l 32 $runArgs"; - my $cmd = "$makeCmd 2>&1 && $runCmd"; - - my $timeDim = 0; - my $numVars = 0; - my $numUpdatedVars = 0; - my @cmdOut; - if ($testing) { - $numSpatialVars = 1; - } else { - print "Running '$cmd' to determine number of vars...\n"; - open CMD, "$cmd 2>&1 |" or die "error: cannot run '$cmd'\n"; - while () { - chomp; - my $line = $_; - push @cmdOut, $line; - - # E.g., - # 'A' 4-D var (t=2 * x=8 * y=48 * z=49) with storage at 0x2aba63016000 ... - my $nvars = 1; - if (/\d-?D .*x=.*y=.*z=/) { - for my $w (split ' ',$line) { - if ($w =~ /(\w+)=(\d+)/) { - my ($dim, $sz) = ($1, $2); - if ($dim eq 't') { - $nvars *= $sz; - } - } - } - $numSpatialVars += $nvars; - print "$line => $nvars XYZ vars\n"; - } - } - close CMD; - } - if (!$numSpatialVars) { - map { print ">> $_\n"; } @cmdOut; - die "error: no relevant var allocations found in '$cmd'; $0 only works with 'x, y, z' 3-D stencils.\n"; - } - print "Determined that $numSpatialVars XYZ vars are allocated.\n"; - } - - # estimate each dim of allocated memory as size + 2 * (halo + pad). - my @sizes = map { roundUp($sizes->[$_], $mults->[$_]) + - 2 * roundUp($radius + $pads->[$_], $mults->[$_]) } 0..$#dirs; - - my $n = mult(@sizes); # mult sizes plus padding & halos. - my $nb = $n * $realBytes; - - # mult by number of vars. - $nb *= $numSpatialVars; - - return $nb; -} - -# calculate fitness from results hash. -sub results2fitness($) { - my $results = shift; - my $fitness = 0; - - my $fitnessMetric = $YaskUtils::log_keys[0]; # first one is fitness. - - # simple lookup. - if (defined $results->{$fitnessMetric}) { - $fitness = $results->{$fitnessMetric}; - } else { - print "WARNING: not fitness result found.\n"; - } - - # any adjustments can go here. - - print "fitness = $fitness\n"; - return $fitness; -} - -# check success from one line of output. -sub setPassed($$) { - my $passed = shift; # ref to var. - my $line = shift; # 1 line of output. - - $$passed = 1 if (/TEST PASSED/); -} - - -# hash of previous results, keyed by command to run. -my %resultsCache; -my %fitnessCache; -my %testCache; - -# run the command and return fitness and various associated data in a hash. -sub evalIndiv($$$$$$) { - my $makeCmd = shift; - my $testCmd = shift; - my $simCmd = shift; - my $shortRunCmd = shift; - my $longRunCmd = shift; - my $pts = shift; - - # check result cache. - my $rkey = "$makeCmd; $longRunCmd"; - my $fitness = 0; - my $results = $resultsCache{$rkey}; - if (defined $results) { - print "using cached results for '$rkey'.\n"; - $fitness = $fitnessCache{$rkey}; - return $fitness, $results; - } - $results = {}; - - # check test cache. - my $tkey = "$makeCmd; $testCmd"; - my $passed = 0; - if ($doVal && $testCmd) { - my $testRes = $testCache{$tkey}; - if (defined $testRes) { - print "using cached test for '$tkey'.\n"; - if (!$testRes) { - print "aborting because previous test failed.\n"; - return $fitness, $results; - } - $passed = 1; # indicate already passed. - } - } else { - $passed = 1; # no test to run. - } - - # just make a nonsense fitness value if testing script. - if ($testing) { - $fitness = md5($simCmd); - $passed = 1; - } else { - my $made = 0; - - # do several runs: - # 0=test - # if not sim, - # 1=short - # 2=long - # else - # 1=sim - for my $N (0..2) { - - # already tested? - next if $N == 0 && $passed; - - # which run? - my $cmd = ($N == 0) ? $testCmd : - $sim ? $simCmd : - ($N == 1) ? $shortRunCmd : - $longRunCmd; - - # need to make? - if (!$made) { - $cmd = "$makeCmd 2>&1 && $cmd"; - $made = 1; - } - - my $secs; - print "running '$cmd' ...\n"; - open CMD, "$cmd 2>&1 |" or die "error: cannot run '$cmd'\n"; - while () { - print ">> $_"; - - # test run. - if ($N == 0) { - setPassed(\$passed, $_); - } - - # perf run. - else { - - # collect run-time from short run only. - # try several different formats from 'time' prefix. - if ($N == 1) { - - # 0.01user 0.02system 1:19.93elapsed 0%CPU (0avgtext+0avgdata 14784maxresident)k - if (/(\d+):([.\d]+)elapsed/) { - $secs = $1 * 60 + $2; - print "elapsed time is $secs secs.\n"; - } - - # 0.000u 0.024s 0:00.03 66.6% 0+0k 0+0io 0pf+0w - elsif (/\ds\s+(\d+):([.\d]+)/) { - $secs = $1 * 60 + $2; - print "elapsed time is $secs secs.\n"; - } - - # real 0m9.905s - elsif (/^real\s+(\d+)m([.\d]+)s/) { - $secs = $1 * 60 + $2; - print "elapsed time is $secs secs.\n"; - } - } - - # look for expected metrics in output. - YaskUtils::getResultsFromLine($results, $_); - } - } - close CMD; - - # calc fitness from results. - $fitness = results2fitness($results); - - # bail if not passed. - if (!$passed) { - print "stopping because test did not pass\n"; - last; - } - - # checks for short run. - if ($N == 1) { - - # bail for simulation. - last if $sim; - - # bail if fitness not close to best. - if ($fitness < $bestFit * 0.9) { - print "stopping after short run due to non-promising fitness\n"; - last; - } else { - print "short run looks promising; continuing with long run...\n"; - } - } - } # N - } - - $testCache{$tkey} = $passed; - $resultsCache{$rkey} = $results; - $fitnessCache{$rkey} = $fitness; - return $fitness, $results; -} - -# return loop-ctrl vars. -sub makeLoopVars($$$$) { - my $h = shift; - my $makePrefix = shift; # e.g., 'BLOCK'. - my $tunerPrefix = shift; # e.g., 'block'. - my $lastDim = shift; # e.g., 2 or 3. - - my $order = readHash($h, $tunerPrefix."Order", 1); - my $orderStr = $loopOrders[$order]; # e.g., '231'. - - # dimension vars. - my @dims = split '',$orderStr; # e.g., ('2', '3', '1). - @dims = grep { $_ <= $lastDim } @dims; # e.g., ('2', '1). - - # vars to create. - my $order = join(',', @dims); # e.g., '2, 1'. - my $outerMods = ''; - - # path gene? - my $pathKey = $tunerPrefix."Path"; - if (exists $h->{$pathKey}) { - my $path = readHash($h, $pathKey, 1); - my $pathStr = @pathNames[$path]; # e.g., 'tiled'. - $outerMods = $pathStr; - } - - my $loopVars = " ".$makePrefix."_LOOP_ORDER='$order'"; - $loopVars .= " ".$makePrefix."_LOOP_MODS='$outerMods'"; - return $loopVars; -} - -# sanity-check vars. -my $numChecks = 0; -my %checkStats; -my %checkNums; -my %checkSums; -my %checkMins; -my %checkMaxs; -my $justChecking = 1; # set to 0 to do actual run. -sub addStat($$$) { - my ($ok, $name, $val) = @_; - $name = $ok ? "check-passed $name" : "check-failed $name"; - $checkNums{$name}++; - $checkSums{$name} += $val; - $checkMins{$name} = $val if !defined $checkMins{$name} || $val < $checkMins{$name}; - $checkMaxs{$name} = $val if !defined $checkMaxs{$name} || $val > $checkMaxs{$name}; -} - -sub printCheckStats { - print "Sanity-check stats:\n"; - for my $k (sort keys %checkStats) { - print " $k: $checkStats{$k}\n"; - } - print " total checked: $numChecks\n"; - if (exists $checkStats{'ok'}) { - my $n = $checkStats{'ok'}; - print " fraction ok: ".($n / $numChecks)."\n"; - for my $k (sort keys %checkSums) { - my $ave = $checkNums{$k} ? $checkSums{$k} / $checkNums{$k} : 0; - print " $k: num=$checkNums{$k}, min=$checkMins{$k}, ave=$ave, max=$checkMaxs{$k}\n"; - } - } -} - -# adjust inner sizes to fit into outer sizes. -sub adjSizes($$) { - my $is = shift; # ref to inner sizes. - my $os = shift; # ref to outer sizes. - - # adjust each dim. - map { - - # If outer size is zero, do nothing. - if ($os->[$_] == 0) { - } - - # If size is zero, set to max of outer. - elsif ($is->[$_] == 0) { - $is->[$_] = $os->[$_]; - } - - # Wrap around. - # TODO: change from abrupt wrap-around function to - # one w/o discontinuities, e.g., /\/\/\/\ instead of /|/|/|/|/|. - else { - $is->[$_] = (($is->[$_] - 1) % $os->[$_]) + 1; - - # Bump up to outer size if close. This is a heuristic to avoid tiny - # remainders. It also gives a higher probability of exactly matching - # the outer size. - if ($is->[$_] > $os->[$_] * 0.9) { - $is->[$_] = $os->[$_]; - } - } - - } 0..$#dirs; -} - -# if just checking, return 0 or 1. -# else, return the goodness of an individual; higher is better. -# fitness values should be > 0. -sub fitness { - my $values = shift; # ref to array. - roundValuesToMult($values); - - my $ok = 1; # set to 0 if found to be bad. - - my $gen = int($run / $popSize) + 1; - my $indiv = ($run % $popSize) + 1; - if (!$justChecking) { - $run++; - print "======= run $run: individual $indiv in generation $gen =======\n"; - } - if ($debugCheck || !$justChecking) { - print "Settings to be evaluated:\n"; - printValues($values); - } - - # get individual vars from hash or fixed values. - my $h = makeHash($values); - my @ds = readHashes($h, 'l', 0); - my $Mbt = readHash($h, 'Mbt', 1); - my @Mbs = readHashes($h, 'Mb', 0); - my $bt = readHash($h, 'bt', 1); - my @bs = readHashes($h, 'b', 0); - my @mbs = readHashes($h, 'mb', 0); - my @nbs = readHashes($h, 'nb', 0); - my (@b_tiles, @mb_tiles, @nb_tiles); - if ($showTiles) { - @b_tiles = readHashes($h, 'b_tile', 0); - @mb_tiles = readHashes($h, 'mb_tile', 0); - @nb_tiles = readHashes($h, 'nb_tile', 0); - } - my @cvs = readHashes($h, 'c', 1); # in vectors, not in points! - my @ps = readHashes($h, 'ep', 0); - my $fold = readHash($h, 'fold', 1); - my $outer_threads = readHash($h, 'outer_threads', 0); - my $inner_threads = readHash($h, 'inner_threads', 0); - my $bind_inner_threads = readHash($h, 'bind_inner_threads', 0); - my $pfd_l1 = readHash($h, 'pfd_l1', 1); - my $pfd_l2 = readHash($h, 'pfd_l2', 1); - - # fold numbers. - my $foldNums = $folds[$fold]; - my @fs = split ' ', $foldNums; - - # adjust inner sizes to fit in their enclosing sizes. - adjSizes(\@Mbs, \@ds); # Mega-block <= domain. - adjSizes(\@bs, \@Mbs); # block <= Mega-block. - adjSizes(\@mbs, \@bs); # micro-block <= block. - adjSizes(\@nbs, \@mbs); # nano-block <= micro-block. - if ($showTiles) { - adjSizes(\@b_tiles, \@bs); # block-tile <= block. - adjSizes(\@mb_tiles, \@mbs); # micro-block-tile <= micro-block. - adjSizes(\@nb_tiles, \@nbs); # nano-block-tile <= nano-block. - } - - # 3d sizes in points. - my $dPts = mult(@ds); - my $MbPts = mult(@Mbs); - my $bPts = mult(@bs); - my $mbPts = mult(@mbs); - my $nbPts = mult(@nbs); - my $fPts = mult(@fs); - my ($b_tilePts, $mb_tilePts, $nb_tilePts); - if ($showTiles) { - $b_tilePts = mult(@b_tiles); - $mb_tilePts = mult(@mb_tiles); - $nb_tilePts = mult(@nb_tiles); - } - - # Vecs per block. - my @bvs = map { ceil($bs[$_] / $fs[$_]) } 0..$#dirs; - my $bvs = mult(@bvs); - - # Micro-blocks per block. - my @bmbs = map { ceil($bs[$_] / $mbs[$_]) } 0..$#dirs; - my $bmbs = mult(@bmbs); - - # Blocks per Mega-block. - my @Mbbs = map { ceil($Mbs[$_] / $bs[$_]) } 0..$#dirs; - my $Mbbs = mult(@Mbbs); - - # Mega-blocks per rank. - my @dMbs = map { ceil($ds[$_] / $Mbs[$_]) } 0..$#dirs; - my $dMbs = mult(@dMbs); - - # mem usage estimate. - my $overallSize = calcSize(\@ds, \@ps, \@fs); - - if ($debugCheck) { - print "Sizes:\n"; - print " local-domain size = $dPts\n"; - print " Mega-block size = $MbPts\n"; - print " block size = $bPts\n"; - print " nano-block size = $nbPts\n"; - print " fold size = $fPts\n"; - print " Mega-blocks per local-domain = $dMbs\n"; - print " blocks per Mega-block = $Mbbs\n"; - print " vecs per block = $bvs\n"; - print " micro-blocks per block = $bmbs\n"; - print " mem estimate = ".