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[doc] measuring performance docs (#2117)

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* performance measuring docs

* spelling

* combining advanced and simple section

* zstd benchmark title change
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CONTRIBUTING.md
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@ -44,7 +44,7 @@ Our contribution process works in three main stages:
git pull https://github.com/facebook/zstd dev
git push origin dev
```
* Topic and deveopment:
* Topic and development:
* Make a new branch on your fork about the topic you're developing for
```
# branch names should be consise but sufficiently informative
@ -80,7 +80,7 @@ Our contribution process works in three main stages:
as the destination.
* Examine the diff presented between the two branches to make sure there is nothing unexpected.
* Write a good pull request description:
* While there is no strict template that our contributers follow, we would like them to
* While there is no strict template that our contributors follow, we would like them to
sufficiently summarize and motivate the changes they are proposing. We recommend all pull requests,
at least indirectly, address the following points.
* Is this pull request important and why?
@ -126,6 +126,206 @@ just `contrib/largeNbDicts` and nothing else, you can run:
scan-build make -C contrib/largeNbDicts largeNbDicts
```
## Performance
Performance is extremely important for zstd and we only merge pull requests whose performance
landscape and corresponding trade-offs have been adequately analyzed, reproduced, and presented.
This high bar for performance means that every PR which has the potential to
impact performance takes a very long time for us to properly review. That being said, we
always welcome contributions to improve performance (or worsen performance for the trade-off of
something else). Please keep the following in mind before submitting a performance related PR:
1. Zstd isn't as old as gzip but it has been around for time now and its evolution is
very well documented via past Github issues and pull requests. It may be the case that your
particular performance optimization has already been considered in the past. Please take some
time to search through old issues and pull requests using keywords specific to your
would-be PR. Of course, just because a topic has already been discussed (and perhaps rejected
on some grounds) in the past, doesn't mean it isn't worth bringing up again. But even in that case,
it will be helpful for you to have context from that topic's history before contributing.
2. The distinction between noise and actual performance gains can unfortunately be very subtle
especially when microbenchmarking extremely small wins or losses. The only remedy to getting
something subtle merged is extensive benchmarking. You will be doing us a great favor if you
take the time to run extensive, long-duration, and potentially cross-(os, platform, process, etc)
benchmarks on your end before submitting a PR. Of course, you will not be able to benchmark
your changes on every single processor and os out there (and neither will we) but do that best
you can:) We've adding some things to think about when benchmarking below in the Benchmarking
Performance section which might be helpful for you.
3. Optimizing performance for a certain OS, processor vendor, compiler, or network system is a perfectly
legitimate thing to do as long as it does not harm the overall performance health of Zstd.
This is a hard balance to strike but please keep in mind other aspects of Zstd when
submitting changes that are clang-specific, windows-specific, etc.
## Benchmarking Performance
Performance microbenchmarking is a tricky subject but also essential for Zstd. We value empirical
testing over theoretical speculation. This guide it not perfect but for most scenarios, it
is a good place to start.
### Stability
Unfortunately, the most important aspect in being able to benchmark reliably is to have a stable
benchmarking machine. A virtual machine, a machine with shared resources, or your laptop
will typically not be stable enough to obtain reliable benchmark results. If you can get your
hands on a desktop, this is usually a better scenario.
Of course, benchmarking can be done on non-hyper-stable machines as well. You will just have to
do a little more work to ensure that you are in fact measuring the changes you've made not and
noise. Here are some things you can do to make your benchmarks more stable:
1. The most simple thing you can do to drastically improve the stability of your benchmark is
to run it multiple times and then aggregate the results of those runs. As a general rule of
thumb, the smaller the change you are trying to measure, the more samples of benchmark runs
you will have to aggregate over to get reliable results. Here are some additional things to keep in
mind when running multiple trials:
* How you aggregate your samples are important. You might be tempted to use the mean of your
results. While this is certainly going to be a more stable number than a raw single sample
benchmark number, you might have more luck by taking the median. The mean is not robust to
outliers whereas the median is. Better still, you could simply take the fastest speed your
benchmark achieved on each run since that is likely the fastest your process will be
capable of running your code. In our experience, this (aggregating by just taking the sample
with the fastest running time) has been the most stable approach.
* The more samples you have, the more stable your benchmarks should be. You can verify
your improved stability by looking at the size of your confidence intervals as you
increase your sample count. These should get smaller and smaller. Eventually hopefully
smaller than the performance win you are expecting.
* Most processors will take some time to get `hot` when running anything. The observations
you collect during that time period will very different from the true performance number. Having
a very large number of sample will help alleviate this problem slightly but you can also
address is directly by simply not including the first `n` iterations of your benchmark in
your aggregations. You can determine `n` by simply looking at the results from each iteration
and then hand picking a good threshold after which the variance in results seems to stabilize.
2. You cannot really get reliable benchmarks if your host machine is simultaneously running
another cpu/memory-intensive application in the background. If you are running benchmarks on your
personal laptop for instance, you should close all applications (including your code editor and
browser) before running your benchmarks. You might also have invisible background applications
running. You can see what these are by looking at either Activity Monitor on Mac or Task Manager
on Windows. You will get more stable benchmark results of you end those processes as well.
* If you have multiple cores, you can even run your benchmark on a reserved core to prevent
pollution from other OS and user processes. There are a number of ways to do this depending
on your OS:
* On linux boxes, you have use https://github.com/lpechacek/cpuset.
