PerfFlowAspect

PerfFlowAspect is a tool to analyze cross-cutting performance concerns of composite scientific workflows.

Introduction

High performance computing (HPC) researchers are increasingly introducing and composing disparate workflow-management technologies and components to create scalable end-to-end science workflows. These technologies have generally been developed in isolation and often feature widely varying levels of performance, scalability and interoperability. All things considered, optimizing the end-to-end workflow amidst those considerations is a highly daunting task and thus it requires effective performance analysis techniques and tools.

Unfortunately, there still is a paucity of techniques and tools that can analyze the end-to-end performance of such a composite workflow. While a myriad of analysis tools exist for traditional HPC programming paradigms (e.g., a single application running at scale), there has been a lack of studies and tools to understand the effectiveness and efficiency of this emerging workflow paradigm.

Enter PerfFlowAspect. It is a simple Aspect-Oriented Programming-based (AOP) tool that can cast a cross-cutting performance-analysis concern or aspect across a heterogeneous set of components (e.g, combining Maestro and a custom workflow pipeline with Flux along with microservices running on on-premises Kubernetes machines) used to create a modern-day composite science workflow.

PerfFlowAspect will provide multi-language support, particularly for those most relevant in HPC workflows including Python. It is designed specifically to allow researchers to weave the performance aspect into critical points of execution across many workflow components without having to lose the modularity and uniformity as to how performance is measured and controlled.

PerfFlowAspect Project Resources

Online Documentation https://perfflowaspect.readthedocs.io/

Github Source Repo https://github.com/flux-framework/PerfFlowAspect.git

Issue Tracker https://github.com/flux-framework/PerfFlowAspect/issues

Contributors

• Dong H. Ahn (NVIDIA)

• Stephanie Brink

• James Corbett

• Stephen Herbein (NVIDIA)

• Aliza Lisan (University of Oregon)

• Daniel Milroy

• Francesco Di Natale (NVIDIA)

• Tapasya Patki

• Jae-Seung Yeom

• Hariharan Devarajan

PerfFlowAspect Documentation

Basic Tutorial

PerfFlowAspect is based on Aspect-Oriented Programming (AOP). PerfFlowAspect relies on annotated functions in the user’s source code and can invoke specific performance-analysis actions, a piece of tracing code, etc. on those points of execution. In AOP, these trigger points are called join points in the source code, and the functionality invoked is called advice. To learn more about AOP and associated terminology, please refer to our presentation slides here.

The python package perfflowaspect contains the PerfFlowAspect tool for the Python language. The file src/python/perfflowaspect/aspect.py contains a key annotating decorator. Users can use the @perfflowaspect.aspect.critical_path() decorator to annotate their functions that are likely to be on the critical path of the workflow’s end-to-end performance. These annotated functions then serve as the join points that can be weaved with PerfFlowAspect to be acted upon. The decorator accepts the following pointcut values at the join points:

• before: The advice is invoked only before the join point.

• after: The advice is invoked only after the join point.

• around: The advice is invoked both before and after the join point.

The asynchronous versions of these pointcut values are also supported in PerfFlowAspect, which are: before_async, after_async, and around_async.

Note: The default pointcut value is around.

The following shows a simple snippet that annotates two functions.

import perfflowaspect.aspect


@perfflowaspect.aspect.critical_path()
def bar(message):
    time.sleep(1)
    print(message)


@perfflowaspect.aspect.critical_path()
def foo():
    time.sleep(2)
    bar("hello")


def main():
    foo()

Once annotated, running this python code will produce a performance trace data file named perfflow.<hostname>.<pid>. It uses Chrome Tracing Format in JSON so that it can be loaded into Google Chrome Tracing to render the critical path events on the global tracing timeline, using the Perfetto visualization tool. Details on these can be found at the links below:

• Chrome Tracing Tool: https://www.chromium.org/developers/how-tos/trace-event-profiling-tool/

• Perfetto Visualizer: https://perfetto.dev/

To disable all PerfFlowAspect annotations, set the PERFFLOW_OPTIONS="log-enable=" to False at runtime.

PERFFLOW_OPTIONS="log-enable=False" ./test/smoketest.py

PerfFlowAspect CLI Options

PerfFlowAspect options can be set with the PERFFLOW_OPTIONS environment variable. Separate multiple variables with a colon as follows:

PERFFLOW_OPTIONS="<var1>=<val1>:<var2>=<val2>" <executable>

Variable	Description	Default Value	Supported Values
name	Name of this workflow component	generic
log-filename-include	Customize name of log file	hostname,pid	name,instance-path,hostname,pid
log-dir	Directory where log file is created	./
log-enable	Toggle annotations on/off	True	True, False
cpu-mem-usage	Collect CPU and memory usage metrics	False	True, False
log-event	Collect B and E events (verbose) or single X event (compact)	Verbose	Verbose, Compact

