[Darshan-commits] [Git][darshan/darshan][apmpi-pydarshan-fixes] autoperf few fixes and code cleanup
Sudheer Chunduri
xgitlab at cels.anl.gov
Thu Apr 1 16:17:10 CDT 2021
Sudheer Chunduri pushed to branch apmpi-pydarshan-fixes at darshan / darshan
Commits:
7e18ce28 by Sudheer Chunduri at 2021-04-01T21:16:57+00:00
autoperf few fixes and code cleanup
- - - - -
3 changed files:
- darshan-util/pydarshan/darshan/backend/cffi_backend.py
- darshan-util/pydarshan/darshan/report.py
- − darshan-util/pydarshan/examples/apmpi_analysis.py
Changes:
=====================================
darshan-util/pydarshan/darshan/backend/cffi_backend.py
=====================================
@@ -421,6 +421,7 @@ def counter_names(mod_name, fcnts=False, special=''):
break
names.append(name)
i += 1
+
return names
=====================================
darshan-util/pydarshan/darshan/report.py
=====================================
@@ -696,6 +696,56 @@ class DarshanReport(object):
pass
+ def mod_read_all_apxc_records(self, mod, dtype=None, warnings=True):
+ """
+ Reads all APXC records for provided module.
+
+ Args:
+ mod (str): Identifier of module to fetch all records
+ dtype (str): 'numpy' for ndarray (default), 'dict' for python dictionary
+
+ Return:
+ None
+
+ """
+ if mod not in self.data['modules']:
+ if warnings:
+ logger.warning(f"Skipping. Log does not contain data for mod: {mod}")
+ return
+
+ supported = ['APXC']
+ if mod not in supported:
+ if warnings:
+ logger.warning(f" Skipping. Unsupported module: {mod} in in mod_read_all_apxc_records(). Supported: {supported}")
+ # skip mod
+ return
+
+ # handling options
+ dtype = dtype if dtype else self.dtype
+
+ self.records[mod] = DarshanRecordCollection(mod=mod, report=self)
+ cn = backend.counter_names(mod)
+
+ # update module metadata
+ self._modules[mod]['num_records'] = 0
+ if mod not in self.counters:
+ self.counters[mod] = {}
+
+ # fetch records
+ # fetch header record
+ rec = backend.log_get_apxc_record(self.log, mod, "HEADER", dtype=dtype)
+ while rec != None:
+ self.records[mod].append(rec)
+ self.data['modules'][mod]['num_records'] += 1
+
+ # fetch next
+ rec = backend.log_get_apxc_record(self.log, mod, "PERF", dtype=dtype)
+
+ if self.lookup_name_records:
+ self.update_name_records()
+
+ pass
+
def mod_read_all_dxt_records(self, mod, dtype=None, warnings=True, reads=True, writes=True):
"""
Reads all dxt records for provided module.
=====================================
darshan-util/pydarshan/examples/apmpi_analysis.py deleted
=====================================
@@ -1,190 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# # DarshanUtils for Python for processing APMPI records
-#
-# This notebook gives an overwiew of features provided by the Python bindings for DarshanUtils.
-
-# By default all AMMPI module records, metadata, and the name records are loaded when opening a Darshan log:
-
-import argparse
-import darshan
-import cffi
-import numpy
-import pandas
-import matplotlib
-#import pprint
-import pandas as pd
-import logging
-
-from darshan.backend.cffi_backend import ffi
-
-logger = logging.getLogger(__name__)
-from darshan.report import DarshanReport
-import darshan.backend.cffi_backend as backend
-import darshan
-import pandas as pd
-import time
-'''
-from rich import print as rprint
-from rich import pretty
-from rich.panel import Panel
-from rich import inspect
-from rich.color import Color
-from rich.console import Console
-console = Console()
-'''
-from matplotlib.backends.backend_pdf import FigureCanvasPdf, PdfPages
-from matplotlib.figure import Figure
-
-#pp = pprint.PrettyPrinter()
-#pretty.install()
-#color = Color.parse("blue")
-
-#inspect(color, methods=True)
-
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--quiet",
- dest="quiet",
- action="store_true",
- default=False,
- help="Surpress zero count calls",
- )
- parser.add_argument(
- "logname", metavar="logname", type=str, nargs=1, help="Logname to parse"
- )
- args = parser.parse_args()
-
- report = darshan.DarshanReport(args.logname[0], read_all=False)
- report.info()
-
- if "APMPI" not in report.modules:
- print("This log does not contain AutoPerf MPI data")
- return
- r = report.mod_read_all_apmpi_records("APMPI")
-
- report.update_name_records()
- report.info()
-
- pdf = matplotlib.backends.backend_pdf.PdfPages("apmpi_output.pdf")
-
- header_rec = report.records["APMPI"][0]
- print("# darshan log version: ", header_rec["version"])
- sync_flag = header_rec["sync_flag"]
- print(
- "APMPI Variance in total mpi time: ", header_rec["variance_total_mpitime"], "\n"
