ruby3x3: how are we going to measure 3x

Ruby3x3: How are we going to measure 3x?

Matthew Gaudet

Developer on the

Eclipse OMR project

Cross platform components for

building reliable, high performance language

runtimes

github.com/eclipse/omr

@eclipseOMR

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Ruby 2.0 Ruby 3.0

Ruby 3x3: The Goal.

Performance

Agenda

Let’s talk about benchmarking! • Some definitions• Some philosophy• Some pitfalls

Ruby 3x3• Some Thoughts from Me.

Benchmarking.

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Art + Science

Definition

Benchmark:

A piece of computer code run in

order to gather measurements

for comparison.

Definition

Benchmark:

Comparing the execution time of different

interpreters, or options.

Comparing the execution time of algorithms

Comparing the accuracy of different machine

learning algorithms

The Art of Benchmarking: What do you run?

MicrobenchmarkFull

Application

The Benchmark Continuum

Application Kernel

Microbenchmarks

Pros

Often easy to setup and run.

Targeted to a particular

aspect.

Fast acquisition of data.

Cons

Exaggerates effects.

Not typically generalizable.

A very small program written to explore the performance of one aspect of the system under test.

Full Applications

Pros

Immediate and obvious real

world impact!

Cons

Small effects can be

swamped in natural

application variance.

Can be complicated to

setup, or slow to run!

Benchmarking a whole application

Application Kernel

Pros

Tight connection to real

world code.

Typically more

generalizable.

Cons

Difficult to know how much

of a an application should

be included vs. mocked.

A particular part of an application extracted for the express purpose of constructing a benchmark.

Pitfalls in benchmark design

Un-Ruby-Like Code:

Code that looks like another language.

“You can write FORTRAN in any language”

Code that never produces garbage.

Code without exceptions


Input Data is a key part of many benchmarks: Watch out

for weird input data!

Imagine an MP3 compressor benchmark

– Inputs are

1. Silence. weird because most mp3s are not silence.

2. White noise. weird because most mp3s have some

structure.

– Reduces the generalizability of the results!


What do you measure?

Time?

Throughput?

Latency?

Definition

Wall-clock time:

The measurement of relative to

a clock independent of the

process being timed.

$ time sleep 1

real 0m1.003suser 0m0.000ssys 0m0.000s

Definition

CPU time:

Measurement of how much of

the CPU the process actually

used

$ time sleep 1


Definition

Throughput:

A count of operations that occur

per unit of time.

Definition

Latency:

The time it takes for a response

to occur after stimulus.


What do you measure?

What do you report?

Raw Measurements?

Speedup?

Definition

Speedup:

A ratio computed between a

baseline and experimental time

measurement.

𝑇𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒𝑇𝑒𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙

The Science of Benchmarking

An aside on misleading with speedup.

Speedup:

A ratio computed between a

baseline and experimental time

measurement.


“He who controls the baseline

controls the speedup”


“Our parallelization system shows

linear speedup as the number of

threads increases”


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1 thread 2 thread 4 thread 8 thread

SPEEDUPSpeedup


Measurement Time (s)

Original Sequential Program 10.0

Parallelized 1 thread 100.0




The distinction between relative speedupand absolute speedup.

How you measure affects whatyou measure.

Both of these are valid benchmarks!

$ cat test.rb

...

puts Benchmark.measure {

1_000_000.times {

compute_foo()

}

}

$ for i in `seq 1 10`; do ruby t.rb ; done;

...

10.times {

puts Benchmark.measure {

1_000_000.times {

compute_foo()

}

}

}

vs.

But they’re going to measure (and may encourage the optimization of ) two different things!

Definition

Warmup:

The time from application start

until it hits peak performance.

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Time per Iteration (s)

When has warmup finished?

Despite this, even knowing warmup exists is important: It allows us to choose methodologies that can accommodate the possibility!

Definition

Run-to-Run Variance

The observed effect that

identical runs do not have

identical times. $ for i in `seq 1 5`; do ruby -I../../lib/ string-equal.rb--loopn 1 1000; done; 1.3473345581.3483506321.306904781.3147649771.323862345

Methodology:

An incomplete list of decisions that need to be made when

developing benchmarking methodology:

1. Does your methodology account for warmup?

2. How are you accounting for run-to-run variance?

3. How are you accounting for the effects of the garbage

collector?


Accounting for warmup often means producing

intermediate scores, so you can see when they stabilize.

If you aren’t accounting for warmup, you may find

that you miss out on peak performance.


Account for run to run variance by running multiple times,

and presenting confidence intervals!

Be sure you’re methodology doesn’t encourage wild

variations in performance though!