($overallSize/$YaskUtils::oneGi)." GB\n"; - if ($showTiles) { - print " block-tile size = $b_tilePts\n"; - print " micro-block-tile size = $mb_tilePts\n"; - print " nano-block-tile size = $nb_tilePts\n"; - } - } - - # check overall size. - if (defined $minGB && $overallSize / $YaskUtils::oneGi < $minGB) { - print " overall size of $overallSize bytes < $minGB GiB\n" if $debugCheck; - $checkStats{'mem too low'}++; - $ok = 0; - } elsif (defined $maxGB && $overallSize / $YaskUtils::oneGi > $maxGB) { - print " overall size of $overallSize bytes > $maxGB GiB\n" if $debugCheck; - $checkStats{'mem too high'}++; - $ok = 0; - } - - # check points. - if (defined $minPoints && $dPts < $minPoints) { - print " $dPts points is too low\n" if $debugCheck; - $checkStats{'num points too low'}++; - $ok = 0; - } elsif (defined $maxPoints && $dPts > $maxPoints) { - print " $dPts points is too high\n" if $debugCheck; - $checkStats{'num points too high'}++; - $ok = 0; - } - - # Each block should do minimal work. - if ($bvs < $minVecsInBlock) { - print " $bvs vecs per block < $minVecsInBlock\n" if $debugCheck; - $checkStats{'block size too small'}++; - $ok = 0; - } - - # Should be min number of blocks. - if ($Mbbs < $minBlocksInMegaBlock) { - print " $Mbbs blocks per Mega-block < $minBlocksInMegaBlock\n" if $debugCheck; - $checkStats{'too few blocks per Mega-block'}++; - $ok = 0; - } - - # all sanity checks done. - print "Sanity check passed\n" if $ok && $debugCheck; - $numChecks++; - $checkStats{'ok'} += $ok; - addStat($ok, 'mem estimate', $overallSize); - addStat($ok, 'local-domain size', $dPts); - addStat($ok, 'Mega-block size', $MbPts); - addStat($ok, 'block size', $bPts); - addStat($ok, 'micro-block size', $mbPts); - addStat($ok, 'nano-block size', $nbPts); - addStat($ok, 'vector size', $fPts); - addStat($ok, 'Mega-blocks per local-domain', $dMbs); - addStat($ok, 'blocks per Mega-block', $Mbbs); - addStat($ok, 'vectors per block', $bvs); - addStat($ok, 'micro-blocks per block', $bmbs); - if ($showTiles) { - addStat($ok, 'block-tile size', $b_tilePts); - addStat($ok, 'micro-block-tile size', $mb_tilePts); - addStat($ok, 'nano-block-tile size', $nb_tilePts); - } - - # exit here if just checking. - return $ok if $justChecking; - - # compile-time settings. - my $macros = ''; # string of macros. - my $mvars = ''; # other make vars. - - # prefetch distances. - $pfd_l1 = 0 if $pfd_l1 < 0; - $pfd_l2 = 0 if $pfd_l2 < 0; - if ($pfd_l1 > 0 && $pfd_l2 > 0) { - - # make sure pfld2 > pfld1. - $pfd_l2 = $pfd_l1 + 1 if $pfd_l1 >= $pfd_l2; - } - $mvars .= " pfd_l1=$pfd_l1 pfd_l2=$pfd_l2"; - - # fold. - $mvars .= " fold=x=$fs[0],y=$fs[1],z=$fs[2]"; - - # gen-loops vars. - $mvars .= makeLoopVars($h, 'RANK', 'rank', 3); - $mvars .= makeLoopVars($h, 'MEGA_BLOCK', 'MegaBlock', 3); - $mvars .= makeLoopVars($h, 'BLOCK', 'block', 3); - $mvars .= makeLoopVars($h, 'MICRO_BLOCK', 'microBlock', 3); - $mvars .= makeLoopVars($h, 'NANO_BLOCK', 'nanoBlock', 3); - - # how to make. - my ( $makeCmd, $tag ) = getMakeCmd($macros, $mvars); - - # how to run. - my $runCmd = getRunCmd($tag); # shell command plus any initial args. - my $args = ""; # exe args. - $args .= " -outer_threads $outer_threads -inner_threads $inner_threads -max_threads ".($outer_threads * $inner_threads); - $args .= ($bind_inner_threads ? " " : " -no"). "-bind_inner_threads"; - - # sizes. - $args .= " -lx $ds[0] -ly $ds[1] -lz $ds[2]"; - $args .= " -Mbt $Mbt -Mbx $Mbs[0] -Mby $Mbs[1] -Mbz $Mbs[2]"; - $args .= " -bt $bt -bx $bs[0] -by $bs[1] -bz $bs[2]"; - $args .= " -mbx $mbs[0] -mby $mbs[1] -mbz $mbs[2]"; - $args .= " -nbx $nbs[0] -nby $nbs[1] -nbz $nbs[2]"; - $args .= " -epx $ps[0] -epy $ps[1] -epz $ps[2]"; - if ($showTiles) { - $args .= " -b_tilex $b_tiles[0] -b_tiley $b_tiles[1] -b_tilez $b_tiles[2]"; - $args .= " -mb_tilex $mb_tiles[0] -mb_tiley $mb_tiles[1] -mb_tilez $mb_tiles[2]"; - $args .= " -nb_tilex $nb_tiles[0] -nb_tiley $nb_tiles[1] -nb_tilez $nb_tiles[2]"; - } - - # num of secs and trials. - my $shortTime = 1; - my $shortTrials = 1; - my $longTime = 5; - my $longTrials = min($gen, 2); - - # various commands. - my $testCmd = "$runCmd -v $runArgs"; # validation on a small problem size. - my $simCmd = "$runCmd $args -num_trials 1 -trial_steps 1 $runArgs"; # simulation w/1 trial & 1 step. - my $shortRunCmd = "$runCmd $args -num_trials $shortTrials -trial_time $shortTime $runArgs"; # fast run for 'upper-bound' time. - my $longRunCmd = "$runCmd $args -num_trials $longTrials -trial_time $longTime $runArgs"; # normal run w/more trials. - - # add kill command to prevent runaway code. - if (-x $killCmd) { - - # for the make command. - $makeCmd = "$killCmd $makeTimeout $makeCmd"; - - # for the run commands. - if (!$sde) { - - # Kill time is some overhead plus allocated time multipled by temporal blocking and num runs. - my $maxOHead = 60 * 10; - my $killTime = $maxOHead + ($shortTime * max($Mbt, $bt) * $shortTrials); - - # Inject kill command into '-exe_prefix' part of run commands. - $shortRunCmd =~ s/$timeCmd/$timeCmd $killCmd $killTime/; - - # Repeat for long run. - $killTime = $maxOHead + ($longTime * max($Mbt, $bt) * $longTrials); - $longRunCmd =~ s/$timeCmd/$timeCmd $killCmd $killTime/; - } - } - - # do actual fitness eval if sanity check passed. - my $fitness; - my $results = {}; - if ($ok) { - ($fitness, $results) = evalIndiv($makeCmd, $testCmd, $simCmd, $shortRunCmd, $longRunCmd, $dPts); - print "results:\n"; - for my $k (sort keys %$results) { - print " $k: $results->{$k}\n"; - } - } - - # count good runs. - if (defined $fitness && $fitness > 0) { - $okRuns++; - } - - # on failure, set fitness to small number. - else { - $fitness = 1e-6; - } - - # track best result. - my $isBest = 0; - if ($fitness > $bestFit) { - $bestFit = $fitness; - $bestGen = $gen; - $isBest = 1; - } - - # print results to CSV file. - my @cols = ( $run, $gen, $indiv ); - for my $fk (sort keys %fixedVals) { - push @cols, $fixedVals{$fk}; - } - push @cols, @$values, '"'.$makeCmd.'"', '"'.$longRunCmd.'"'; - print $outFH join(',', @cols,''); - YaskUtils::printCsvValues($results, $outFH); - @cols = ( $fitness, $bestGen, $bestFit, $isBest ? 'TRUE':'FALSE' ); - print $outFH join(',', '',@cols), "\n"; - - print "final fitness = $fitness\n". - "=====================================\n"; - return $fitness; -} - -# called at end of every generation. -# terminate if non-zero returned. -sub terminateFunc { - my $ga = shift; - my $best = $ga->getFittest->score(); - my $gen = $ga->generation()+1; - - print "========== end of generation $gen ===============\n"; - printCheckStats(); - print "Best fitness so far (from gen $bestGen) = $best\n"; - - if ($okRuns >= $maxOkRuns) { - print "max runs of $maxOkRuns reached; exiting.\n"; - return 1; - } - - if ($bestGen + $maxFlatGens < $gen) { - print "exiting because the best generation was ".($gen - $bestGen)." generations ago.\n"; - return 1; - } - - return 0; -} - -# sweep through var #i; recurse if not last var. -# returns number of runs made. -sub sweep { - my $doRun = shift; # whether to actually run. - my $i = shift; # index into array. - my @a = @_; # array of current values. - - my $n = 0; - for (my $v = $ranges[$i][$minI]; $v <= $ranges[$i][$maxI]; $v += $ranges[$i][$stepI]) { - $a[$i] = $v; - - # evaluate if at end of list. - if ($i == $#ranges) { - if (check(\@a)) { - $n++; - fitness(\@a) if $doRun; - } - } - - # otherwise, recurse. - else { - $n += sweep($doRun, $i + 1, @a); - } - } - return $n; -} - -sub printNumCombos($) { - my $nt = shift; - printf "Total: %.3g trials\n". - "Time est.: %.2g hrs (%.2g days) assuming $secPerTrial secs per trial\n", - $nt, $nt * $secPerTrial / 60/60, $nt * $secPerTrial / 60/60/24; -} - -# header -my @names = map { $_->[$nameI] } @ranges; -print $outFH join(',', "run", "generation", "individual", - sort(keys %fixedVals), @names, - "make command", "run command", ''); -YaskUtils::printCsvHeader($outFH); -print $outFH join(',', '', "fitness", "best generation so far", - "best fitness so far", "is this best so far"), "\n" - unless $checking; -print "\nSize of search space:\n"; -my $nt = 1; -for my $i (0..$#ranges) { - my $n = int(($ranges[$i][$maxI] - $ranges[$i][$minI]) / $ranges[$i][$stepI]) + 1; - my $by = ($ranges[$i][$stepI] == 1) ? '' : " by $ranges[$i][$stepI]"; - print " '$ranges[$i][$nameI]': \t$ranges[$i][$minI] - $ranges[$i][$maxI]$by: \t$n values\n"; - $nt *= $n; -} -printNumCombos($nt); -print "Memory footprint restriction: >= $minGB GiB.\n" - if defined $minGB; -print "Memory footprint restriction: <= $maxGB GiB.\n" - if defined $maxGB; - -if ($sweep) { - $popSize = $nt; # just so all output shows as 1st gen. - - die "search space is too large for an exhaustive sweep; reduce the ranges and retry" - if $nt > 1e8; - - print "\nNumber of trials passing initial check:\n"; - my @values; - $nt = sweep(0, 0, @values); - printNumCombos($nt); - - die "search space is too large for an exhaustive sweep; reduce the ranges and retry" - if $nt > 1e6; - - die "Exiting due to -check option.\n" if $checking; - print "\nStarting in 5 sec...\n"; - sleep 5; - init(); - sweep(1, 0, @values); - -} else { - - my $nt = $popSize * $numGens; - $nt = $maxOkRuns if $maxOkRuns < $nt; - print "Max number of trials for GA if it does not converge:\n"; - print " population size = $popSize\n"; - print " num generations = $numGens\n"; - print " max evals = $maxOkRuns\n"; - printNumCombos($nt); - - # see http://search.cpan.org/~aqumsieh/AI-Genetic-0.05/Genetic.pm - # -check option was added, and some bugs were fixed. - my $ga = new AI::Genetic( -fitness => \&fitness, - -check => \&fitness, - -type => 'rangevector', - -population => $popSize, - -crossover => 0.90, - -mutation => 0.10, - -terminate => \&terminateFunc, - -maxCheckFails => 100000, - ); - - print "Creating initial population of $popSize...\n"; - init(); - my $ok = $ga->init(\@ranges); - printCheckStats(); - die "Exiting due to -check option.\n" if $checking; - - if ($ok) { - print "Starting evaluations in 5 sec...\n"; - sleep 5; - $justChecking = 0; - $debugCheck = 1; - $ga->evolve('rouletteTwoPoint', $numGens); - - print "Size = ", $ga->size(), "\n"; - print "Gen = ", $ga->generation()+1, "\n"; - print "Best score = ", $ga->getFittest->score(), "\n"; - } -} -close $outFH; -print "Done; output in '$outDir'.\n"; diff --git a/utils/bin/yask_tuner_summary.csh b/utils/bin/yask_tuner_summary.csh deleted file mode 100755 index cbca2e3b..00000000 --- a/utils/bin/yask_tuner_summary.csh +++ /dev/null @@ -1,44 +0,0 @@ -#!/bin/csh -f - -############################################################################## -## YASK: Yet Another Stencil Kit -## Copyright (c) 2014-2023, Intel Corporation -## -## Permission is hereby granted, free of charge, to any person obtaining a copy -## of this software and associated documentation files (the "Software"), to -## deal in the Software without restriction, including without limitation the -## rights to use, copy, modify, merge, publish, distribute, sublicense, and/or -## sell copies of the Software, and to permit persons to whom the Software is -## furnished to do so, subject to the following conditions: -## -## * The above copyright notice and this permission notice shall be included in -## all copies or substantial portions of the Software. -## -## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -## IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -## FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -## AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -## LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING -## FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS -## IN THE SOFTWARE. -############################################################################## - -# Purpose: find best result from each GA search csv file. - -if ( "-$1" == "-" ) then - if ( `echo logs/yask_*/*.csv | wc -l` > 0 ) then - $0 logs/yask_*/*.csv - else - echo "usage: $0 [csv-file(s) from yask_tuner.pl]" - endif - exit -endif - -echo "Summary of yask_tuner results:" -head -n1 $1 -foreach f ($*) - echo '==========' - wc -l $f - grep ',TRUE' $f | tail -n1 -end - diff --git a/utils/lib/AI/Changes b/utils/lib/AI/Changes deleted file mode 100644 index d6a8577d..00000000 --- a/utils/lib/AI/Changes +++ /dev/null @@ -1,29 +0,0 @@ -Revision history for Perl extension AI::Genetic. - -0.01 Tue Nov 18 18:04:12 2003 - - original