Profile Anything

Profile any linux distribution on C++ at the harware level, period. Zero complexity. Identify critical hotspots within your C++ code.

Side Notes

This project is an attempt to extend the marvelous work done by Viktor Leis in his perfevent repository into a seamless, scalable API. Currently, it is known to work with C++ and Python.

Features

• Support for Docker builds and reproducible testing in isolated environments
• Obtain hardware-level metrics like the L1 Cache Miss Rate, Instructions, Cycles, CPU usage, and many more.
• Bringing profiling capabilities to higher level languages like Python.

Installation

You can use the Makefile provided for a 2-3 command installation. Steps are the following:

Local Setup

1. Run the installations necessary for perf to run. make install
2. Build the C++ API. make build
3. Execute any profiled code (develop this in the src folder) with make run

Docker Setup

1. Build the docker image (make sure you have docker installed!) using make docker/build
2. Run the image with make docker/run

Python API Setup

1. Run the installations necessary for perf to run. make install
2. Build the C++ API. make python/build
3. Install the package to pip for global use pip install .

Quickstart

Copy over the PerfEvent.hpp headers from the include directory. The src/hyperprofiler.cpp/h files contain a simple API that can be used to interact with Perf in your C++ and Python

C++ Interface

#include <iostream>
#include "hyperprofiler.h"

using namespace std;

void processToProfile() {
    cout << "Hello World" << endl;
}

int main() 
{
    //Starter profiling code
    HyperProfiler p = HyperProfiler();
    p.startProfiling();

    //Run the process we seek to profile
    processToProfile();

    //Ending profiling code
    p.stopProfiling();
    
    cout << p.getSingleStat("L1-misses") << " " << p.getSingleStat("cycles") << endl; // use n as scale factor
    cout << endl;
    return 0;
}

Python Interface

Make sure to follow the steps for the python installation before running any code.

import hyperprofiler

hp = hyperprofiler.HyperProfiler()

# Start the profiler
hp.startProfiling()

# Insert whatever method you'd like
yourMethodToProfile()

# Complete Profiling
hp.stopProfiling()

print(hp.getSingleStat("L1-misses"))

Todo

1. Create a list of supported metrics from the perf interface
2. Add some support for instant, isolated segmented tests using Docker.

This repo is being actively maintained as of Feb 2024. Facing bugs, Have some more suggestions about what should be done next? Open up an ISSUE!