* On Windows, you can "Set Processor Affinity" using https://www.thewindowsclub.com/processor-affinity-windows
* On Mac, you can try to use their dedicated affinity API https://developer.apple.com/library/archive/releasenotes/Performance/RN-AffinityAPI/#//apple_ref/doc/uid/TP40006635-CH1-DontLinkElementID_2
3. To benchmark, you will likely end up writing a separate c/c++ program that will link libzstd.
Dynamically linking your library will introduce some added variation (not a large amount but
definitely some). Statically linking libzstd will be more stable. Static libraries should
be enabled by default when building zstd.
4. Use a profiler with a good high resolution timer. See the section below on profiling for
details on this.
5. Disable frequency scaling, turbo boost and address space randomization (this will vary by OS)
6. Try to avoid storage. On some systems you can use tmpfs. Putting the program, inputs and outputs on
tmpfs avoids touching a real storage system, which can have a pretty big variability.
Also check our LLVM's guide on benchmarking here: https://llvm.org/docs/Benchmarking.html
### Zstd benchmark
The fastest signal you can get regarding your performance changes is via the in-build zstd cli
bench option. You can run Zstd as you typically would for your scenario using some set of options
and then additionally also specify the `-b#` option. Doing this will run our benchmarking pipeline
for that options you have just provided. If you want to look at the internals of how this
benchmarking script works, you can check out programs/benchzstd.c
For example: say you have made a change that you believe improves the speed of zstd level 1. The
very first thing you should use to asses whether you actually achieved any sort of improvement
is `zstd -b`. You might try to do something like this. Note: you can use the `-i` option to
specify a running time for your benchmark in seconds (default is 3 seconds).
Usually, the longer the running time, the more stable your results will be.
```
$ git checkout <commit-before-your-change>
$ make && cp zstd zstd-old
$ git checkout <commit-after-your-change>
$ make && cp zstd zstd-new
$ zstd-old -i5 -b1 <your-test-data>
1<your-test-data> : 8990 -> 3992 (2.252), 302.6 MB/s , 626.4 MB/s
$ zstd-new -i5 -b1 <your-test-data>
1<your-test-data> : 8990 -> 3992 (2.252), 302.8 MB/s , 628.4 MB/s
```
Unless your performance win is large enough to be visible despite the intrinsic noise
on your computer, benchzstd alone will likely not be enough to validate the impact of your
changes. For example, the results of the example above indicate that effectively nothing
changed but there could be a small <3% improvement that the noise on the host machine
obscured. So unless you see a large performance win (10-15% consistently) using just
this method of evaluation will not be sufficient.
### Profiling
There are a number of great profilers out there. We're going to briefly mention how you can
profile your code using `instruments` on mac, `perf` on linux and `visual studio profiler`
on windows.
Say you have an idea for a change that you think will provide some good performance gains
for level 1 compression on Zstd. Typically this means, you have identified a section of
code that you think can be made to run faster.
The first thing you will want to do is make sure that the piece of code is actually taking up
a notable amount of time to run. It is usually not worth optimzing something which accounts for less than
0.0001% of the total running time. Luckily, there are tools to help with this.
Profilers will let you see how much time your code spends inside a particular function.
If your target code snippit is only part of a function, it might be worth trying to
isolate that snippit by moving it to its own function (this is usually not necessary but
might be).
Most profilers (including the profilers dicusssed below) will generate a call graph of
functions for you. Your goal will be to find your function of interest in this call grapch
and then inspect the time spent inside of it. You might also want to to look at the
annotated assembly which most profilers will provide you with.
#### Instruments
We will once again consider the scenario where you think you've identified a piece of code
whose performance can be improved upon. Follow these steps to profile your code using
Instruments.
1. Open Instruments
2. Select `Time Profiler` from the list of standard templates
3. Close all other applications except for your instruments window and your terminal
4. Run your benchmarking script from your terminal window
* You will want a benchmark that runs for at least a few seconds (5 seconds will
usually be long enough). This way the profiler will have something to work with
and you will have ample time to attach your profiler to this process:)
* I will just use benchzstd as my bencharmking script for this example:
```
$ zstd -b1 -i5 <my-data> # this will run for 5 seconds
```
5. Once you run your benchmarking script, switch back over to instruments and attach your
process to the time profiler. You can do this by:
* Clicking on the `All Processes` drop down in the top left of the toolbar.
* Selecting your process from the dropdown. In my case, it is just going to be labled
`zstd`
* Hitting the bright red record circle button on the top left of the toolbar
6. You profiler will now start collecting metrics from your bencharking script. Once
you think you have collected enough samples (usually this is the case after 3 seconds of
recording), stop your profiler.
7. Make sure that in toolbar of the bottom window, `profile` is selected.
8. You should be able to see your call graph.
* If you don't see the call graph or an incomplete call graph, make sure you have compiled
zstd and your benchmarking scripg using debug flags. On mac and linux, this just means
you will have to supply the `-g` flag alone with your build script. You might also
have to provide the `-fno-omit-frame-pointer` flag
9. Dig down the graph to find your function call and then inspect it by double clicking
the list item. You will be able to see the annotated source code and the assembly side by
side.
#### Perf
This wiki has a pretty detailed tutorial on getting started working with perf so we'll
leave you to check that out of you're getting started:
https://perf.wiki.kernel.org/index.php/Tutorial
Some general notes on perf:
* Use `perf stat -r # <bench-program>` to quickly get some relevant timing and
counter statistics. Perf uses a high resolution timer and this is likely one
of the first things your team will run when assessing your PR.
* Perf has a long list of hardware counters that can be viewed with `perf --list`.
When measuring optimizations, something worth trying is to make sure the handware
counters you expect to be impacted by your change are in fact being so. For example,
if you expect the L1 cache misses to decrease with your change, you can look at the
counter `L1-dcache-load-misses`
* Perf hardware counters will not work on a virtual machine.
#### Visual Studio
TODO
## Setting up continuous integration (CI) on your fork
Zstd uses a number of different continuous integration (CI) tools to ensure that new changes
are well tested before they make it to an official release. Specifically, we use the platforms