version; created by h2xs 1.21 with options - -AX -n AI::Genetic - -0.02 Fri Apr 16 14:15:17 2004 - - Packaged v0.02 for CPAN. - - This is a complete re-write from scratch. - -0.03 Thu Mar 3 16:13:35 2005 - - Fixed bug where range vector genes were not always - integers. - - Implemented a work-around to a sort bug in Perl. The - bug is discussed here: - http://rt.perl.org/rt3/Ticket/Display.html?id=7063 - -0.04 Mon Jul 18 16:51:47 PDT 2005 - - Fixed bugs reported by crenz@web42.com regarding - rangevectors. Turns out that the last element - in the range was never reached. Fixed. - -0.05 Fri May 11 10:46:09 PDT 2007 - - Fixed bug reported by yraber@mailup.net regarding - listvectors. If a single option is given for a - listvector genome, then mutation would run in an - infinite loop. Fixed. - - updated docs a bit to better show how to specify - the fitness and termination functions. diff --git a/utils/lib/AI/Genetic.pm b/utils/lib/AI/Genetic.pm deleted file mode 100644 index 5080d268..00000000 --- a/utils/lib/AI/Genetic.pm +++ /dev/null @@ -1,799 +0,0 @@ - -package AI::Genetic; - -use strict; -use Carp; - -use vars qw/$VERSION/; - -$VERSION = 0.05; - -use AI::Genetic::Defaults; - -# new AI::Genetic. More modular. -# Not too many checks are done still. - -##### Shared private vars -# this hash predefines some strategies - -my %_strategy = ( - rouletteSinglePoint => \&AI::Genetic::Defaults::rouletteSinglePoint, - rouletteTwoPoint => \&AI::Genetic::Defaults::rouletteTwoPoint, - rouletteUniform => \&AI::Genetic::Defaults::rouletteUniform, - - tournamentSinglePoint => \&AI::Genetic::Defaults::tournamentSinglePoint, - tournamentTwoPoint => \&AI::Genetic::Defaults::tournamentTwoPoint, - tournamentUniform => \&AI::Genetic::Defaults::tournamentUniform, - - randomSinglePoint => \&AI::Genetic::Defaults::randomSinglePoint, - randomTwoPoint => \&AI::Genetic::Defaults::randomTwoPoint, - randomUniform => \&AI::Genetic::Defaults::randomUniform, - ); - -# this hash maps the genome types to the -# classes they're defined in. - -my %_genome2class = ( - bitvector => 'AI::Genetic::IndBitVector', - rangevector => 'AI::Genetic::IndRangeVector', - listvector => 'AI::Genetic::IndListVector', - ); - -################## - -# sub new(): -# This is the constructor. It creates a new AI::Genetic -# object. Options are: -# -population: set the population size -# -crossover: set the crossover probability -# -mutation: set the mutation probability -# -fitness: set the fitness function -# -check: set the initial population viability-check function -# -type: set the genome type. See docs. -# -terminate: set termination sub. - -sub new { - my ($class, %args) = @_; - - my $self = bless { - ADDSEL => {}, # user-defined selections - ADDCRS => {}, # user-defined crossovers - ADDMUT => {}, # user-defined mutations - ADDSTR => {}, # user-defined strategies - } => $class; - - $self->{FITFUNC} = $args{-fitness} || sub { 1 }; # default returns '1'. - $self->{CHECKFUNC} = $args{-check} || sub { 1 }; # default always passes. - $self->{CROSSRATE} = $args{-crossover} || 0.95; - $self->{MUTPROB} = $args{-mutation} || 0.05; - $self->{POPSIZE} = $args{-population} || 100; - $self->{TYPE} = $args{-type} || 'bitvector'; - $self->{TERM} = $args{-terminate} || sub { 0 }; - $self->{MAXCHECKFAILS} = $args{-maxCheckFails} || 1000; # max tries to make an individual. - - $self->{PEOPLE} = []; # list of individuals - $self->{GENERATION} = 0; # current gen. - - $self->{INIT} = 0; # whether pop is initialized or not. - $self->{SORTED} = 0; # whether the population is sorted by score or not. - $self->{INDIVIDUAL} = ''; # name of individual class to use(). - - return $self; -} - -# sub createStrategy(): -# This method creates a new strategy. -# It takes two arguments: name of strategy, and -# anon sub that implements it. - -sub createStrategy { - my ($self, $name, $sub) = @_; - - if (ref($sub) eq 'CODE') { - $self->{ADDSTR}{$name} = $sub; - } else { - # we don't know what this operation is. - carp <{ADDSTR}{$name}; - return undef; - } - - return $name; -} - -# sub evolve(): -# This method evolves the population using a specific strategy -# for a specific number of generations. - -sub evolve { - my ($self, $strategy, $gens) = @_; - - unless ($self->{INIT}) { - carp "can't evolve() before init()"; - return undef; - } - - my $strSub; - if (exists $self->{ADDSTR}{$strategy}) { - $strSub = $self->{ADDSTR}{$strategy}; - } elsif (exists $_strategy{$strategy}) { - $strSub = $_strategy{$strategy}; - } else { - carp "ERROR: Do not know what strategy '$strategy' is,"; - return undef; - } - - $gens ||= 1; - - for my $i (1 .. $gens) { - $self->sortPopulation; - $strSub->($self); - - $self->{GENERATION}++; - $self->{SORTED} = 0; - - last if $self->{TERM}->($self); - -# my @f = $self->getFittest(10); -# for my $f (@f) { -# print STDERR " Fitness = ", $f->score, "..\n"; -# print STDERR " Genes are: @{$f->genes}.\n"; -# } - } -} - -# sub sortIndividuals(): -# This method takes as input an anon list of individuals, and returns -# another anon list of the same individuals but sorted in decreasing -# score. - -sub sortIndividuals { - my ($self, $list) = @_; - - # make sure all scores are calculated. - # This is to avoid a bug in Perl where a sort is called from within another - # sort, and they are in different packages, then you get a use of uninit value - # warning. See http://rt.perl.org/rt3/Ticket/Display.html?id=7063 - for my $i (@$list) { - $i->score; - } - - return [sort { - die if !defined $b; - die if !defined $a; - $b->{SCORE} <=> $a->{SCORE}; - } @$list]; -} - -# sub sortPopulation(): -# This method sorts the population of individuals. - -sub sortPopulation { - my $self = shift; - - return if $self->{SORTED}; - - $self->{PEOPLE} = $self->sortIndividuals($self->{PEOPLE}); - $self->{SORTED} = 1; -} - -# sub getFittest(): -# This method returns the fittest individuals. - -sub getFittest { - my ($self, $N) = @_; - - $N ||= 1; - $N = 1 if $N < 1; - - $N = @{$self->{PEOPLE}} if $N > @{$self->{PEOPLE}}; - - $self->sortPopulation; - - my @r = @{$self->{PEOPLE}}[0 .. $N-1]; - - return $r[0] if $N == 1 && not wantarray; - - return @r; -} - -# sub init(): -# This method initializes the population to completely -# random individuals. It deletes all current individuals!!! -# It also examines the type of individuals we want, and -# require()s the proper class. Throws an error if it can't. -# Must pass to it an anon list that will be passed to the -# newRandom method of the individual. - -# In case of bitvector, $newArgs is length of bitvector. -# In case of rangevector, $newArgs is anon list of anon lists. -# each sub-anon list has two elements, min number and max number. -# In case of listvector, $newArgs is anon list of anon lists. -# Each sub-anon list contains possible values of gene. - -sub init { - my ($self, $newArgs) = @_; - - $self->{INIT} = 0; - - my $ind; - if (exists $_genome2class{$self->{TYPE}}) { - $ind = $_genome2class{$self->{TYPE}}; - } else { - $ind = $self->{TYPE}; - } - - eval "use $ind"; # does this work if package is in same file? - if ($@) { - carp "ERROR: Init failed. Can't require '$ind': $@"; - return undef; - } - - $self->{INDIVIDUAL} = $ind; - $self->{PEOPLE} = []; - $self->{SORTED} = 0; - $self->{GENERATION} = 0; - $self->{INITARGS} = $newArgs; - - for my $i (1 .. $self->{POPSIZE}) { - - # keep trying until check passes or limit reached. - my $ok = 0; - for my $tries (1 .. $self->{MAXCHECKFAILS}) { - my $n = $ind->newRandom($newArgs); - if ($n->check($self->{CHECKFUNC})) { - push @{$self->{PEOPLE}} => $n; - $ok = 1; - last; - } - } - if (!$ok) { - carp "ERROR: unable to create a viable individual in $self->{MAXCHECKFAILS} tries"; - return undef; - } - } - - for my $i (@{$self->{PEOPLE}}) { - $i->fitness($self->{FITFUNC}); - } - - $self->{INIT} = 1; -} - -# sub people(): -# returns the current list of individuals in the population. -# note: this returns the actual array ref, so any changes -# made to it (ex, shift/pop/etc) will be reflected in the -# population. - -sub people { - my $self = shift; - - if (@_) { - $self->{PEOPLE} = shift; - $self->{SORTED} = 0; - } - - $self->{PEOPLE}; -} - -# useful little methods to set/query parameters. -sub size { $_[0]{POPSIZE} = $_[1] if defined $_[1]; $_[0]{POPSIZE} } -sub crossProb { $_[0]{CROSSRATE} = $_[1] if defined $_[1]; $_[0]{CROSSRATE} } -sub mutProb { $_[0]{MUTPROB} = $_[1] if defined $_[1]; $_[0]{MUTPROB} } -sub indType { $_[0]{INDIVIDUAL} } -sub generation { $_[0]{GENERATION} } - -# sub inject(): -# This method is used to add individuals to the current population. -# The point of it is that sometimes the population gets stagnant, -# so it could be useful add "fresh blood". -# Takes a variable number of arguments. The first argument is the -# total number, N, of new individuals to add. The remaining arguments -# are genomes to inject. There must be at most N genomes to inject. -# If the number, n, of genomes to inject is less than N, N - n random -# genomes are added. Perhaps an example will help? -# returns 1 on success and undef on error. - -sub inject { - my ($self, $count, @genomes) = @_; - - unless ($self->{INIT}) { - carp "can't inject() before init()"; - return undef; - } - - my $ind = $self->{INDIVIDUAL}; - - my @newInds; - for my $i (1 .. $count) { - my $genes = shift @genomes; - - if ($genes) { - push @newInds => $ind->newSpecific($genes, $self->{INITARGS}); - } else { - push @newInds => $ind->newRandom ($self->{INITARGS}); - } - } - - $_->fitness($self->{FITFUNC}) for @newInds; - - push @{$self->{PEOPLE}} => @newInds; - - return 1; -} - -__END__ - -=head1 NAME - -AI::Genetic - A pure Perl genetic algorithm implementation. - -=head1 SYNOPSIS - - use AI::Genetic; - my $ga = new AI::Genetic( - -fitness => \&fitnessFunc, - -check => \&checkFunc, - -type => 'bitvector', - -population => 500, - -crossover => 0.9, - -mutation => 0.01, - -terminate => \&terminateFunc, - ); - - $ga->init(10); - $ga->evolve('rouletteTwoPoint', 100); - print "Best score = ", $ga->getFittest->score, ".