- )
- if sync_flag:
- print(
- "APMPI Variance in total mpi sync time: ",
- header_rec["variance_total_mpisynctime"],
- )
-
- df_apmpi = pd.DataFrame()
- list_mpiop = []
- list_rank = []
- for rec in report.records["APMPI"][
- 1:
- ]: # skip the first record which is header record
- mpi_nonzero_callcount = []
- for k, v in rec["all_counters"].items():
- if k.endswith("_CALL_COUNT") and v > 0:
- mpi_nonzero_callcount.append(k[: -(len("CALL_COUNT"))])
-
- df_rank = pd.DataFrame()
- for mpiop in mpi_nonzero_callcount:
- ncall = mpiop
- ncount = mpiop + "CALL_COUNT"
- nsize = mpiop + "TOTAL_BYTES"
- h0 = mpiop + "MSG_SIZE_AGG_0_256"
- h1 = mpiop + "MSG_SIZE_AGG_256_1K"
- h2 = mpiop + "MSG_SIZE_AGG_1K_8K"
- h3 = mpiop + "MSG_SIZE_AGG_8K_256K"
- h4 = mpiop + "MSG_SIZE_AGG_256K_1M"
- h5 = mpiop + "MSG_SIZE_AGG_1M_PLUS"
- ntime = mpiop + "TOTAL_TIME"
- mintime = mpiop + "MIN_TIME"
- maxtime = mpiop + "MAX_TIME"
- if sync_flag:
- totalsync = mpiop + "TOTAL_SYNC_TIME"
-
- mpiopstat = {}
- mpiopstat["Rank"] = rec["rank"]
- mpiopstat["Node_ID"] = rec["node_name"]
- mpiopstat["Call"] = ncall[:-1]
- mpiopstat["Total_Time"] = rec["all_counters"][ntime]
- mpiopstat["Count"] = rec["all_counters"][ncount]
- mpiopstat["Total_Bytes"] = rec["all_counters"].get(nsize, None)
- mpiopstat["[0-256B]"] = rec["all_counters"].get(h0, None)
- mpiopstat["[256-1KB]"] = rec["all_counters"].get(h1, None)
- mpiopstat["[1K-8KB]"] = rec["all_counters"].get(h2, None)
- mpiopstat["[8K-256KB]"] = rec["all_counters"].get(h3, None)
- mpiopstat["256K-1MB"] = rec["all_counters"].get(h4, None)
- mpiopstat["[>1MB]"] = rec["all_counters"].get(h5, None)
- mpiopstat["Min_Time"] = rec["all_counters"][mintime]
- mpiopstat["Max_Time"] = rec["all_counters"][maxtime]
- if sync_flag:
- mpiopstat["Total_SYNC_Time"] = rec["all_counters"][totalsync]
-
- list_mpiop.append(mpiopstat)
- rankstat = {}
- rankstat["Rank"] = rec["rank"]
- rankstat["Node_ID"] = rec["node_name"]
- rankstat["Call"] = "Total_MPI_time"
- rankstat["Total_Time"] = rec["all_counters"]["RANK_TOTAL_MPITIME"]
- list_rank.append(rankstat)
- df_rank = pd.DataFrame(list_rank)
- avg_total_time = df_rank["Total_Time"].mean()
- max_total_time = df_rank["Total_Time"].max()
- min_total_time = df_rank["Total_Time"].min()
- max_rank = df_rank.loc[df_rank["Total_Time"].idxmax()]["Rank"]
- min_rank = df_rank.loc[df_rank["Total_Time"].idxmin()]["Rank"]
- # assumption: row index and rank id are same in df_rank
- # .. need to check if that is an incorrect assumption
- mean_rank = (
- (df_rank["Total_Time"] - df_rank["Total_Time"].mean()).abs().argsort()[:1][0]
- )
-
- list_combined = list_mpiop + list_rank
- df_apmpi = pd.DataFrame(list_combined)
- df_apmpi = df_apmpi.sort_values(by=["Rank", "Total_Time"], ascending=[True, False])
- print("[bold green] MPI stats for rank with maximum MPI time")#, border_style="blue")
- print("[bold green] MPI stats for rank with maximum MPI time\n", df_apmpi.loc[df_apmpi["Rank"] == max_rank])
- print("[bold green] MPI stats for rank with minimum MPI time")# border_style="blue")
- print(df_apmpi.loc[df_apmpi["Rank"] == min_rank])
- print("[bold green] MPI stats for rank with mean MPI time")#, border_style="blue")
- print(df_apmpi.loc[df_apmpi["Rank"] == mean_rank])
- # print(df_apmpi)
- df_apmpi.to_csv('apmpi.csv', index=False)
- fig = Figure()
- ax = fig.gca()
- ax.plot(df_rank["Rank"], df_rank["Total_Time"])
- ax.set_xlabel("Rank")
- ax.set_ylabel("MPI Total time(s)")
- canvas = FigureCanvasPdf(fig)
- canvas.print_figure(pdf)
- fig = Figure()
- ax = fig.gca()
- #fig2.plot(df_apmpi.loc[df_apmpi["Rank"] == max_rank])
- ax.plot(df_apmpi.loc[df_apmpi["Rank"] == max_rank]["Call"], df_apmpi.loc[df_apmpi["Rank"] == max_rank]["Total_Time"])
- ax.set_xlabel("MPI OP")
- ax.set_ylabel("Total time(s)")
- canvas = FigureCanvasPdf(fig)
- #canvas.print_figure(pdf)
- fig = Figure()
- ax = fig.gca()
- ax.plot(df_apmpi.loc[df_apmpi["Rank"] == min_rank]["Call"], df_apmpi.loc[df_apmpi["Rank"] == min_rank]["Total_Time"])
- ax.set_xlabel("MPI OP")
- ax.set_ylabel("Total time(s)")
- ax.set_title("Min rank MPI times")
- canvas = FigureCanvasPdf(fig)
- #canvas.print_figure(pdf)
- #fig3.plot(df_apmpi.loc[df_apmpi["Rank"] == min_rank])
- return
-
-
-if __name__ == "__main__":
- main()
View it on GitLab: https://xgitlab.cels.anl.gov/darshan/darshan/-/commit/7e18ce2846ad6a4d3602c7152af26203f972236f
--
View it on GitLab: https://xgitlab.cels.anl.gov/darshan/darshan/-/commit/7e18ce2846ad6a4d3602c7152af26203f972236f
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