261 lines
9.1 KiB
Python
261 lines
9.1 KiB
Python
import data_stat
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from data_stat import Stat, Cpu, Format, MatrixType
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import argparse
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import os, sys
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import subprocess, signal
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import json
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import time
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parser = argparse.ArgumentParser()
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parser.add_argument('cpu', choices=[x.name.lower() for x in Cpu])
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parser.add_argument('matrix_type', type=str,
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choices=[t.name.lower() for t in MatrixType])
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parser.add_argument('format', type=str,
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choices=[fmt.name.lower() for fmt in Format])
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parser.add_argument('base_iterations', type=int)
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parser.add_argument('min_time_s', type=int)
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parser.add_argument('baseline_time_s', type=int)
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parser.add_argument('baseline_delay_s', type=int)
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#parser.add_argument('--perf', action='store_true')
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parser.add_argument('-m', '--matrix_file', type=str)
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parser.add_argument('-ss', '--synthetic_size', type=int)
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parser.add_argument('-sd', '--synthetic_density', type=float)
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parser.add_argument('-c', '--cores', type=int)
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parser.add_argument('--power', action='store_true')
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parser.add_argument('-d', '--debug', action='store_true')
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args = parser.parse_args()
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args.cpu = Cpu[args.cpu.upper()]
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args.matrix_type = MatrixType[args.matrix_type.upper()]
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args.format = Format[args.format.upper()]
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assert args.baseline_time_s >= 2
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python = {
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Cpu.ALTRA: 'python3',
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Cpu.EPYC_7313P: 'python3.11',
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Cpu.XEON_4216: 'python3.11'
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}
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perf = ['perf', 'stat']
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perf_args = {
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Cpu.ALTRA: [
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['-d', '-d'],
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['-M', 'branch_misprediction_ratio'],
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['-M', 'dtlb_walk_ratio,itlb_walk_ratio'],
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['-M', 'l1d_cache_miss_ratio,l1i_cache_miss_ratio'],
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['-M', 'l2_cache_miss_ratio,l2_tlb_miss_ratio,ll_cache_read_miss_ratio']]
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}
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power = ['taskset', '-c', '0', './power.sh']
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def program(
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cpu: Cpu,
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cores: int,
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matrix_type: MatrixType,
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fmt: Format,
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iterations: int,
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matrix_file: str,
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synthetic_size: int,
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synthetic_density: float
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) -> list:
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apptainer = ['apptainer', 'run']
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if cores is not None:
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apptainer += [ '--env', 'OMP_PROC_BIND=true',
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'--env', 'OMP_PLACES={0:' + f'{cores}' + '}']
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spmv = f'python3 spmv.py {matrix_type.name.lower()} {fmt.name.lower()} '
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spmv += f'{iterations} '
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if matrix_type == MatrixType.SUITESPARSE:
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spmv += f'-m {matrix_file}'
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elif matrix_type == MatrixType.SYNTHETIC:
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spmv += f'-ss {synthetic_size} -sd {synthetic_density}'
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else:
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exit("Unrecognized matrix type!")
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if cores is not None:
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spmv += f' -c {cores}'
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if cpu == Cpu.ALTRA:
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return apptainer + ['pytorch-altra.sif', '-c',
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'numactl --cpunodebind=0 --membind=0 ' + spmv]
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elif cpu == Cpu.EPYC_7313P:
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return apptainer + ['pytorch-epyc_7313p.sif'] + spmv.split(' ')
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elif cpu == Cpu.XEON_4216:
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return apptainer + ['pytorch-xeon_4216.sif',
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'numactl', '--cpunodebind=0', '--membind=0'] + spmv.split(' ')
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def baseline_power(cpu: Cpu, baseline_time_s: int) -> list:
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power_process = subprocess.Popen(power + [str(baseline_time_s)],
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stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
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return [float(x) for x in power_process.communicate()[0].strip().split('\n') if len(x) != 0]
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def run_program(program: list[str]) -> tuple[dict, str]:
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if args.debug:
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print(program, file=sys.stderr)
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process = subprocess.run(program,
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stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
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process.check_returncode()
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if args.debug:
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print(process.stdout, file=sys.stderr)
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print(process.stderr, file=sys.stderr)
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return (json.loads(process.stdout), process.stderr)
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def trapezoidal_rule(power: list[float], time_s: float) -> float:
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result = 0.0
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#relevant_power = power[-int(time_s):]
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relevant_power = power
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assert(time_s >= 2)
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assert(len(relevant_power) >= 2)
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#assert(len(power) >= time_s)
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for pair in zip(relevant_power, relevant_power[1:]):
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result += 0.5 * (pair[0] + pair[1])
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result += (time_s % 1) * (power[-1])
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return result
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result = dict()
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result[Stat.CPU.name] = args.cpu.value
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if args.cores is not None:
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result[Stat.CORES.name] = args.cores
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else:
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if args.cpu == Cpu.ALTRA:
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result[Stat.CORES.name] = 80
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elif args.cpu == Cpu.EPYC_7313P:
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result[Stat.CORES.name] = 16
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elif args.cpu == Cpu.XEON_4216:
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result[Stat.CORES.name] = 16
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iterations = args.base_iterations
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program_result = run_program(program(
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args.cpu, args.cores, args.matrix_type, args.format, iterations,
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args.matrix_file, args.synthetic_size, args.synthetic_density))
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while program_result[0][Stat.TIME_S.name] < args.min_time_s:
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# Increase the number of iterations by difference between the current time taken and the desired time.
