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#!/usr/bin/env python3
#
# BLIS
# An object-based framework for developing high-performance BLAS-like
# libraries.
#
# Copyright (C) 2018, The University of Texas at Austin
# Copyright (C) 2018 - 2019, Advanced Micro Devices, Inc.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
# - Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# - Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# - Neither the name(s) of the copyright holder(s) nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#
# Import modules
import os
import sys
import getopt
import re
import subprocess
import time
import statistics
def print_usage():
my_print( " " )
my_print( " %s" % script_name )
my_print( " " )
my_print( " Field G. Van Zee" )
my_print( " " )
my_print( " Repeatedly run a test driver and accumulate statistics for the" )
my_print( " output." )
my_print( " " )
my_print( " Usage:" )
my_print( " " )
my_print( " %s [options] drivername" % script_name )
my_print( " " )
my_print( " Arguments:" )
my_print( " " )
my_print( " drivername The filename/path of the test driver to run. The" )
my_print( " test driver must output its performance data to" )
my_print( " standard output." )
my_print( " " )
my_print( " The following options are accepted:" )
my_print( " " )
my_print( " -c num performance column index" )
my_print( " Find the performance result in column index <num> of" )
my_print( " the test driver's output. Here, a column is defined" )
my_print( " as a contiguous sequence of non-whitespace characters," )
my_print( " with the column indices beginning at 0. By default," )
my_print( " the second-to-last column index in the output is used." )
my_print( " " )
my_print( " -d delay sleep() delay" )
my_print( " Wait <delay> seconds after each execution of the" )
my_print( " test driver. The default delay is 0." )
my_print( " " )
my_print( " -n niter number of iterations" )
my_print( " Execute the test driver <niter> times. The default" )
my_print( " value is 10." )
my_print( " " )
my_print( " -q quiet; summary only" )
my_print( " Do not output statistics after every new execution of" )
my_print( " the test driver; instead, only output the final values" )
my_print( " after all iterations are complete. The default is to" )
my_print( " output updated statistics after each iteration." )
my_print( " " )
my_print( " -h help" )
my_print( " Output this information and exit." )
my_print( " " )
# ------------------------------------------------------------------------------
def my_print( s ):
sys.stdout.write( "%s\n" % s )
#sys.stdout.flush()
# ------------------------------------------------------------------------------
# Global variables.
script_name = None
output_name = None
def main():
global script_name
global output_name
# Obtain the script name.
path, script_name = os.path.split(sys.argv[0])
output_name = script_name
# Default values for optional arguments.
#perf_col = 9
perf_col = -1
delay = 0
niter = 10
quiet = False
# Process our command line options.
try:
opts, args = getopt.getopt( sys.argv[1:], "c:d:n:hq" )
except getopt.GetoptError as err:
# print help information and exit:
my_print( str(err) ) # will print something like "option -a not recognized"
print_usage()
sys.exit(2)
for opt, optarg in opts:
if opt == "-c":
perf_col = optarg
elif opt == "-d":
delay = optarg
elif opt == "-n":
niter = optarg
elif opt == "-q":
quiet = True
elif opt == "-h":
print_usage()
sys.exit()
else:
print_usage()
sys.exit()
# Print usage if we don't have exactly one argument.
if len( args ) != 1:
print_usage()
sys.exit()
# Acquire our only mandatory argument: the name of the test driver.
driverfile = args[0]
#my_print( "test driver: %s" % driverfile )
#my_print( "column num: %s" % perf_col )
#my_print( "delay: %s" % delay )
#my_print( "num iter: %s" % niter )
# Build a list of iterations.
iters = range( int(niter) )
# Run the test driver once to detect the number of lines of output.
p = subprocess.run( driverfile, stdout=subprocess.PIPE )
lines0 = p.stdout.decode().splitlines()
num_lines0 = int(len(lines0))