Visualization of PerfFlowAspect Output Files

There are two types of logging allowed in PerfFlowAspect trace files which are verbose and compact. Either can be enabled by setting PERFFLOW_OPTIONS="log-event=" to compact or verbose, respectively. The logging is verbose by default. Verbose logging uses B (begin) and E (end) events in the trace file as shown below:

[
  {"name": "foo", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 3134, "tid": 3134, "ts": 1679127184455376.0, "ph": "B"},
  {"name": "bar", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 3134, "tid": 3134, "ts": 1679127184456525.0, "ph": "B"},
  {"name": "bas", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 3134, "tid": 3134, "ts": 1679127184457610.0, "ph": "B"},
  {"name": "bas", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 3134, "tid": 3134, "ts": 1679127184457636.0, "ph": "E"},
  {"name": "bar", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 3134, "tid": 3134, "ts": 1679127184457657.0, "ph": "E"},
  {"name": "foo", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 3134, "tid": 3134, "ts": 1679127184457676.0, "ph": "E"},
  ...
]

The above trace file is generated for three functions with around pointcut annotations. The same trace file will be reduced to half the lines with compact logging which uses a single X (complete) events, as can be seen below:

[
  {"name": "bas", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 2688, "tid": 2688, "ts": 1679127137181517.0, "ph": "X", "dur": 600.0},
  {"name": "bar", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 2688, "tid": 2688, "ts": 1679127137179879.0, "ph": "X", "dur": 2885.0},
  {"name": "foo", "cat": "/PerfFlowAspect/src/c/test/smoketest.cpp", "pid": 2688, "tid": 2688, "ts": 1679127137177783.0, "ph": "X", "dur": 5532.0},
  ...
]

The visualization of both types of logging in trace files will be the same in Perfetto UI. An example visualization is shown below:

Fig. 1: Visualization of a single process, single thread program in Perfetto UI

The visualization in Fig. 1 is of the following python program:

#!/usr/bin/env python

import time
import perfflowaspect
import perfflowaspect.aspect


@perfflowaspect.aspect.critical_path(pointcut="around")
def bas():
   print("bas")


@perfflowaspect.aspect.critical_path(pointcut="around")
def bar():
   print("bar")
   time.sleep(0.001)
   bas()


@perfflowaspect.aspect.critical_path()
def foo(msg):
   print("foo")
   time.sleep(0.001)
   bar()
   if msg == "hello":
      return 1
   return 0


def main():
   print("Inside main")
   for i in range(4):
      foo("hello")
   return 0


if __name__ == "__main__":
   main()

PerfFlowAspect also allows the user to log CPU and memory usage of annotated functions by setting PERFFLOW_OPTIONS="cpu-mem-usage=" to True at runtime. The trace file, in that case, will have the following structure with compact logging enabled:

[
  {"name": "bas", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351167907.0, "ph": "C", "args": {"cpu_usage": 0.0, "memory_usage": 10944}},
  {"name": "bas", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351168628.0, "ph": "C", "args": {"cpu_usage": 0.0, "memory_usage": 0}},
  {"name": "bas", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351167907.0, "ph": "X", "dur": 721.0},
  {"name": "bar", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351167127.0, "ph": "C", "args": {"cpu_usage": 11.980575694383594, "memory_usage": 10944}},
  {"name": "bar", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351170287.0, "ph": "C", "args": {"cpu_usage": 0.0, "memory_usage": 0}},
  {"name": "bar", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351167127.0, "ph": "X", "dur": 3160.0},
  {"name": "foo", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351165193.0, "ph": "C", "args": {"cpu_usage": 98.625834450525915, "memory_usage": 14976}},
  {"name": "foo", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351505085.0, "ph": "C", "args": {"cpu_usage": 0.0, "memory_usage": 0}},
  {"name": "foo", "cat": "/PerfFlowAspect/src/c/test/smoketest3.cpp", "pid": 44479, "tid": 44479, "ts": 1679184351165193.0, "ph": "X", "dur": 339892.0},
  ...
]

Following is the visualization for the python program above with CPU and memory usage logging enabled:

Fig. 2: Visualization of a single process, single thread program with CPU and memory usage

Release Information

NOTE: The interfaces are being actively developed and are not yet stable. The GitHub issue tracker is the primary way to communicate with the developers.

License Information

GNU LESSER GENERAL PUBLIC LICENSE

Version 3, 29 June 2007

Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

This version of the GNU Lesser General Public License incorporates

the terms and conditions of version 3 of the GNU General Public License, supplemented by the additional permissions listed below.

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As used herein, “this License” refers to version 3 of the GNU Lesser

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Build Instructions

Python Install

The minimum Python version needed is 3.8. You can get PerfFlowAspect from its GitHub repository using this command:

$ git clone https://github.com/flux-framework/PerfFlowAspect

This will create a directory called PerfFlowAspect.