\n"; - - sub checkFunc { - my $genes = shift; - - my $ok; - # determine whether $genes is probably ok. - - return $ok; - } - - sub fitnessFunc { - my $genes = shift; - - my $fitness; - # assign a number to $fitness based on the @$genes - # ... - - return $fitness; - } - - sub terminateFunc { - my $ga = shift; - - # terminate if reached some threshold. - return 1 if $ga->getFittest->score > $THRESHOLD; - return 0; - } - -=head1 DESCRIPTION - -This module implements a Genetic Algorithm (GA) in pure Perl. -Other Perl modules that achieve the same thing (perhaps better, -perhaps worse) do exist. Please check CPAN. I mainly wrote this -module to satisfy my own needs, and to learn something about GAs -along the way. - -B As of v0.02, AI::Genetic has been re-written from -scratch to be more modular and expandable. To achieve this, I had -to modify the API, so it is not backward-compatible with v0.01. -As a result, I do not plan on supporting v0.01. - -I will not go into the details of GAs here, but here are the -bare basics. Plenty of information can be found on the web. - -In a GA, a population of individuals compete for survival. Each -individual is designated by a set of genes that define its -behaviour. Individuals that perform better (as defined by the -fitness function) have a higher chance of mating with other -individuals. When two individuals mate, they swap some of -their genes, resulting in an individual that has properties -from both of its "parents". Every now and then, a mutation -occurs where some gene randomly changes value, resulting in -a different individual. If all is well defined, after a few -generations, the population should converge on a "good-enough" -solution to the problem being tackled. - -A GA implementation runs for a discrete number of time steps -called I. What happens during each generation can -vary greatly depending on the strategy being used (See -L for more info). -Typically, a variation of the following happens at -each generation: - -=over 4 - -=item B<1. Selection> - -Here the performance of all the individuals is evaluated -based on the fitness function, and each is given a specific -fitness value. The higher the value, the bigger the chance -of an individual passing its genes on in future generations -through mating (crossover). - -=item B<2. Crossover> - -Here, individuals selected are randomly paired up for -crossover (aka I). This is further -controlled by the crossover rate specified and may result in -a new offspring individual that contains genes common to -both parents. New individuals are injected into the current -population. - -=item B<3. Mutation> - -In this step, each individual is given the chance to mutate -based on the mutation probability specified. If an individual -is to mutate, each of its genes is given the chance to randomly -switch its value to some other state. - -=back - -=head1 CLASS METHODS - -Here are the public methods. - -=over 4 - -=item I<$ga>-EB(I) - -This is the constructor. It accepts options in the form of -hash-value pairs. These are: - -=over 8 - -=item B<-population> - -This defines the size of the population, i.e. how many individuals -to simultaneously exist at each generation. Defaults to 100. - -=item B<-crossover> - -This defines the crossover rate. Defaults to 0.95. - -=item B<-mutation> - -This defines the mutation rate. Defaults to 0.05. - -=item I<-fitness> - -This defines a fitness function. It expects a reference to a subroutine. -More details are given in L. - -=item I<-type> - -This defines the type of the genome. Currently, AI::Genetic -supports only three types: - -=over - -=item I - -Individuals of this type have genes that are bits. Each gene -can be in one of two possible states, on or off. - -=item I - -Each gene of a listvector individual can assume one string value from -a specified list of possible string values. - -=item I - -Each gene of a rangevector individual can assume one integer value -from a range of possible integer values. Note that only integers are -supported. The user can always transform any desired fractional values -by multiplying and dividing by an appropriate power of 10. - -=back - -Defaults to I. - -=item I<-terminate> - -This option allows the definition of a termination subroutine. -It expects a subroutine reference. This sub will be called at -the end of each generation with one argument: the AI::Genetic -object. Evolution terminates if the sub returns a true value. - -=back - -=item I<$ga>-EB(I, I) - -This method allows the creation of a custom-made strategy to be used -during evolution. It expects a unique strategy name, and a subroutine -reference as arguments. The subroutine will be called with one argument: -the AI::Genetic object. It is expected to alter the population at each -generation. See L for more information. - -=item I<$ga>-EB(I) - -This method initializes the population with random individuals. It B -be called before any call to I or I. As a side effect, -any already existing individuals in the population are deleted. It expects -one argument, which depends on the type of individuals: - -=over - -=item o - -For bitvectors, the argument is simply the length of the bitvector. - - $ga->init(10); - -this initializes a population where each individual has 10 genes. - -=item o - -For listvectors, the argument is an anonymous list of lists. The -number of sub-lists is equal to the number of genes of each individual. -Each sub-list defines the possible string values that the corresponding gene -can assume. - - $ga->init([ - [qw/red blue green/], - [qw/big medium small/], - [qw/very_fat fat fit thin very_thin/], - ]); - -this initializes a population where each individual has 3 genes, and each gene -can assume one of the given values. - -=item o - -For rangevectors, the argument is an anonymous list of lists. The -number of sub-lists is equal to the number of genes of each individual. -Each sub-list defines the minimum and maximum integer values that the -corresponding gene can assume. - - $ga->init([ - [1, 5], - [0, 20], - [4, 9], - ]); - -this initializes a population where each individual has 3 genes, and each gene -can assume an integer within the corresponding range. - -=back - -=item I<$ga>-EB(I, ?I?) - -This method can be used to add more individuals to the population. New individuals -can be randomly generated, or be explicitly specified. The first argument specifies -the number, I, of new individuals to add. This can be followed by at most I -arguments, each of which is an anonymous list that specifies the genome of a -single individual to add. If the number of genomes given, I, is less than I, then -I - I random individuals are added for a total of I new individuals. Random -individuals are generated using the same arguments passed to the I method. -For example: - - $ga->inject(5, - [qw/red big thin/], - [qw/blue small fat/], - ); - -this adds 5 new individuals, 2 with the specified genetic coding, and 3 randomly -generated. - -=item I<$ga>-EB(I, ?I?) - -This method causes the GA to evolve the population using the specified strategy. -A strategy name has to be specified as the first argument. The second argument -is optional and specifies the number of generations to evolve. It defaults to -1. See L for more information on the default strategies. - -Each generation consists of the following steps: - -=over - -=item o - -The population is sorted according to the individuals' fitnesses. - -=item o - -The subroutine corresponding to the named strategy is called with one argument, -the AI::Genetic object. This subroutine is expected to alter the object itself. - -=item o - -If a termination subroutine is given, it is executed and the return value is -checked. Evolution terminates if this sub returns a true value. - -=back - -=item I<$ga>-EB(?I?) - -This returns the I fittest individuals. If not specified, -I defaults to 1. As a side effect, it sorts the population by -fitness score. The actual AI::Genetic::Individual objects are returned. -You can use the C and C methods to get the genes and the -scores of the individuals. Please check L for details. - -=item I<$ga>-EB - -This method sorts the population according to fitness function. The results -are cached for speed. - -=item I<$ga>-EB(?[I]?) - -Given an anonymous list of individuals, this method sorts them according -to fitness, returning an anonymous list of the sorted individuals. - -=item I<$ga>-EB() - -Returns an anonymous list of individuals of the current population. -B: the actual array reference used by the AI::Genetic object -is returned, so any changes to it will be reflected in I<$ga>. - -=item I<$ga>-EB(?I?) - -This method is used to query and set the population size. - -=item I<$ga>-EB(?I?) - -This method is used to query and set the crossover rate. - -=item I<$ga>-EB(?I?) - -This method is used to query and set the mutation rate. - -=item I<$ga>-EB() - -This method returns the type of individual: I, I, -or I. - -=item I<$ga>-EB() - -This method returns the current generation. - -=back - -=head1 FITNESS FUNCTION - -Very quickly you will realize that properly defining the fitness function -is the most important aspect of a GA. Most of the time that a genetic -algorithm takes to run is spent in running the fitness function for each -separate individual to get its fitness. AI::Genetic tries to minimize this -time by caching the fitness result for each individual. But, B - -The fitness function should expect only one argument, an anonymous list of -genes, corresponding to the individual being analyzed. It is expected -to return a number which defines the fitness score of the said individual. -The higher the score, the more fit the individual, the more the chance it -has to be chosen for crossover. - -=head1 STRATEGIES - -AI::Genetic comes with 9 predefined strategies. These are: - -=over - -=item rouletteSinglePoint - -This strategy implements roulette-wheel selection and single-point crossover. - -=item rouletteTwoPoint - -This strategy implements roulette-wheel selection and two-point crossover. - -=item rouletteUniform - -This strategy implements roulette-wheel selection and uniform crossover. - -=item tournamentSinglePoint - -This strategy implements tournament selection and single-point crossover. - -=item tournamentTwoPoint - -This strategy implements tournament selection and two-point crossover. - -=item tournamentUniform - -This strategy implements tournament selection and uniform crossover. - -=item randomSinglePoint - -This strategy implements random selection and single-point crossover. - -=item randomTwoPoint - -This strategy implements random selection and two-point crossover. - -=item randomUniform - -This strategy implements random selection and uniform crossover. - -=back - -More detail on these strategies and how to call them in your own -custom strategies can be found in L, -L and L. - -You can use the functions defined in the above modules in your -own custom-made strategy. Consult their manpages for more info. -A custom-made strategy can be defined using the I -method and is called at the beginning of each generation. The only -argument to it is the AI::Genetic object itself. Note that the -population at this point is sorted accoring to each individual's -fitness score. It is expected that the strategy sub will modify -the population stored in the AI::Genetic object. Here's the -pseudo-code of events: - - for (1 .. num_generations) { - sort population; - call strategy_sub; - if (termination_sub exists) { - call termination_sub; - last if returned true value; - } - } - -=head1 A NOTE ON SPEED/EFFICIENCY - -Genetic algorithms are inherently slow. -Perl can be pretty fast, but will never reach the speed of optimized -C code (at least my Perl coding will not). I wrote AI::Genetic mainly -for my own learning experience, but still tried to optimize it as -much as I can while trying to keep it as flexible as possible. - -To do that, I resorted to some well-known tricks like passing a -reference of a long list instead of the list itself (for example, -when calling the fitness function, a reference of the gene list -is passed), and caching fitness scores (if you try to evaluate -the fitness of the same individual more than once, then the fitness -function will not be called, and the cached result is returned). - -To help speed up your run times, you should pay special attention -to the design of your fitness function since this will be called once -for each unique individual in each generation. If you can shave off a -few clock cycles here and there, then it will be greatly magnified in -the total run time. - -=head1 BUGS - -I have tested this module quite a bit, and even used it to solve a -work-related problem successfully. But, if you think you found a bug -then please let me know, and I promise to look at it. - -Also, if you have any requests, comments or suggestions, then feel -free to email me. - -=head1 INSTALLATION - -Either the usual: - - perl Makefile.PL - make - make install - -or just stick it somewhere in @INC where perl can find it. It is in pure Perl. - -=head1 AUTHOR & CREDITS - -Written by Ala Qumsieh I. - -Special thanks go to John D. Porter and Oliver Smith for stimulating -discussions and great suggestions. Daniel Martin and Ivan Tubert-Brohman -uncovered various bugs and for this I'm grateful. - -=head1 COPYRIGHTS - -(c) 2003-2005 Ala Qumsieh. All rights