xemu/scripts/performance/dissect.py
Ahmed Karaman 01afa757b6 scripts/performance: Add dissect.py script
Python script that dissects QEMU execution into three main phases:
code generation, JIT execution and helpers execution.

Syntax:
dissect.py [-h] -- <qemu executable> [<qemu executable options>] \
                 <target executable> [<target executable options>]

[-h] - Print the script arguments help message.

Example of usage:
dissect.py -- qemu-arm coulomb_double-arm

Example output:
Total Instructions:        4,702,865,362

Code Generation:             115,819,309	 2.463%
JIT Execution:             1,081,980,528	23.007%
Helpers:                   3,505,065,525	74.530%

Signed-off-by: Ahmed Karaman <ahmedkhaledkaraman@gmail.com>
Reviewed-by: Aleksandar Markovic <aleksandar.qemu.devel@gmail.com>
Reviewed-by: Philippe Mathieu-Daudé <philmd@redhat.com>
Message-Id: <20200709052055.2650-2-ahmedkhaledkaraman@gmail.com>
Signed-off-by: Philippe Mathieu-Daudé <philmd@redhat.com>
2020-07-14 22:22:22 +02:00

167 lines
6.6 KiB
Python
Executable File

#!/usr/bin/env python3
# Print the percentage of instructions spent in each phase of QEMU
# execution.
#
# Syntax:
# dissect.py [-h] -- <qemu executable> [<qemu executable options>] \
# <target executable> [<target executable options>]
#
# [-h] - Print the script arguments help message.
#
# Example of usage:
# dissect.py -- qemu-arm coulomb_double-arm
#
# This file is a part of the project "TCG Continuous Benchmarking".
#
# Copyright (C) 2020 Ahmed Karaman <ahmedkhaledkaraman@gmail.com>
# Copyright (C) 2020 Aleksandar Markovic <aleksandar.qemu.devel@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import argparse
import os
import subprocess
import sys
import tempfile
def get_JIT_line(callgrind_data):
"""
Search for the first instance of the JIT call in
the callgrind_annotate output when ran using --tree=caller
This is equivalent to the self number of instructions of JIT.
Parameters:
callgrind_data (list): callgrind_annotate output
Returns:
(int): Line number
"""
line = -1
for i in range(len(callgrind_data)):
if callgrind_data[i].strip('\n') and \
callgrind_data[i].split()[-1] == "[???]":
line = i
break
if line == -1:
sys.exit("Couldn't locate the JIT call ... Exiting.")
return line
def main():
# Parse the command line arguments
parser = argparse.ArgumentParser(
usage='dissect.py [-h] -- '
'<qemu executable> [<qemu executable options>] '
'<target executable> [<target executable options>]')
parser.add_argument('command', type=str, nargs='+', help=argparse.SUPPRESS)
args = parser.parse_args()
# Extract the needed variables from the args
command = args.command
# Insure that valgrind is installed
check_valgrind = subprocess.run(
["which", "valgrind"], stdout=subprocess.DEVNULL)
if check_valgrind.returncode:
sys.exit("Please install valgrind before running the script.")
# Save all intermediate files in a temporary directory
with tempfile.TemporaryDirectory() as tmpdirname:
# callgrind output file path
data_path = os.path.join(tmpdirname, "callgrind.data")
# callgrind_annotate output file path
annotate_out_path = os.path.join(tmpdirname, "callgrind_annotate.out")
# Run callgrind
callgrind = subprocess.run((["valgrind",
"--tool=callgrind",
"--callgrind-out-file=" + data_path]
+ command),
stdout=subprocess.DEVNULL,
stderr=subprocess.PIPE)
if callgrind.returncode:
sys.exit(callgrind.stderr.decode("utf-8"))
# Save callgrind_annotate output
with open(annotate_out_path, "w") as output:
callgrind_annotate = subprocess.run(
["callgrind_annotate", data_path, "--tree=caller"],
stdout=output,
stderr=subprocess.PIPE)
if callgrind_annotate.returncode:
sys.exit(callgrind_annotate.stderr.decode("utf-8"))
# Read the callgrind_annotate output to callgrind_data[]
callgrind_data = []
with open(annotate_out_path, 'r') as data:
callgrind_data = data.readlines()
# Line number with the total number of instructions
total_instructions_line_number = 20
# Get the total number of instructions
total_instructions_line_data = \
callgrind_data[total_instructions_line_number]
total_instructions = total_instructions_line_data.split()[0]
total_instructions = int(total_instructions.replace(',', ''))
# Line number with the JIT self number of instructions
JIT_self_instructions_line_number = get_JIT_line(callgrind_data)
# Get the JIT self number of instructions
JIT_self_instructions_line_data = \
callgrind_data[JIT_self_instructions_line_number]
JIT_self_instructions = JIT_self_instructions_line_data.split()[0]
JIT_self_instructions = int(JIT_self_instructions.replace(',', ''))
# Line number with the JIT self + inclusive number of instructions
# It's the line above the first JIT call when running with --tree=caller
JIT_total_instructions_line_number = JIT_self_instructions_line_number-1
# Get the JIT self + inclusive number of instructions
JIT_total_instructions_line_data = \
callgrind_data[JIT_total_instructions_line_number]
JIT_total_instructions = JIT_total_instructions_line_data.split()[0]
JIT_total_instructions = int(JIT_total_instructions.replace(',', ''))
# Calculate number of instructions in helpers and code generation
helpers_instructions = JIT_total_instructions-JIT_self_instructions
code_generation_instructions = total_instructions-JIT_total_instructions
# Print results (Insert commas in large numbers)
# Print total number of instructions
print('{:<20}{:>20}\n'.
format("Total Instructions:",
format(total_instructions, ',')))
# Print code generation instructions and percentage
print('{:<20}{:>20}\t{:>6.3f}%'.
format("Code Generation:",
format(code_generation_instructions, ","),
(code_generation_instructions / total_instructions) * 100))
# Print JIT instructions and percentage
print('{:<20}{:>20}\t{:>6.3f}%'.
format("JIT Execution:",
format(JIT_self_instructions, ","),
(JIT_self_instructions / total_instructions) * 100))
# Print helpers instructions and percentage
print('{:<20}{:>20}\t{:>6.3f}%'.
format("Helpers:",
format(helpers_instructions, ","),
(helpers_instructions/total_instructions)*100))
if __name__ == "__main__":
main()