hawk-utils-scripts/performance/mpiP/share/mpip2POP.py

#!/usr/bin/env python3
import sys
import re
from operator import itemgetter
import logging

logging.basicConfig(stream=sys.stderr, level=logging.ERROR)

def parse_arguments():
    import argparse
    parser = argparse.ArgumentParser(
            description='Calculate POP metrics from a list of mpiP reports.')
    parser.add_argument('-s', '--scaling',
            choices=['strong','weak'], default='weak',
            help='Strong or weak scaling experiment')
    parser.add_argument('mpip_report', nargs='+', type=argparse.FileType('r'))
    
    return parser.parse_args()


def get_Nranks(fn): 
    import subprocess

    cmd = "grep '^@ MPI Task Assignment' %s | sed -n '$='" % (fn,)
    result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE)
    try:
        nranks = int(result.stdout)
    except ValueError:
        logging.warning("Cannot infer nranks from '@ MPI Task Assignment'.")
        nranks = None

    return {"ranks": nranks}

def get_times(report):
    logging.debug("Getting times from %s" % report.name)
    import re
    start_time_stats = re.compile("^@-+ Task Time Statistics")
    separator = re.compile("^-")
    header = re.compile(".*AppTime")
    maxline = re.compile("^Max ")
    meanline = re.compile("^Mean\s+([\.\d]+)\s+([\.\d]+)")
    minline = re.compile("^Min\s+([\.\d]+)\s+([\.\d]+)")
    stdline = re.compile("^Stddev ")
    aggrline = re.compile("^Aggregate\s+([\.\d]+)\s+([\.\d]+)")

    times = dict()

    # find start of time statistcs
    line = report.readline()
    while line:
        if start_time_stats.match(line):
            break
        line = report.readline()
    logging.debug("Done start ")

    # expect separator
    line = report.readline()
    if not separator.match(line):
        logging.error("Failed to parse expected separator.")
        return times
    logging.debug("Done separator")

    # expect header
    line = report.readline()
    if not header.match(line):
        logging.error("Failed to parse expected header.")
        return times
    logging.debug("Done header")

    # expect Max line
    line = report.readline()
    if not maxline.match(line):
        logging.error("Failed to parse expected Max line.")
        return times
    
    # expect and parse Mean line
    line = report.readline()
    m = meanline.match(line)
    if not m:
        logging.error("Failed to parse expected Mean line.")
        return times
    avg_app = float(m.group(1))
    avg_mpi = float(m.group(2))

    # expect and parse Min line
    line = report.readline()
    m = minline.match(line)
    if not m:
        logging.error("Failed to parse expected Min line.")
        return times
    min_mpi = float(m.group(2))     

    # expect Stddev line
    line = report.readline()
    if not stdline.match(line):
        logging.error("Failed to parse expected Stddev line.")
        return times
    
    # expect and parse Aggregate line
    line = report.readline()
    m = aggrline.match(line)
    if not m:
        logging.error("Failed to parse expected Aggregate line.")
        return times
    aggr_app = float(m.group(1))
    logging.debug("Done times")

    elapsed = avg_app
    times["elapsed"] = elapsed
    times["avg useful"] = elapsed - avg_mpi
    times["max useful"] = elapsed - min_mpi
    times["ranks_alt"] = round(aggr_app/avg_app)

    return times


def efficiencies(report):
    metrics = dict()

    metrics.update(get_Nranks(report.name))
    metrics.update(get_times(report))

    if metrics["ranks"] == None:
        metrics["ranks"] = metrics["ranks_alt"]

    metrics["PE"] = metrics["avg useful"]/metrics["elapsed"]
    metrics["LB"] = metrics["avg useful"]/metrics["max useful"]
    metrics["CE"] = metrics["max useful"]/metrics["elapsed"]

    return metrics

def scalabilities(metrics, strong):

    ref = metrics[0]

    nranks_ref = ref["ranks"]
    useful_ref = ref["avg useful"]
    
    for m in metrics: 
        scal = useful_ref/m["avg useful"]
        if strong:
            scal *= nranks_ref/m["ranks"]
        m["CScal"] = scal
        m["GE"] = m["PE"]*scal
    
    return metrics

def dump(metrics, stream=sys.stdout, sep=",", keys=None):
    if not keys:
        keys = [
            "ranks",
            "elapsed", "avg useful", "max useful", 
            "GE", "PE", "LB", "CE", "CScal"
        ]
    
    header = "# " + sep.join(keys) + "\n"
    stream.write(header)

    for m in metrics:
        line = sep.join([str(m[key]) for key in keys]) + "\n"
        stream.write(line)
        

def pretty_print(metrics, stream=sys.stdout):
    descriptions = {
        "GE":         " GE             ",
        "PE":         "   PE           ",
        "LB":         "     LB         ",
        "CE":         "     CE         ",
        "TrE":        "       TrE      ",
        "SerR":       "       SerE     ",
        "CScal":      "   CScal        ",
        "elapsed":    " Elapsed time   ",
        "avg useful": " Average useful ",
        "max useful": " Max useful     "
    }
    eff_keys = ["GE", "PE", "LB", "CE"]
    scal_keys = ["CScal"]
    other_keys = ["elapsed", "avg useful", "max useful"]
    separator = "|"

    width_desc = len(descriptions["GE"])
    width_col = 7
    width_separator = len(separator)

    ncols = len(metrics)
    skip = "-" * (width_desc + (width_separator+width_col)*ncols + width_separator)
    
    table = ""

    table += skip + "\n"

    table += " " * width_desc + separator
    for col in metrics:
        table += f'{col["ranks"]:^{width_col}d}' + separator
    table += "\n"
    table += skip + "\n"

    for key in eff_keys:
        line = descriptions[key] + separator
        for col in metrics:
            line +=  f'{col[key]:^{width_col}.2f}' + separator
        table += line + "\n"
    for key in scal_keys:
        line = descriptions[key] + separator
        for col in metrics:
            line +=  f'{col[key]:^{width_col}.2f}' + separator
        table += line + "\n" 
    table += skip + "\n"

    for key in other_keys:
        line = descriptions[key] + separator
        for col in metrics:
            line +=  f'{col[key]:^{width_col}.2g}' + separator
        table += line + "\n" 
    table += skip + "\n"    

    stream.write(table)

def print_scaling(scaling, stream=sys.stdout):

    stream.write(f'CScal was calculated for {scaling} scaling.\n')


def main():
    args = parse_arguments()

    metrics = list()

    # compute parallel efficiencies for each input
    for report in args.mpip_report:
        print("Processing file %s" % report.name)
        metrics.append(efficiencies(report))

    # sort metrics by ranks
    metrics.sort(key=itemgetter('ranks'))

    # compute scalabilities
    metrics = scalabilities(metrics, strong=args.scaling=='strong')

    dump(metrics)
    pretty_print(metrics)
    print_scaling(args.scaling)

if __name__ == "__main__":
    main()