reserved. -This module is distributed under the same terms as Perl itself. - -=cut diff --git a/utils/lib/AI/Genetic/Defaults.pm b/utils/lib/AI/Genetic/Defaults.pm deleted file mode 100644 index 9bffb05b..00000000 --- a/utils/lib/AI/Genetic/Defaults.pm +++ /dev/null @@ -1,128 +0,0 @@ - -package AI::Genetic::Defaults; - -use strict; -use AI::Genetic::OpSelection; -use AI::Genetic::OpCrossover; -use AI::Genetic::OpMutation; - -1; - -# this implements the default strategies. - -sub rouletteSinglePoint { - # initialize the roulette wheel - AI::Genetic::OpSelection::initWheel($_[0]->people); - - push @_ => 'vectorSinglePoint', 'rouletteUnique'; - goto &genericStrategy; -} - -sub rouletteTwoPoint { - # initialize the roulette wheel - AI::Genetic::OpSelection::initWheel($_[0]->people); - - push @_ => 'vectorTwoPoint', 'rouletteUnique'; - goto &genericStrategy; -} - -sub rouletteUniform { - # initialize the roulette wheel - AI::Genetic::OpSelection::initWheel($_[0]->people); - - push @_ => 'vectorUniform', 'rouletteUnique'; - goto &genericStrategy; -} - -sub tournamentSinglePoint { - push @_ => 'vectorSinglePoint', 'tournament', [$_[0]->people]; - goto &genericStrategy; -} - -sub tournamentTwoPoint { - push @_ => 'vectorTwoPoint', 'tournament', [$_[0]->people]; - goto &genericStrategy; -} - -sub tournamentUniform { - push @_ => 'vectorUniform', 'tournament', [$_[0]->people]; - goto &genericStrategy; -} - -sub randomSinglePoint { - push @_ => 'vectorSinglePoint', 'random', [$_[0]->people]; - goto &genericStrategy; -} - -sub randomTwoPoint { - push @_ => 'vectorTwoPoint', 'random', [$_[0]->people]; - goto &genericStrategy; -} - -sub randomUniform { - push @_ => 'vectorUniform', 'random', [$_[0]->people]; - goto &genericStrategy; -} - -# generic sub that implements everything. -sub genericStrategy { - my ($ga, $Xop, $selOp, $selArgs) = @_; - - #perhaps args should be: - # ($ga, [xop, xargs], [selop, selargs]) ? - - my $pop = $ga->people; - - # now double up the individuals, and get top half. - my $size = $ga->size; - my $ind = $ga->indType; - - my @newPop; - - # optimize - my $crossProb = $ga->crossProb; - - # figure out mutation routine to use, and its arguments. - my @mutArgs = ($ga->mutProb); - my $mutOp = 'bitVector'; - if ($ind =~ /IndRangeVector/) { - $mutOp = 'rangeVector'; - push @mutArgs => $pop->[0]->ranges; - } elsif ($ind =~ /IndListVector/) { - $mutOp = 'listVector'; - push @mutArgs => $pop->[0]->lists; - } - - my ($ssub, $xsub, $msub); - { - no strict 'refs'; - $ssub = \&{"AI::Genetic::OpSelection::$selOp"}; - $xsub = \&{"AI::Genetic::OpCrossover::$Xop"}; - $msub = \&{"AI::Genetic::OpMutation::$mutOp"}; - } - - for my $i (1 .. $size/2) { - my @parents = $ssub->(@$selArgs); - @parents < 2 and push @parents => $ssub->(@$selArgs); - - my @cgenes = $xsub->($crossProb, map scalar $_->genes, @parents); - - # check if two didn't mate. - unless (ref $cgenes[0]) { - @cgenes = map scalar $_->genes, @parents; - } - - # mutate them. - $_ = $msub->(@mutArgs, $_) for @cgenes; - - # push them into pop. - push @newPop => map $pop->[0]->new($_), @cgenes; - } - - # assign the fitness function. This is UGLY. - my $fit = $pop->[0]->fitness; - $_->fitness($fit) for @newPop; - - # now chop in half and reassign the population. - $ga->people(AI::Genetic::OpSelection::topN([@$pop, @newPop], $size)); -} diff --git a/utils/lib/AI/Genetic/IndBitVector.pm b/utils/lib/AI/Genetic/IndBitVector.pm deleted file mode 100644 index 988320a0..00000000 --- a/utils/lib/AI/Genetic/IndBitVector.pm +++ /dev/null @@ -1,45 +0,0 @@ - -package AI::Genetic::IndBitVector; - -use strict; -use base qw/AI::Genetic::Individual/; - -1; - -sub newRandom { - my ($class, $length) = @_; - - my $self = bless { - GENES => [], - SCORE => 0, - FITFUNC => sub {}, - CHECKFUNC => sub {}, - CALCED => 0, - } => $class; - - push @{$self->{GENES}} => rand > 0.5 ? 1 : 0 - for 1 .. $length; - - return $self; -} - -sub newSpecific { - my ($class, $genes) = @_; - - my $self = bless { - GENES => $genes, - CALCED => 0, - SCORE => 0, - FITFUNC => sub {}, - CHECKFUNC => sub {}, - } => $class; - - return $self; -} - -sub genes { - my $self = shift; - - return wantarray ? @{$self->{GENES}} : [@{$self->{GENES}}]; -} - diff --git a/utils/lib/AI/Genetic/IndListVector.pm b/utils/lib/AI/Genetic/IndListVector.pm deleted file mode 100644 index 034c8275..00000000 --- a/utils/lib/AI/Genetic/IndListVector.pm +++ /dev/null @@ -1,47 +0,0 @@ - -package AI::Genetic::IndListVector; - -use strict; -use base qw/AI::Genetic::Individual/; - -1; - -sub newRandom { - my ($class, $lists) = @_; - - my $self = bless { - GENES => [], - SCORE => 0, - FITFUNC => sub {}, - CHECKFUNC => sub {}, - CALCED => 0, - LISTS => $lists, - } => $class; - - push @{$self->{GENES}} => $_->[rand @$_] for @$lists; - - return $self; -} - -sub newSpecific { - my ($class, $genes, $lists) = @_; - - my $self = bless { - GENES => $genes, - CALCED => 0, - SCORE => 0, - FITFUNC => sub {}, - CHECKFUNC => sub {}, - LISTS => $lists, - } => $class; - - return $self; -} - -sub genes { - my $self = shift; - - return wantarray ? @{$self->{GENES}} : [@{$self->{GENES}}]; -} - -sub lists { $_[0]{LISTS} } diff --git a/utils/lib/AI/Genetic/IndRangeVector.pm b/utils/lib/AI/Genetic/IndRangeVector.pm deleted file mode 100644 index 0fa82e3c..00000000 --- a/utils/lib/AI/Genetic/IndRangeVector.pm +++ /dev/null @@ -1,50 +0,0 @@ - -package AI::Genetic::IndRangeVector; - -use strict; -use base qw/AI::Genetic::Individual/; - -1; - -sub newRandom { - my ($class, $ranges) = @_; - - my $self = bless { - GENES => [], - SCORE => 0, - FITFUNC => sub {}, - CHECKFUNC => sub {}, - CALCED => 0, - RANGES => $ranges, - } => $class; - - for my $r (@$ranges) { - my $rand = $r->[0] + int rand($r->[1] - $r->[0] + 1); - push @{$self->{GENES}} => $rand; - } - - return $self; -} - -sub newSpecific { - my ($class, $genes, $ranges) = @_; - - my $self = bless { - GENES => $genes, - CALCED => 0, - SCORE => 0, - FITFUNC => sub {}, - CHECKFUNC => sub {}, - RANGES => $ranges, - } => $class; - - return $self; -} - -sub genes { - my $self = shift; - - return wantarray ? @{$self->{GENES}} : [@{$self->{GENES}}]; -} - -sub ranges { $_[0]{RANGES} } diff --git a/utils/lib/AI/Genetic/Individual.pm b/utils/lib/AI/Genetic/Individual.pm deleted file mode 100644 index 7c24af32..00000000 --- a/utils/lib/AI/Genetic/Individual.pm +++ /dev/null @@ -1,256 +0,0 @@ - -package AI::Genetic::Individual; - -use strict; -use vars qw/$VERSION/; -$VERSION = 0.02; - -# this package is to serve as a base package to -# all other individuals. It doesn't do anything -# interesting. - -1; - -sub new { # hmm .. do I need this? - my ($class, $genes) = @_; - - my $self; - if (ref $class) { # clone mode - $self = bless {} => ref $class; - $self->{$_} = $class->{$_} for keys %$class; - $self->{GENES} = $genes; - $self->{CALCED} = 0; - - } else { # new mode. Genome is given - goto &newSpecific; - } - - return $self; -} - -sub new_old { # hmm .. do I need this? - my ($class, $genes) = @_; - - my $self; - if (ref $class) { # clone mode - $self = bless {} => ref $class; - $self->{$_} = $class->{$_} for keys %$class; - $self->{GENES} = $genes; - $self->{CALCED} = 0; - - } else { # new mode. Just call newRandom. - goto &newRandom; - } - - return $self; -} - -# should create default methods. -# those are the only three needed. -sub newRandom {} -sub newSpecific {} -sub genes {} - -# the following methods shouldn't be overridden. -sub fitness { - my ($self, $sub) = @_; - - $self->{FITFUNC} = $sub if $sub; - return $self->{FITFUNC}; -} - -sub check { - my ($self, $sub) = @_; - - $self->{CHECKFUNC} = $sub if $sub; - return $self->{CHECKFUNC}->(scalar $self->genes); -} - -sub score { - my $self = shift; - - return $self->{SCORE} if $self->{CALCED}; - - $self->{SCORE} = $self->{FITFUNC}->(scalar $self->genes); - $self->{CALCED} = 1; - - return $self->{SCORE}; -} - -sub resetScore { $_[0]{CALCED} = 0 } - -# hmmm .. how do I reset {CALCED} in case of mutation? - -__END__ - -=head1 NAME - -AI::Genetic::Individual - Base class for AI::Genetic Individuals. - -=head1 SYNOPSIS - -See L. - -=head1 DESCRIPTION - -This package implements the base class for all AI::Genetic individuals. -It provides basic methods required by AI::Genetic for correct evolution. -Furthermore, it can be very easily used as a base class for additional -types of individuals. AI::Genetic comes with three individual types that -inherit from this class. These are I, I, -and I. - -See L for more details. - -=head1 CLASS METHODS - -The following methods are accessible publicly. They are not meant to -be over-ridden: - -=over - -=item I<$individual> -EB - -=item I -EB - -=item I -EB - -=item I-EB - -This is the default constructor. It can be called as an instance method or -as a class method. In both cases it returns a new individual of -the proper type. - -If called as an instance method, it expects one argument -which defines the genes of the individual. All other attributes, like -fitness function, class, etc, will be copied from the calling -instance. - -If called as a class method, then it calls I. See below -for details. - -=item I<$individual>-EB - -This method is used to set/query the anonymous subroutine used to -calculate the individual's fitness. If an argument is given, it expects -a subroutine reference which will be set as the fitness subroutine. In -either case, it'll return the fitness sub ref. - -=item I<$individual>-EB - -This method returns the fitness score of the individual. If the score has -never been calculated before, then the fitness function is executed and -the score saved. Subsequent calls to score() will return the cached value. - -=item I<$individual>-EB - -This method resets the score of the individual such that a subsequent call -to I will result in the execution of the fitness sub. - -=back - -The following methods are meant to be over-ridden by any class that -inherits from AI::Genetic::Individual: - -=over 4 - -=item I<$individual>-EB - -This method returns an individual with random genes. It is called with the -arguments supplied to I as explained in -L-EB(I)>. - -=item I<$individual>-EB - -This method returns an individual with the given genetic makeup. The -options depend on the type of individual: - -=over - -=item o bitvector - -One argument is expected which is an anonymous list of genes: - - AI::Genetic::IndBitVector->new([0, 1, 1, 0, 1, 0]); - -=item o listvector - -Two arguments are expected. The first is an anonymous list of -genes, the second is an anonymous list of lists of possible gene values, -similar to the argument of I. - - AI::Genetic::IndListVector->new( - [qw/red medium fat/], # genes - [ # possible values - [qw/red blue green/], - [qw/big medium small/], - [qw/very_fat fat fit thin very_thin/], - ]); - -=item o rangevector - -Two arguments are expected. The first is an anonymous list of -genes, the second is an anonymous list of lists of possible gene values, -similar to the argument of I. - - AI::Genetic::IndListVector->new( - [3, 14, 4], # genes - [ # possible values - [1, 5], - [0, 20], - [4, 9], - ]); - -=back - -=item I<$individual>-EB - -In list context, returns a list of genes. In scalar context returns an -anonymous list of the genes. - -=back - -Other useful non-generic methods: - -=over - -=item I<$listVectorInd>-EB - -This method returns an anonymous list of lists which describes the possible -value of each gene for the given AI::Genetic::IndListVector individual. This -is the same argument passed to I. - -=item I<$rangeVectorInd>-EB - -This method returns an anonymous list of lists which describes the possible -range of each gene for the given AI::Genetic::IndRangeVector individual. This -is the same argument