Be aware, benchmarks can act Weird

Garbage Collector Impact

ruby -J-Xmx330m -J-Xms330m -I../../lib/ connected.rb --loopn10 1

0.4264123000029940.354429644008632750.34847818300477230.362810398000874560.35657457199704370.361791819988866340.317137328005628660.33650193299399690.3053975360089680.3006619710067753

ruby -J-Xmx33m -J-Xms33m -I../../lib/ connected.rb --loopn10 1

0.54314418800640850.84104106100858190.79751591700187420.84587562699744010.99742122599855071.08870255399961021.0670530100032921.0570035319979071.07081619399832561.0480617069988512

Garbage Collector Impact

Garbage collector impact can make benchmarks incredibly difficult to

compare:

The Ruby+OMR Preview uses the OMR GC technology, including a

change to move off heap data on heap.

Side effect of this is that it’s crazy difficult to compare against the default

ruby: there’s an entirely different set of data on the heap!

If heap size adapts to machine memory, you’ll need to figure out how to

lock it to give good comparisons across machines

42

string malloc string OMRBuffer

Benchmarking:

User Error

$ time ruby their_implementation.rb 100000


$ time ruby my_implementation.rb 10000


10x speedup!

User Error

$ time ruby their_implementation.rb 100000


$ time ruby my_implementation.rb 10000


10x speedup!

Pro Tip: Use a harness that keeps you out of the benchmarking process.

Aim for reproducibility!

Tim

e (

s)

Iterations

Unplugs Laptop

Return Power

Power Saving

Mode

Other Hardware Effects to watch for!

TurboBoost (and similar): Frequency scaling based

on the season.



on the season location.



on the season location rack



on the season location rack CPU temperature.

Even in the cloud! [1]

[1]: http://www.brendangregg.com/blog/2014-09-15/the-msrs-of-ec2.html

Software Pitfalls

What about your backup service?

Long sequence of benchmarks… do you have

automatic software updates installed?

Do your system administrators know you are

benchmarking?

What about your screensaver?

Paranoia is a matter of Effect Sizes

Hardware Changes:

– Disable turbo boost,

– Disable hyperthreading.

Krun tool:

– Set ulimit for heap and stack.

– Reboot machine before execution

– Monitor dmesg for unexpected output

– Monitor temperature of machine.

– Disable pstates

– CPU Governor set to performance mode.

– Perf sample rate control.

– Disable ASLR.

– Create a new user account for each run

http://arxiv.org/pdf/1602.00602v1.pdf

Performance improvements compound!

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is 10 increases of 11% is 25 increases of 4.5% is 100 increases of 1.1%

3x

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Ruby 2.0 Ruby 2.1 Ruby 2.2 Ruby 2.3 Ruby 2.4 Ruby 2.? Ruby 2.? Ruby 2.? Ruby 2.? Ruby 2.? Ruby 2.? Ruby 3.0

Ruby 3x3: The Process

Performance

(Made up data for illustration only)

Philosophizing

Philosophy

Benchmarks drive change.

– What you measure is what

people try to change.

–What you don’t measure, may

not change how you want.

Squeezing a Water Balloon

Be sure to measure associated metrics to have a

clear headed view of tradeoffs:

For example: JIT Compilation:

Trade startup speed for peak speed.

Trade footprint for speed.

Benchmarks age!

Benchmarks can be wrung of all their possible

performance at some point.

Using the same benchmarks for too long can lead to

shortsighted decisions driven by old benchmarks.

Idiomatic code evolves in a language.

Benchmark use of language features can help drive

adoption!

–Be sure to benchmark desirable new language features!

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Benchmarking 3x3

https://twitter.com/tenderlove/status/765288219931881472

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Ruby Community has some great starting points!

Recall: Benchmarks drive change

Thought: Choose 9 application kernels that represent what we want from a future CRuby!

• Why 9?• Too many benchmarks can diffuse effort.• Also! 3x3 = 9!

¯\_(ツ)_/¯

Brainstorming on the nine?

1. Some CPU intensive applications: • OptCarrot, Neural Nets, Monte Carlo Tree

Search, PSD filter pipeline? 2. Some memory intensive application:

• Large tree mutation benchmark? 3. A startup benchmark:

• time ruby -e “def foo; ‘100’; end; puts foo”? 4. Some web application framework benchmarks.

Choose a methodology that drives the change we want in CRuby.

Want great performance, but not huge warmup

times?

–Only run 5 iterations, and score the last one?

Don’t want to deal with warmup?

–Don’t run iterations: Score the first run!

I Error Bars

One last idea…

What about a more ambitious choice?

Use the ecosystem!

Add a standard performance harness to RubyGems.

Would allow VM developers to sample popular gems, and

run a perf suite written by gem authors.

With effort, time and $$$, we could make broad statements

about performance impact on the gem ecosystem.

Use the ecosystem!

Doesn’t just help VM developers

Gem authors get

1. Enabled for performance tracking!

2. Easier performance reporting with VM developers.

Credits Headache: https://en.wikipedia.org/wiki/Headache#/media/File:Cruikshank_-

_The_Head_Ache.png

@MattStudies

[email protected]

For more on software systems evaluation, be sure to visitThe Evaluate Collaboratory @

http://evaluate.inf.usi.ch/

https://en.wikipedia.org/wiki/Headache#/media/File:Cruikshank_-_The_Head_Ache.png

ruby3x3: how are we going to measure 3x

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