To use PerfFlowAspect, you will need to update your PYTHONPATH with the path to the PerfFlowAspect python directory:

$ cd src/python
$ export PYTHONPATH=$PWD:$PYTHONPATH

C Build

Host Config Files

To handle build options, third-party library paths, and other environment-specific configurations, PerfFlowAspect relies on CMake’s initial-cache file mechanism.

These initial-cache files are called host-config files in PerfFlowAspect, since we typically create a file for each platform or specific system if necessary.

Example configuration files can be found in the host-configs/ directory. Assuming you are in a build/ directory, you can call the host-config file as follows:

$ cmake -C host-configs/{config_file}.cmake ../

Build Dependencies and Versions

redhat	ubuntu	version
clang	clang	>= 6.0
llvm-devel	llvm-dev	>= 6.0
jansson-devel	libjansson-dev	>= 2.6
openssl-devel	libssl-dev	>= 1.0.2
cmake	cmake	>= 3.10
flex	flex	>= 2.5.37
bison	bison	>= 3.0.4
make	make	>= 3.82

Building PerfFlowAspect

PerfFlowAspect uses CMake and requires Clang and LLVM development packages as well as a jansson-devel package for JSON manipulation. It additionally requires the dependencies of our annotation parser code: i.e., flex and bison. Note that LLVM_DIR must be set to the corresponding LLVM cmake directory which may differ across different Linux distributions.

$ module load clang/10.0.1-gcc-8.3.1 (on LLNL systems only)
$ cd PerfFlowAspect/src/c
$ mkdir build && cd build
$ cmake -DCMAKE_CXX_COMPILER=clang++ ../
$ make (note: parallel make (make -j) not supported yet)

$ find . -print | grep lib # successful build produces 3 libraries
./build/parser/libperfflow_parser.so
./build/runtime/libperfflow_runtime.so
./build/weaver/weave/libWeavePass.so

Source Code Annotations

Users can annotate their workflow code to get end-to-end performance insights. Currently, three techniques are available for this:

• Critical path annotation

• Synchronous events annotation

• Asynchronous events annotation

For critical path annotation, the user can provide pointcut and scope information for the annotated region. Currently, valid pointcut values are before, after, around, before_async, after_async, and around_async. When no pointcut is specified, the default assumption is around. We show an example of this below:

#!/usr/bin/python3

import time
import perfflowaspect
import perfflowaspect.aspect


@perfflowaspect.aspect.critical_path()
def foo(msg):
    print("foo")
    time.sleep(1)
    if msg == "hello":
        return 1
    return 0


def main():
    print("Inside main")
    for i in range(4):
        foo("hello")
    return 0


if __name__ == "__main__":
    main()

For synchronous event annotation, the user can provide a pointcut, name, and category for the annotated region. Valid pointcut values are before, after, and around. The name represents a way to identify the current function being annotated, and the category can be a filename. An example of this is shown below:

#!/usr/bin/python3

import time
import os.path
from perfflowaspect import aspect


def foo():
    aspect.sync_event("before", "foo", filename)
    time.sleep(2)
    print("hello")
    aspect.sync_event("after", "foo", filename)


def main():
    aspect.sync_event("before", "main", filename)
    foo()
    aspect.sync_event("after", "main", filename)


if __name__ == "__main__":
    filename = os.path.basename(__file__)
    main()

For asynchronous event annotation, the user can provide a pointcut, name, category, and scope for the annotated region. An example of this is shown below with the help of futures and thread pools:

#!/usr/bin/python3

import os.path
import time
import logging
import threading
from perfflowaspect import aspect

from concurrent.futures import ThreadPoolExecutor
from time import sleep

pool = ThreadPoolExecutor(3)


def bar(message):
    aspect.async_event("before", "bar", filename)
    sleep(3)
    aspect.async_event("after", "bar", filename)
    return message


def foo():
    aspect.sync_event("before", "foo", filename)
    time.sleep(2)
    future = pool.submit(bar, ("hello"))
    while not future.done():
        sleep(1)
    print(future.done())
    print(future.result())
    aspect.sync_event("after", "foo", filename)


def main():
    foo()


if __name__ == "__main__":
    filename = os.path.basename(__file__)
    main()

Upcoming Features

Upcoming features include the ability to specify categories while tracing, a connector for Hatchet, and allow for collection of other statistics, such as GPU utilization. Additionally, the team plans to provide examples of how various benchmarks and workflows have been annotated with PerfFlowAspect. Please follow our GitHub issues page for learning about upcoming features, as well as for suggesting new features for PerfFlowAspect.

Developer’s Guide

This page is currently under construction.

Indices and tables

• Index

• Module Index

• Search Page