passed to I. - -=back - -=head1 CREATING YOUR OWN INDIVIDUAL CLASS - -Creating your own individual class is easy. All you have to do is inherit from -AI::Genetic::Individual and override the I, I, and -I methods to conform with the documentation above. Specifically, the -arguments to i and I have to match what I -expects as arguments. You can also define any additional methods that you might -require in your own custom-made strategies. - -Note that in order for your own individual class to be useful, you have to define -your own custom strategy that knows how to evolve such individuals. Conceptually, -this should be very simple. - -=head1 AUTHOR - -Written by Ala Qumsieh I. - -=head1 COPYRIGHTS - -(c) 2003,2004 Ala Qumsieh. All rights reserved. -This module is distributed under the same terms as Perl itself. - -=cut - -=cut - diff --git a/utils/lib/AI/Genetic/OpCrossover.pm b/utils/lib/AI/Genetic/OpCrossover.pm deleted file mode 100644 index 899870e6..00000000 --- a/utils/lib/AI/Genetic/OpCrossover.pm +++ /dev/null @@ -1,182 +0,0 @@ - -package AI::Genetic::OpCrossover; - -use strict; - -1; - -# sub vectorSinglePoint(): -# Single point crossover. -# arguments are crossover prob, two -# anon lists of genes (parents). -# If crossover occurs, returns two anon lists -# of children genes. If no crossover, returns 0. -# both parents have to be of same length. - -sub vectorSinglePoint { - my ($prob, $mom, $dad) = @_; - - return 0 if rand > $prob; - - # get single index from 1 to $#{$dad} - my $ind = 1 + int rand $#{$dad}; - - my @c1 = (@$mom[0 .. $ind - 1], - @$dad[$ind .. $#{$dad}]); - my @c2 = (@$dad[0 .. $ind - 1], - @$mom[$ind .. $#{$dad}]); - - return (\@c1, \@c2); -} - -# sub vectorTwoPoint(): -# Two point crossover. -# arguments are crossover prob, two -# anon lists of genes (parents). -# If crossover occurs, returns two anon lists -# of children genes. If no crossover, returns 0. -# both parents have to be of same length. - -sub vectorTwoPoint { - my ($prob, $mom, $dad) = @_; - - return 0 if rand > $prob; - - # get first index from 1 to $#{$dad}-1 - my $ind1 = 1 + int rand($#{$dad} - 1); - - # get second index from $ind1 to $#{$dad} - my $ind2 = $ind1 + 1 + int rand($#{$dad} - $ind1); - my @c1 = (@$mom[0 .. $ind1 - 1], - @$dad[$ind1 .. $ind2 - 1], - @$mom[$ind2 .. $#{$dad}]); - - my @c2 = (@$dad[0 .. $ind1 - 1], - @$mom[$ind1 .. $ind2 - 1], - @$dad[$ind2 .. $#{$dad}]); - - return (\@c1, \@c2); -} - -# sub vectorUniform(): -# Uniform crossover. -# arguments are crossover prob, two -# anon lists of genes (parents). -# If crossover occurs, returns two anon lists -# of children genes. If no crossover, returns 0. -# both parents have to be of same length. - -sub vectorUniform { - my ($prob, $mom, $dad) = @_; - - return 0 if rand > $prob; - - my (@c1, @c2); - for my $i (0 .. $#{$dad}) { - if (rand > 0.5) { - push @c1 => $mom->[$i]; - push @c2 => $dad->[$i]; - } else { - push @c2 => $mom->[$i]; - push @c1 => $dad->[$i]; - } - } - - return (\@c1, \@c2); -} - -__END__ - -=head1 NAME - -AI::Genetic::OpCrossover - A class that implements various crossover operators. - -=head1 SYNOPSIS - -See L. - -=head1 DESCRIPTION - -This package implements a few crossover mechanisms that can be used in user-defined -strategies. The methods in this class are to be called as static class methods, -rather than instance methods, which means you must call them as such: - - AI::Genetic::OpCrossover::MethodName(arguments) - -=head1 CROSSOVER OPERATORS AND THEIR METHODS - -The following crossover operators are defined: - -=over - -=item Single Point - -In single point crossover, a point is selected along the choromosomes of both parents. -The chromosomes are then split at that point, and the head of one parent chromosome is -joined with the tail of the other and vice versa, creating two child chromosomes. -The following method is defined: - -=over - -=item B(I) - -The first argument is the crossover rate. The second and third arguments are anonymous -lists that define the B -of the parents (not AI::Genetic::Individual objects, but the return value of the I -method in scalar context). -If mating occurs, two anonymous lists of genes are returned corresponding to the two -new children. If no mating occurs, 0 is returned. - -=back - -=item Two Point - -In two point crossover, two points are selected along the choromosomes of both parents. -The chromosomes are then cut at those points, and the middle parts are swapped, -creating two child chromosomes. The following method is defined: - -=over - -=item B(I) - -The first argument is the crossover rate. The second and third arguments are anonymous -lists that define the B -of the parents (not AI::Genetic::Individual objects, but the return value of the I -method in scalar context). -If mating occurs, two anonymous lists of genes are returned corresponding to the two -new children. If no mating occurs, 0 is returned. - -=back - -=item Uniform - -In uniform crossover, two child chromosomes are created by looking at each gene in both -parents, and randomly selecting which one to go with each child. -The following method is defined: - -=over - -=item B(I) - -The first argument is the crossover rate. The second and third arguments are anonymous -lists that define the B -of the parents (not AI::Genetic::Individual objects, but the return value of the I -method in scalar context). -If mating occurs, two anonymous lists of genes are returned corresponding to the two -new children. If no mating occurs, 0 is returned. - -=back - -=back - -=head1 AUTHOR - -Written by Ala Qumsieh I. - -=head1 COPYRIGHTS - -(c) 2003,2004 Ala Qumsieh. All rights reserved. -This module is distributed under the same terms as Perl itself. - -=cut - diff --git a/utils/lib/AI/Genetic/OpMutation.pm b/utils/lib/AI/Genetic/OpMutation.pm deleted file mode 100644 index fe03c67f..00000000 --- a/utils/lib/AI/Genetic/OpMutation.pm +++ /dev/null @@ -1,161 +0,0 @@ - -package AI::Genetic::OpMutation; - -use strict; - -1; - -# This package implements various mutation -# algorithms. To be used as static functions. - -# sub bitVector(): -# each gene is a bit: 0 or 1. arguments are mutation -# prob. and anon list of genes. -# returns anon list of mutated genes. - -sub bitVector { - my ($prob, $genes) = @_; - - for my $g (@$genes) { - next if rand > $prob; - - $g = $g ? 0 : 1; - } - - return $genes; -} - -# sub rangeVector(): -# each gene is a floating number, and can be anything -# within a range of two numbers. -# arguments are mutation prob., anon list of genes, -# and anon list of ranges. Each element in $ranges is -# an anon list of two numbers, min and max value of -# the corresponding gene. - -sub rangeVector { - my ($prob, $ranges, $genes) = @_; - - my $i = -1; - for my $g (@$genes) { - $i++; - next if rand > $prob; - - # now randomly choose another value from the range. - my $abs = $ranges->[$i][1] - $ranges->[$i][0] + 1; - $g = $ranges->[$i][0] + int rand($abs); - } - - return $genes; -} - -# sub listVector(): -# each gene is a string, and can be anything -# from a list of possible values supplied by user. -# arguments are mutation prob., anon list of genes, -# and anon list of value lists. Each element in $lists -# is an anon list of the possible values of -# the corresponding gene. - -sub listVector { - my ($prob, $lists, $genes) = @_; - - my $i = -1; - for my $g (@$genes) { - $i++; - next if rand > $prob; - - # now randomly choose another value from the lists. - my $new; - - if (@{$lists->[$i]} == 1) { - $new = $lists->[$i][0]; - } else { - do { - $new = $lists->[$i][rand @{$lists->[$i]}]; - } while $new eq $g; - } - - $g = $new; - } - - return $genes; -} - -__END__ - -=head1 NAME - -AI::Genetic::OpMutation - A class that implements various mutation operators. - -=head1 SYNOPSIS - -See L. - -=head1 DESCRIPTION - -This package implements a few mutation mechanisms that can be used in user-defined -strategies. The methods in this class are to be called as static class methods, -rather than instance methods, which means you must call them as such: - - AI::Genetic::OpCrossover::MethodName(arguments) - -=head1 CLASS METHODS - -There is really one kind of mutation operator implemented in this class, but it -implemented for the three default individuals types. Each gene of an individual -is looked at separately to decide whether it will be mutated or not. Mutation is -decided based upon the mutation rate (or probability). If a mutation is to happen, -then the value of the gene is switched to some other possible value. - -For the case of I, an ON gene switches to an OFF gene. - -For the case of I, a gene's value is replaced by another one from -the possible list of values. - -For the case of I, a gene's value is replaced by another one from -the possible range of integers. - -Thus, there are only three methods: - -=over - -=item B(I) - -The method takes as input the mutation rate, and an anonymous list of genes of -a bitvector individual. The return value is an anonymous list of mutated genes. -Note that -it is possible that no mutation will occur, and thus the returned genes are -identical to the given ones. - -=item B(I) - -The method takes as input the mutation rate, an anonymous list of genes of -a listvector individual, and a list of lists which describe the possible -values for each gene. The return value is an anonymous list of mutated genes. -Note that -it is possible that no mutation will occur, and thus the returned genes are -identical to the given ones. - -=item B(I) - -The method takes as input the mutation rate, an anonymous list of genes of -a rangevector individual, and a list of lists which describe the range of -possible values for each gene. The return value is an anonymous list of -mutated genes. Note that -it is possible that no mutation will occur, and thus the returned genes are -identical to the given ones. - -=back - -=head1 AUTHOR - -Written by Ala Qumsieh I. - -=head1 COPYRIGHTS - -(c) 2003,2004 Ala Qumsieh. All rights reserved. -This module is distributed under the same terms as Perl itself. - -=cut - diff --git a/utils/lib/AI/Genetic/OpSelection.pm b/utils/lib/AI/Genetic/OpSelection.pm deleted file mode 100644 index 1699d622..00000000 --- a/utils/lib/AI/Genetic/OpSelection.pm +++ /dev/null @@ -1,266 +0,0 @@ - -package AI::Genetic::OpSelection; - -use strict; - -my @wheel; -my $wheelPop; - -# sub init(): -# initializes the roulette wheel array. -# must be called whenever the population changes. -# only useful for roulette(). - -sub initWheel { - my $pop = shift; - - my $tot = 0; - for (@$pop) { - my $s = $_->score; - if ($s < 0) { - warn "warning: scores must be >= 0; changing $s to 0\n"; - $s = 0; - } - $tot += $s; - } - - # normalize - # fix if $tot==0; - my @norms = map { $tot ? $_->score / $tot : 1/scalar(@$pop) } @$pop; - - @wheel = (); - - my $cur = 0; - for my $i (@norms) { - push @wheel => [$cur, $cur + $i]; - $cur += $i; - } - - $wheelPop = $pop; -} - -# sub roulette(): -# Roulette Wheel selection. -# argument is number of individuals to select (def = 2). -# returns selected individuals. - -sub roulette { - my $num = shift || 2; - - my @selected; - - for my $j (1 .. $num) { - my $rand = rand; - for my $i (0 .. $#wheel) { - if ($wheel[$i][0] <= $rand && $rand < $wheel[$i][1]) { - push @selected => $wheelPop->[$i]; - last; - } - } - } - - return @selected; -} - -# same as roulette(), but returns unique individuals. -sub rouletteUnique { - my $num = shift || 2; - - # make sure we select unique individuals. - my %selected; - - my $tries = 0; - while ($num > keys %selected) { - my $rand = rand; - - # if it's too hard to find using weighted wheel, just pick evenly. - if ($tries++ > $num * 1000) { - my $i = int rand($#wheel+1); - $selected{$i} = 