# Initialize the list of lists (one list per performance result).
aperf = []
for i in range( num_lines0 ):
aperf.append( [] )
for it in iters:
# Run the test driver.
p = subprocess.run( driverfile, stdout=subprocess.PIPE )
# Acquire the lines of output.
lines = p.stdout.decode().splitlines()
# Accumulate the test driver's latest results into aperf.
for i in range( num_lines0 ):
# Parse the current line to find the performance value.
line = lines[i]
words = line.split()
if perf_col == -1:
perf = words[ len(words)-2 ]
else:
perf = words[ int(perf_col) ]
# As unlikely as it is, guard against Inf and NaN.
if float(perf) == float('Inf') or \
float(perf) == -float('Inf') or \
float(perf) == float('NaN'): perf = 0.0
# Add the performance value to the list at the ith entry of aperf.
aperf[i].append( float(perf) )
# Compute stats for the current line.
avgp = statistics.mean( aperf[i] )
maxp = max( aperf[i] )
minp = min( aperf[i] )
# Only compute stdev() when we have two or more data points.
if len( aperf[i] ) > 1: stdp = statistics.stdev( aperf[i] )
else: stdp = 0.0
# Construct a string to match the performance value and then
# use that string to search-and-replace with four format specs
# for the min, avg, max, and stdev values computed above.
search = '%8s' % perf
newline = re.sub( str(search), ' %7.2f %7.2f %7.2f %6.2f', line )
# Search for the column index range that would be present if this were
# matlab-compatible output. The index range will typically be 1:n,
# where n is the number of columns of data.
found_index = False
for word in words:
if re.match( '1:', word ):
index_str = word
found_index = True
break
# If we find the column index range, we need to update it to reflect
# the replacement of one column of data with four, for a net increase
# of columns. We do so via another instance of re.sub() in which we
# search for the old index string and replace it with the new one.
if found_index:
last_col = int(index_str[2]) + 3
new_index_str = '1:%1s' % last_col
newline = re.sub( index_str, new_index_str, newline )
# If the quiet flag was not give, output the intermediate results.
if not quiet:
print( newline % ( float(minp), float(avgp), float(maxp), float(stdp) ) )
# Flush stdout after each set of output prior to sleeping.
sys.stdout.flush()
# Sleep for a bit until the next iteration.
time.sleep( int(delay) )
# If the quiet flag was given, output the final results.
if quiet:
for i in range( num_lines0 ):
# Parse the current line to find the performance value (only
# needed for call to re.sub() below).
line = lines0[i]
words = line.split()
if perf_col == -1:
perf = words[ len(words)-2 ]
else:
perf = words[ int(perf_col) ]
# Compute stats for the current line.
avgp = statistics.mean( aperf[i] )
maxp = max( aperf[i] )
minp = min( aperf[i] )
# Only compute stdev() when we have two or more data points.
if len( aperf[i] ) > 1: stdp = statistics.stdev( aperf[i] )
else: stdp = 0.0
# Construct a string to match the performance value and then
# use that string to search-and-replace with four format specs
# for the min, avg, max, and stdev values computed above.
search = '%8s' % perf
newline = re.sub( str(search), ' %7.2f %7.2f %7.2f %6.2f', line )
# Search for the column index range that would be present if this were
# matlab-compatible output. The index range will typically be 1:n,
# where n is the number of columns of data.
found_index = False
for word in words:
if re.match( '1:', word ):
index_str = word
found_index = True
break
# If we find the column index range, we need to update it to reflect
# the replacement of one column of data with four, for a net increase
# of columns. We do so via another instance of re.sub() in which we
# search for the old index string and replace it with the new one.
if found_index:
last_col = int(index_str[2]) + 3
new_index_str = '1:%1s' % last_col
newline = re.sub( index_str, new_index_str, newline )
# Output the results for the current line.
print( newline % ( float(minp), float(avgp), float(maxp), float(stdp) ) )
# Flush stdout afterwards.
sys.stdout.flush()
# Return from main().
return 0
if __name__ == "__main__":
main()
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