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iterations *= 1 / (program_result[0][Stat.TIME_S.name] / args.min_time_s)
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# Add another 5% for safety.
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iterations += iterations * 0.05
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iterations = int(iterations)
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program_result = run_program(program(
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args.cpu, args.cores, args.matrix_type, args.format, iterations,
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args.matrix_file, args.synthetic_size, args.synthetic_density))
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result[Stat.ITERATIONS.name] = iterations
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result |= program_result[0]
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print(program_result[1], file=sys.stderr)
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result[Stat.TIME_S_1KI.name] = (
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(result[Stat.TIME_S.name] / result[Stat.ITERATIONS.name]) * 1000
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)
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if args.power:
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result[Stat.BASELINE_TIME_S.name] = args.baseline_time_s
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result[Stat.BASELINE_DELAY_S.name] = args.baseline_delay_s
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# Baseline
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time.sleep(args.baseline_delay_s)
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baseline_list = baseline_power(args.cpu, args.baseline_time_s)
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if args.debug:
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print(baseline_list, file=sys.stderr)
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assert(len(baseline_list) == args.baseline_time_s)
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# Power Collection
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power_process = subprocess.run(
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power + ['-1'] + program(
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args.cpu, args.cores, args.matrix_type, args.format,
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result[Stat.ITERATIONS.name],
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args.matrix_file, args.synthetic_size, args.synthetic_density),
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stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
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power_process.check_returncode()
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power_list = [float(x)
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for x in power_process.stdout.strip().split('\n')]
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power_time_s = json.loads(power_process.stderr)[Stat.TIME_S.name]
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if args.debug:
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print(power_list, file=sys.stderr)
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print(power_time_s, file=sys.stderr)
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if args.cpu == Cpu.ALTRA:
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# Trapezoidal Rule across the last (s) power recordings.
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result[Stat.J.name] = trapezoidal_rule(
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power_list, power_time_s)
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elif args.cpu == Cpu.EPYC_7313P or args.cpu == Cpu.XEON_4216:
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result[Stat.J.name] = power_list[0] * power_time_s
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result[Stat.W.name] = result[Stat.J.name] / power_time_s
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if args.debug:
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print(result, file=sys.stderr)
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#print(len(result['power']))
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#print(sum(result['power']) / len(result['power']))
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# Baseline
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time.sleep(args.baseline_delay_s)
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baseline_list += baseline_power(args.cpu, args.baseline_time_s)
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if args.debug:
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print(baseline_list, file=sys.stderr)
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assert(len(baseline_list) / 2 == args.baseline_time_s)
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baseline_joules = (
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trapezoidal_rule(
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baseline_list[:args.baseline_time_s],
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args.baseline_time_s) +
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trapezoidal_rule(
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baseline_list[args.baseline_time_s:],
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args.baseline_time_s)
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)
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baseline_wattage = baseline_joules / (args.baseline_time_s * 2)
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if args.debug:
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print(baseline_joules, file=sys.stderr)
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print(baseline_wattage, file=sys.stderr)
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result[Stat.J_1KI.name] = (
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(result[Stat.J.name] / result[Stat.ITERATIONS.name]) * 1000
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)
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result[Stat.W_1KI.name] = (
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(result[Stat.W.name] / result[Stat.ITERATIONS.name]) * 1000
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)
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result[Stat.W_D.name] = result[Stat.W.name] - baseline_wattage
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result[Stat.J_D.name] = result[Stat.W_D.name] * power_time_s
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result[Stat.W_D_1KI.name] = (
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(result[Stat.W_D.name] / result[Stat.ITERATIONS.name]) * 1000
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)
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result[Stat.J_D_1KI.name] = (
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(result[Stat.W_D_1KI.name] / result[Stat.ITERATIONS.name]) * 1000
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)
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if args.debug:
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print(result, file=sys.stderr)
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print(json.dumps(result))
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#if args.perf:
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# for perf_arg in perf_args[args.cpu]:
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# output = run_program(perf + perf_arg + program[args.cpu])[1]
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# print(output, file=sys.stderr)
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# result = result | data_stat.parse_output(output, args.cpu)
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# if args.debug:
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# print(result)
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#
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# result = result | data_stat.derive_stats(result)
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#
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# if args.debug:
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# print(result)
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#arch = subprocess.run(['uname', '-m'], stdout=subprocess.PIPE, text=True).stdout.strip()
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#baseline = subprocess.run(
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# ['./power.sh', args.baseline_time_s],
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# stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
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#print(baseline)
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#for line in baseline.stdout.split('\n'):
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# print("line")
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# print(line)
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#os.path.basename(args.matrix_file)
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#subprocess.run(
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# ['apptainer', 'run', 'pytorch-altra.sif', '-c',
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# f'"numactl --cpunodebind=0 --membind=0 python spmv.py {args.matrix_file} {args.iterations}"'
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# ],
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# stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
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