1; - } - - else { - for my $i (0 .. $#wheel) { - if ($wheel[$i][0] <= $rand && $rand < $wheel[$i][1]) { - $selected{$i} = 1; - last; - } - } - } - } - - return map $wheelPop->[$_], keys %selected; -} - -# sub tournament(): -# arguments are anon list of population, and number -# of individuals in tournament (def = 2). -# return 1 individual. - -sub tournament { - my ($pop, $num) = @_; - - $num ||= 2; - - my %s; - while ($num > keys %s) { - my $i = int rand @$pop; - $s{$i} = 1; - } - - # Force eval of score to keep from breaking $_. - my @p = map $pop->[$_], keys %s; - for my $i (@p) { - $i->score; - } - - return (sort {$b->{SCORE} <=> $a->{SCORE}} @p)[0]; -} - -# sub random(): -# pure random choice of individuals. -# arguments are anon list of population, and number -# of individuals to select (def = 1). -# returns selected individual(s). - -sub random { - my ($pop, $num) = @_; - - $num ||= 1; - - my %s; - while ($num > keys %s) { - my $i = int rand @$pop; - $s{$i} = 1; - } - - return map $pop->[$_], keys %s; -} - -# sub topN(): -# fittest N individuals. -# arguments are anon list of pop, and N (def = 1). -# return anon list of top N individuals. - -sub topN { - my ($pop, $N) = @_; - - $N ||= 1; - - # Force eval to avoid breaking $_. - for my $i (@$pop) { - $i->score; - } - - # hmm .. are inputs already sorted? - return [(sort { - die if !defined $a; - die if !defined $b; - $b->{SCORE} <=> $a->{SCORE}; - } @$pop)[0 .. $N-1]]; -} - -1; - -__END__ - -=head1 NAME - -AI::Genetic::OpSelection - A class that implements various selection operators. - -=head1 SYNOPSIS - -See L. - -=head1 DESCRIPTION - -This package implements a few selection mechanisms that can be used in user-defined -strategies. The methods in this class are to be called as static class methods, -rather than instance methods, which means you must call them as such: - - AI::Genetic::OpSelection::MethodName(arguments) - -=head1 SELECTION OPERATORS AND THEIR METHODS - -The following selection operators are defined: - -=over - -=item Roulette Wheel - -Here, the probability of an individual being selected is proportional to its fitness -score. The following methods are defined: - -=over - -=item B(I) - -This method initializes the roulette wheel. It expects an anonymous list of individuals, -as returned by the I method as described in L. -This B be called only once. - -=item B(?I?) - -This method selects I individuals. I defaults to 2. Note that the same individual -can be selected multiple times. - -=item B(?I?) - -This method selects I unique individuals. I defaults to 2. Any individual -can be selected only once per call to this method. - -=back - -=item Tournament - -Here, I individuals are randomly selected, and the fittest one of -them is returned. The following method is defined: - -=over - -=item B(?I?) - -I defaults to 2. Note that only one individual is returned per call to this -method. - -=back - -=item Random - -Here, I individuals are randomly selected and returned. -The following method is defined: - -=over - -=item B(?I?) - -I defaults to 1. - -=back - -=item Fittest - -Here, the fittest I individuals of the whole population are returned. -The following method is defined: - -=over - -=item B(?I?) - -I defaults to 1. - -=back - -=back - -=head1 AUTHOR - -Written by Ala Qumsieh I. - -=head1 COPYRIGHTS - -(c) 2003,2004 Ala Qumsieh. All rights reserved. -This module is distributed under the same terms as Perl itself. - -=cut - diff --git a/utils/lib/AI/MANIFEST b/utils/lib/AI/MANIFEST deleted file mode 100644 index 80dff2e3..00000000 --- a/utils/lib/AI/MANIFEST +++ /dev/null @@ -1,6 +0,0 @@ -Changes -Genetic.pm -Makefile.PL -MANIFEST -README -test.pl diff --git a/utils/lib/AI/Makefile.PL b/utils/lib/AI/Makefile.PL deleted file mode 100644 index 900ee4d2..00000000 --- a/utils/lib/AI/Makefile.PL +++ /dev/null @@ -1,11 +0,0 @@ -use ExtUtils::MakeMaker; -# See lib/ExtUtils/MakeMaker.pm for details of how to influence -# the contents of the Makefile that is written. -WriteMakefile( - 'NAME' => 'AI::Genetic', - 'VERSION_FROM' => 'Genetic.pm', # finds $VERSION - 'PREREQ_PM' => {}, # e.g., Module::Name => 1.1 - ($] >= 5.005 ? ## Add these new keywords supported since 5.005 - (ABSTRACT_FROM => 'Genetic.pm', # retrieve abstract from module - AUTHOR => 'Ala Qumsieh ') : ()), -); diff --git a/utils/lib/AI/README b/utils/lib/AI/README deleted file mode 100644 index b7b260eb..00000000 --- a/utils/lib/AI/README +++ /dev/null @@ -1,389 +0,0 @@ -NAME - AI::Genetic - A pure Perl genetic algorithm implementation. - -SYNOPSIS - use AI::Genetic; - my $ga = new AI::Genetic( - -fitness => \&fitnessFunc, - -type => 'bitvector', - -population => 500, - -crossover => 0.9, - -mutation => 0.01, - -terminate => \&terminateFunc, - ); - - $ga->init(10); - $ga->evolve('rouletteTwoPoint', 100); - print "Best score = ", $ga->getFittest->score, ".\n"; - - sub fitnessFunc { - my $genes = shift; - - my $fitness; - # assign a number to $fitness based on the @$genes - # ... - - return $fitness; - } - - sub terminateFunc { - my $ga = shift; - - # terminate if reached some threshold. - return 1 if $ga->getFittest->score > $THRESHOLD; - return 0; - } - -DESCRIPTION - This module implements a Genetic Algorithm (GA) in pure Perl. Other Perl - modules that achieve the same thing (perhaps better, perhaps worse) do - exist. Please check CPAN. I mainly wrote this module to satisfy my own - needs, and to learn something about GAs along the way. - - PLEASE NOTE: As of v0.02, AI::Genetic has been re-written from scratch - to be more modular and expandable. To achieve this, I had to modify the - API, so it is not backward-compatible with v0.01. As a result, I do not - plan on supporting v0.01. - - I will not go into the details of GAs here, but here are the bare - basics. Plenty of information can be found on the web. - - In a GA, a population of individuals compete for survival. Each - individual is designated by a set of genes that define its behaviour. - Individuals that perform better (as defined by the fitness function) - have a higher chance of mating with other individuals. When two - individuals mate, they swap some of their genes, resulting in an - individual that has properties from both of its "parents". Every now and - then, a mutation occurs where some gene randomly changes value, - resulting in a different individual. If all is well defined, after a few - generations, the population should converge on a "good-enough" solution - to the problem being tackled. - - A GA implementation runs for a discrete number of time steps called - *generations*. What happens during each generation can vary greatly - depending on the strategy being used (See the section on "STRATEGIES" - for more info). Typically, a variation of the following happens at each - generation: - - 1. Selection - Here the performance of all the individuals is evaluated based on - the fitness function, and each is given a specific fitness value. - The higher the value, the bigger the chance of an individual passing - its genes on in future generations through mating (crossover). - - 2. Crossover - Here, individuals selected are randomly paired up for crossover (aka - *sexual reproduction*). This is further controlled by the crossover - rate specified and may result in a new offspring individual that - contains genes common to both parents. New individuals are injected - into the current population. - - 3. Mutation - In this step, each individual is given the chance to mutate based on - the mutation probability specified. If an individual is to mutate, - each of its genes is given the chance to randomly switch its value - to some other state. - -CLASS METHODS - Here are the public methods. - - *$ga*->new(*options*) - This is the constructor. It accepts options in the form of - hash-value pairs. These are: - - -population - This defines the size of the population, i.e. how many - individuals to simultaneously exist at each generation. - Defaults to 100. - - -crossover - This defines the crossover rate. Defaults to 0.95. - - -mutation - This defines the mutation rate. Defaults to 0.05. - - *-fitness* - This defines a fitness function. It expects a reference to a - subroutine. More details are given in the section on - "FITNESS FUNCTION". - - *-type* This defines the type of the genome. Currently, AI::Genetic - supports only three types: - - *bitvector* - Individuals of this type have genes that are bits. Each - gene can be in one of two possible states, on or off. - - *listvector* - Each gene of a listvector individual can assume one - string value from a specified list of possible string - values. - - *rangevector* - Each gene of a rangevector individual can assume one - integer value from a range of possible integer values. - Note that only integers are supported. The user can - always transform any desired fractional values by - multiplying and dividing by an appropriate power of 10. - - Defaults to *bitvector*. - - *-terminate* - This option allows the definition of a termination - subroutine. It expects a subroutine reference. This sub will - be called at the end of each generation with one argument: - the AI::Genetic object. Evolution terminates if the sub - returns a true value. - - *$ga*->createStrategy(*strategy_name*, *sub_ref*) - This method allows the creation of a custom-made strategy to be used - during evolution. It expects a unique strategy name, and a - subroutine reference as arguments. The subroutine will be called - with one argument: the AI::Genetic object. It is expected to alter - the population at each generation. See the section on "STRATEGIES" - for more information. - - *$ga*->init(*initArgs*) - This method initializes the population with random individuals. It - MUST be called before any call to *evolve()* or *inject()*. As a - side effect, any already existing individuals in the population are - deleted. It expects one argument, which depends on the type of - individuals: - - o For bitvectors, the argument is simply the length of the - bitvector. - - $ga->init(10); - - this initializes a population where each individual has 10 - genes. - - o For listvectors, the argument is an anonymous list of lists. The - number of sub-lists is equal to the number of genes of each - individual. Each sub-list defines the possible string values - that the corresponding gene can assume. - - $ga->init([ - [qw/red blue green/], - [qw/big medium small/], - [qw/very_fat fat fit thin very_thin/], - ]); - - this initializes a population where each individual has 3 genes, - and each gene can assume one of the given values. - - o For rangevectors, the argument is an anonymous list of lists. - The number of sub-lists is equal to the number of genes of each - individual. Each sub-list defines the minimum and maximum - integer values that the corresponding gene can assume. - - $ga->init([ - [1, 5], - [0, 20], - [4, 9], - ]); - - this initializes a population where each individual has 3 genes, - and each gene can assume an integer within the corresponding - range. - - *$ga*->inject(*N*, ?*args*?) - This method can be used to add more individuals to the population. - New individuals can be randomly generated, or be explicitly - specified. The first argument specifies the number, *N*, of new - individuals to add. This can be followed by at most *N* arguments, - each of which is an anonymous list that specifies the genome of a - single individual to add. If the number of genomes given, *n*, is - less than *N*, then *N* - *n* random individuals are added for a - total of *N* new individuals. Random individuals are generated using - the same arguments passed to the *init()* method. For example: - - $ga->inject(5, - [qw/red big thin/], - [qw/blue small fat/], - ); - - this adds 5 new individuals, 2 with the specified genetic coding, - and 3 randomly generated. - - *$ga*->evolve(*strategy*, ?