autoperf-0.9.0 is not a library.

autoperf

autoperf vastly simplifies the instrumentation of programs with performance counters on Intel machines. Rather than trying to learn how to measure every event and manually programming event values in counter registers or perf, you can use autoperf which will repeatedly run your program until it has measured every single performance event on your machine. autoperf tries to compute a near-optimal schedule that maximizes the amount of events measured per run, minimizes the total number of runs and avoids multiplexing of events on counters.

Installation

autoperf is known to work with Ubuntu 18.04 on Skylake and IvyBridge/SandyBridge architectures. All Intel architectures should work, please file a bug request if they don't. autoperf builds on perf from the Linux project and a few other libraries that can be installed using:

$ sudo apt-get install likwid cpuid hwloc numactl util-linux

To run the example analysis scripts, you'll need these python3 libraries:

$ pip3 install ascii_graph matplotlib pandas argparse numpy

You'll also need Rust which is best installed using rustup:

$ curl https://sh.rustup.rs -sSf | sh -s -- -y
$ source $HOME/.cargo/env

autoperf is published on crates.io, so once you have rust and cargo, you can install it like this:

$ cargo install autoperf

Or clone and build the repository yourself:

$ git clone https://github.com/gz/autoperf.git
$ cd autoperf
$ cargo build --release
$ ./target/release/autoperf --help

autoperf uses perf internally to interface with Linux and the performance counter hardware. perf recommends that the following settings are disabled. Therefore, autoperf will check the values of those configurations and refuse to start if they are not set like below:

sudo sh -c 'echo 0 >> /proc/sys/kernel/kptr_restrict'
sudo sh -c 'echo 0 > /proc/sys/kernel/nmi_watchdog'
sudo sh -c 'echo -1 > /proc/sys/kernel/perf_event_paranoid'

Usage

autoperf has a few commands, use --help to get a better overview of all the options.

Profiling

The profile command instruments a single program by running it multiple times until every performance event is measured. For example,

$ autoperf profile sleep 2

will repeatedly run sleep 2 while measuring events with performance counters. After it is done you will find an out folder with many csv files that contain measurements from individual runs.

Aggregating results

To combine all those runs into a single CSV result file you can use the aggregate command:

$ autoperf aggregate ./out

This will do some sanity checking and produce a results.csv file which contains all the measured data.

Analyze results

Now you have all the data, so you can start asking some questions. As an example the following script tells you which events were correlated when your program was running:

$ python3 analyze/profile/correlation.py ./out
$ open out/correlation_heatmap.png

For example, visualizing event correlation for the profiled sleep 2 command above looks like this (every dot represents the correlation between two measured performance events, on a Skylake machine which had around 1.7k non-zero event measurement): Correlation Heatmap

You can look at individual events too:

python3 analyze/profile/event_detail.py --resultdir ./out --features AVG.OFFCORE_RESPONSE.ALL_RFO.L3_MISS.REMOTE_HIT_FORWARD

Plot events

There are more scripts in the analyze folder to better work with the captured data-sets. Have a look.

What do I use this for?

autoperf allows you to quickly gather lots of performance (or training) data and reason about it quantitatively. For example, we initially developed autoperf to build ML classifiers that the Barrelfish scheduler could use for detecting application slowdown and make better scheduling decisions. autoperf meant that we needed no domain knowledge about events, aside from how to measure them they could be treated as a black-box.

You can read more about our experiments here:

But we found that autoperf can greatly simplify your life in many other scenarios too:

Find out what performance events are relevant for your workload
Analyzing and finding performance issues in your code
Find classifiers to detect hardware exploits (side channels/spectre/meltdown etc.)
...