*num_generations*?) - This method causes the GA to evolve the population using the - specified strategy. A strategy name has to be specified as the first - argument. The second argument is optional and specifies the number - of generations to evolve. It defaults to 1. See the section on - "STRATEGIES" for more information on the default strategies. - - Each generation consists of the following steps: - - o The population is sorted according to the individuals' - fitnesses. - - o The subroutine corresponding to the named strategy is called - with one argument, the AI::Genetic object. This subroutine is - expected to alter the object itself. - - o If a termination subroutine is given, it is executed and the - return value is checked. Evolution terminates if this sub - returns a true value. - - *$ga*->getFittest(?*N*?) - This returns the *N* fittest individuals. If not specified, *N* - defaults to 1. As a side effect, it sorts the population by fitness - score. The actual AI::Genetic::Individual objects are returned. You - can use the "genes()" and "score()" methods to get the genes and the - scores of the individuals. Please check the AI::Genetic::Individual - manpage for details. - - *$ga*->sortPopulation - This method sorts the population according to fitness function. The - results are cached for speed. - - *$ga*->sortIndividuals(?[*ListOfIndividuals*]?) - Given an anonymous list of individuals, this method sorts them - according to fitness, returning an anonymous list of the sorted - individuals. - - *$ga*->people() - Returns an anonymous list of individuals of the current population. - IMPORTANT: the actual array reference used by the AI::Genetic object - is returned, so any changes to it will be reflected in *$ga*. - - *$ga*->size(?*newSize*?) - This method is used to query and set the population size. - - *$ga*->crossProb(?*newProb*?) - This method is used to query and set the crossover rate. - - *$ga*->mutProb(?*newProb*?) - This method is used to query and set the mutation rate. - - *$ga*->indType() - This method returns the type of individual: *bitvector*, - *listvector*, or *rangevector*. - - *$ga*->generation() - This method returns the current generation. - -FITNESS FUNCTION - Very quickly you will realize that properly defining the fitness - function is the most important aspect of a GA. Most of the time that a - genetic algorithm takes to run is spent in running the fitness function - for each separate individual to get its fitness. AI::Genetic tries to - minimize this time by caching the fitness result for each individual. - But, you should spend a lot of time optimizing your fitness function to - achieve decent run times. - - The fitness function should expect only one argument, an anonymous list - of genes, corresponding to the individual being analyzed. It is expected - to return a number which defines the fitness score of the said - individual. The higher the score, the more fit the individual, the more - the chance it has to be chosen for crossover. - -STRATEGIES - AI::Genetic comes with 9 predefined strategies. These are: - - rouletteSinglePoint - This strategy implements roulette-wheel selection and single-point - crossover. - - rouletteTwoPoint - This strategy implements roulette-wheel selection and two-point - crossover. - - rouletteUniform - This strategy implements roulette-wheel selection and uniform - crossover. - - tournamentSinglePoint - This strategy implements tournament selection and single-point - crossover. - - tournamentTwoPoint - This strategy implements tournament selection and two-point - crossover. - - tournamentUniform - This strategy implements tournament selection and uniform crossover. - - randomSinglePoint - This strategy implements random selection and single-point - crossover. - - randomTwoPoint - This strategy implements random selection and two-point crossover. - - randomUniform - This strategy implements random selection and uniform crossover. - - More detail on these strategies and how to call them in your own custom - strategies can be found in the AI::Genetic::OpSelection manpage, the - AI::Genetic::OpCrossover manpage and the AI::Genetic::OpMutation - manpage. - - You can use the functions defined in the above modules in your own - custom-made strategy. Consult their manpages for more info. A - custom-made strategy can be defined using the *strategy()* method and is - called at the beginning of each generation. The only argument to it is - the AI::Genetic object itself. Note that the population at this point is - sorted accoring to each individual's fitness score. It is expected that - the strategy sub will modify the population stored in the AI::Genetic - object. Here's the pseudo-code of events: - - for (1 .. num_generations) { - sort population; - call strategy_sub; - if (termination_sub exists) { - call termination_sub; - last if returned true value; - } - } - -A NOTE ON SPEED/EFFICIENCY - Genetic algorithms are inherently slow. Perl can be pretty fast, but - will never reach the speed of optimized C code (at least my Perl coding - will not). I wrote AI::Genetic mainly for my own learning experience, - but still tried to optimize it as much as I can while trying to keep it - as flexible as possible. - - To do that, I resorted to some well-known tricks like passing a - reference of a long list instead of the list itself (for example, when - calling the fitness function, a reference of the gene list is passed), - and caching fitness scores (if you try to evaluate the fitness of the - same individual more than once, then the fitness function will not be - called, and the cached result is returned). - - To help speed up your run times, you should pay special attention to the - design of your fitness function since this will be called once for each - unique individual in each generation. If you can shave off a few clock - cycles here and there, then it will be greatly magnified in the total - run time. - -BUGS - I have tested this module quite a bit, and even used it to solve a - work-related problem successfully. But, if you think you found a bug - then please let me know, and I promise to look at it. - - Also, if you have any requests, comments or suggestions, then feel free - to email me. - -INSTALLATION - Either the usual: - - perl Makefile.PL - make - make install - - or just stick it somewhere in @INC where perl can find it. It is in pure - Perl. - -AUTHOR & CREDITS - Written by Ala Qumsieh *aqumsieh@cpan.org*. - - Special thanks go to John D. Porter and Oliver Smith for stimulating - discussions and great suggestions. Daniel Martin and Ivan Tubert-Brohman - uncovered various bugs and for this I'm grateful. - -COPYRIGHTS - (c) 2003-2005 Ala Qumsieh. All rights reserved. This module is - distributed under the same terms as Perl itself. - diff --git a/utils/lib/AI/test.pl b/utils/lib/AI/test.pl deleted file mode 100644 index 1c3598b0..00000000 --- a/utils/lib/AI/test.pl +++ /dev/null @@ -1,17 +0,0 @@ -# Before `make install' is performed this script should be runnable with -# `make test'. After `make install' it should work as `perl test.pl' - -######################### - -# change 'tests => 1' to 'tests => last_test_to_print'; - -use Test; -BEGIN { plan tests => 1 }; -use AI::Genetic; -ok(1); # If we made it this far, we're ok. - -######################### - -# Insert your test code below, the Test module is use()ed here so read -# its man page ( perldoc Test ) for help writing this test script. - From 733202d34eb42b478ba68ee5f251ee3d49871493 Mon Sep 17 00:00:00 2001 From: "Yount, Chuck" Date: Mon, 8 Apr 2024 13:11:30 -0700 Subject: [PATCH 2/3] Remove compiler warnings. --- src/contrib/coefficients/fd_coeff.cpp | 2 +- src/kernel/lib/halo.cpp | 5 ++--- src/kernel/lib/settings.hpp | 4 ++++ src/kernel/lib/setup.cpp | 12 ++++++++---- 4 files changed, 15 insertions(+), 8 deletions(-) diff --git a/src/contrib/coefficients/fd_coeff.cpp b/src/contrib/coefficients/fd_coeff.cpp index 8ff551e8..59b8f12d 100644 --- a/src/contrib/coefficients/fd_coeff.cpp +++ b/src/contrib/coefficients/fd_coeff.cpp @@ -58,7 +58,7 @@ namespace yask { int m_idx = (order+1)*num_points; int n_idx = num_points; int dsz = m_idx * n_idx; - double d[dsz]; + vector d(dsz); for (int i = 1; i < dsz; i++) d[i] = 0.0; diff --git a/src/kernel/lib/halo.cpp b/src/kernel/lib/halo.cpp index e28300ec..8439de43 100644 --- a/src/kernel/lib/halo.cpp +++ b/src/kernel/lib/halo.cpp @@ -516,13 +516,12 @@ namespace yask { auto* var_send_reqs = var_mpi_data.send_reqs.data(); auto* var_recv_stats = var_mpi_data.recv_stats.data(); auto* var_send_stats = var_mpi_data.send_stats.data(); + auto* stats = var_mpi_data.stats.data(); + auto* indices = var_mpi_data.indices.data(); #ifndef USE_INDIV_MPI_TESTS // Bulk testing. - auto asize = max(var_mpi_data.recv_reqs.size(), var_mpi_data.send_reqs.size()); - int indices[asize]; - MPI_Status stats[asize]; int n = 0; MPI_Testsome(int(var_mpi_data.recv_reqs.size()), var_recv_reqs, &n, indices, stats); num_tests++; diff --git a/src/kernel/lib/settings.hpp b/src/kernel/lib/settings.hpp index 026f72af..fd4e3234 100644 --- a/src/kernel/lib/settings.hpp +++ b/src/kernel/lib/settings.hpp @@ -570,6 +570,8 @@ namespace yask { std::vector send_reqs; std::vector recv_stats; std::vector send_stats; + std::vector stats; + std::vector indices; MPIData(MPIInfoPtr mpi_info) : _mpi_info(mpi_info) { @@ -586,6 +588,8 @@ namespace yask { memset(&nullst, 0, sizeof(nullst)); recv_stats.resize(n, nullst); send_stats.resize(n, nullst); + stats.resize(n, nullst); + indices.resize(n, 0); } void reset_locks() { diff --git a/src/kernel/lib/setup.cpp b/src/kernel/lib/setup.cpp index 79212fff..ccc8870c 100644 --- a/src/kernel/lib/setup.cpp +++ b/src/kernel/lib/setup.cpp @@ -254,8 +254,12 @@ namespace yask { rank_domain_offsets.set_vals_same(0); // Tables to share data across ranks. - idx_t coords[nr][nddims]; // rank indices. - idx_t rsizes[nr][nddims]; // rank sizes. + vector> coords(nr); // rank indices. + vector> rsizes(nr); // rank sizes. + for (int rn = 0; rn < nr; rn++) { + coords[rn].resize(nddims); + rsizes[rn].resize(nddims); + } // Two passes over ranks: // 0: sum all specified local sizes. @@ -274,9 +278,9 @@ namespace yask { // Exchange coord and size info between all ranks. for (int rn = 0; rn < nr; rn++) { - MPI_Bcast(&coords[rn][0], nddims, MPI_INTEGER8, + MPI_Bcast(coords[rn].data(), nddims, MPI_INTEGER8, rn, env->comm); - MPI_Bcast(&rsizes[rn][0], nddims, MPI_INTEGER8, + MPI_Bcast(rsizes[rn].data(), nddims, MPI_INTEGER8, rn, env->comm); } // Now, the tables are filled in for all ranks. From f2303740c3e09f3490cc11d72857c5a2ee65cfa6 Mon Sep 17 00:00:00 2001 From: "Yount, Chuck" Date: Wed, 10 Apr 2024 11:59:12 -0700 Subject: [PATCH 3/3] Update tutorial and README to indicate GA script removal. --- README.md | 2 ++ docs/YASK-tutorial.pdf | Bin 1850706 -> 1548297 bytes 2 files changed, 2 insertions(+) diff --git a/README.md b/README.md index 6d4c477f..c9167eb0 100644 --- a/README.md +++ b/README.md @@ -68,6 +68,8 @@ and Intel(R) graphics processors. ## Backward-compatibility notices ### Version 4 +* Version 4.05.00 removes the "out-of-band" genetic-algorithm tuning script + due to lack of resources for maintenance and testing. * Version 4.04.00 deprecates the existing `void* {set,get}_elements_in_slice()` APIs and provides safer `float*` and `double*` versions. * Version 4.03.00 is a significant release with the following notices: diff --git a/docs/YASK-tutorial.pdf b/docs/YASK-tutorial.pdf index 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