criterion-1.2.1.0: Robust, reliable performance measurement and analysis

Copyright (c) 2009-2014 Bryan O'Sullivan BSD-style bos@serpentine.com experimental GHC Trustworthy Haskell2010

Criterion.Main

Description

Wrappers for compiling and running benchmarks quickly and easily. See defaultMain below for an example.

Synopsis

# How to write benchmarks

The Benchmarkable type is a container for code that can be benchmarked. The value inside must run a benchmark the given number of times. We are most interested in benchmarking two things:

• IO actions. Any IO action can be benchmarked directly.
• Pure functions. GHC optimises aggressively when compiling with -O, so it is easy to write innocent-looking benchmark code that doesn't measure the performance of a pure function at all. We work around this by benchmarking both a function and its final argument together.

## Benchmarking IO actions

Any IO action can be benchmarked easily if its type resembles this:

IO a


## Benchmarking pure code

Because GHC optimises aggressively when compiling with -O, it is potentially easy to write innocent-looking benchmark code that will only be evaluated once, for which all but the first iteration of the timing loop will be timing the cost of doing nothing.

To work around this, we provide two functions for benchmarking pure code.

The first will cause results to be fully evaluated to normal form (NF):

nf :: NFData b => (a -> b) -> a -> Benchmarkable


The second will cause results to be evaluated to weak head normal form (the Haskell default):

whnf :: (a -> b) -> a -> Benchmarkable


As both of these types suggest, when you want to benchmark a function, you must supply two values:

• The first element is the function, saturated with all but its last argument.
• The second element is the last argument to the function.

Here is an example that makes the use of these functions clearer. Suppose we want to benchmark the following function:

firstN :: Int -> [Int]
firstN k = take k [(0::Int)..]


So in the easy case, we construct a benchmark as follows:

nf firstN 1000


## Fully evaluating a result

The whnf harness for evaluating a pure function only evaluates the result to weak head normal form (WHNF). If you need the result evaluated all the way to normal form, use the nf function to force its complete evaluation.

Using the firstN example from earlier, to naive eyes it might appear that the following code ought to benchmark the production of the first 1000 list elements:

whnf firstN 1000


Since we are using whnf, in this case the result will only be forced until it reaches WHNF, so what this would actually benchmark is merely how long it takes to produce the first list element!

# Types

A pure function or impure action that can be benchmarked. The Int64 parameter indicates the number of times to run the given function or action.

data Benchmark Source #

Specification of a collection of benchmarks and environments. A benchmark may consist of:

• An environment that creates input data for benchmarks, created with env.
• A single Benchmarkable item with a name, created with bench.
• A (possibly nested) group of Benchmarks, created with bgroup.

Instances

 Source # MethodsshowList :: [Benchmark] -> ShowS #

# Creating a benchmark suite

Arguments

 :: NFData env => IO env Create the environment. The environment will be evaluated to normal form before being passed to the benchmark. -> (env -> Benchmark) Take the newly created environment and make it available to the given benchmarks. -> Benchmark

Run a benchmark (or collection of benchmarks) in the given environment. The purpose of an environment is to lazily create input data to pass to the functions that will be benchmarked.

A common example of environment data is input that is read from a file. Another is a large data structure constructed in-place.

Motivation. In earlier versions of criterion, all benchmark inputs were always created when a program started running. By deferring the creation of an environment when its associated benchmarks need the its, we avoid two problems that this strategy caused:

• Memory pressure distorted the results of unrelated benchmarks. If one benchmark needed e.g. a gigabyte-sized input, it would force the garbage collector to do extra work when running some other benchmark that had no use for that input. Since the data created by an environment is only available when it is in scope, it should be garbage collected before other benchmarks are run.
• The time cost of generating all needed inputs could be significant in cases where no inputs (or just a few) were really needed. This occurred often, for instance when just one out of a large suite of benchmarks was run, or when a user would list the collection of benchmarks without running any.

Creation. An environment is created right before its related benchmarks are run. The IO action that creates the environment is run, then the newly created environment is evaluated to normal form (hence the NFData constraint) before being passed to the function that receives the environment.

Complex environments. If you need to create an environment that contains multiple values, simply pack the values into a tuple.

Lazy pattern matching. In situations where a "real" environment is not needed, e.g. if a list of benchmark names is being generated, undefined will be passed to the function that receives the environment. This avoids the overhead of generating an environment that will not actually be used.

The function that receives the environment must use lazy pattern matching to deconstruct the tuple, as use of strict pattern matching will cause a crash if undefined is passed in.

Example. This program runs benchmarks in an environment that contains two values. The first value is the contents of a text file; the second is a string. Pay attention to the use of a lazy pattern to deconstruct the tuple in the function that returns the benchmarks to be run.

setupEnv = do
let small = replicate 1000 (1 :: Int)
big <- map length . words <$> readFile "/usr/dict/words" return (small, big) main = defaultMain [ -- notice the lazy pattern match here! env setupEnv$ \ ~(small,big) -> bgroup "main" [
bgroup "small" [
bench "length" $whnf length small , bench "length . filter"$ whnf (length . filter (==1)) small
]
,  bgroup "big" [
bench "length" $whnf length big , bench "length . filter"$ whnf (length . filter (==1)) big
]
] ]

Discussion. The environment created in the example above is intentionally not ideal. As Haskell's scoping rules suggest, the variable big is in scope for the benchmarks that use only small. It would be better to create a separate environment for big, so that it will not be kept alive while the unrelated benchmarks are being run.

Arguments

 :: NFData env => IO env Create the environment. The environment will be evaluated to normal form before being passed to the benchmark. -> (env -> IO a) Clean up the created environment. -> (env -> Benchmark) Take the newly created environment and make it available to the given benchmarks. -> Benchmark

Same as env, but but allows for an additional callback to clean up the environment. Resource clean up is exception safe, that is, it runs even if the Benchmark throws an exception.

Arguments

 :: (NFData env, NFData b) => (Int64 -> IO env) Create an environment for a batch of N runs. The environment will be evaluated to normal form before running. -> (env -> IO b) Function returning the IO action that should be benchmarked with the newly generated environment. -> Benchmarkable

Create a Benchmarkable where a fresh environment is allocated for every batch of runs of the benchmarkable.

The environment is evaluated to normal form before the benchmark is run.

When using whnf, whnfIO, etc. Criterion creates a Benchmarkable whichs runs a batch of N repeat runs of that expressions. Criterion may run any number of these batches to get accurate measurements. Environments created by env and envWithCleanup, are shared across all these batches of runs.

This is fine for simple benchmarks on static input, but when benchmarking IO operations where these operations can modify (and especially grow) the environment this means that later batches might have their accuracy effected due to longer, for example, longer garbage collection pauses.

An example: Suppose we want to benchmark writing to a Chan, if we allocate the Chan using environment and our benchmark consists of writeChan env (), the contents and thus size of the Chan will grow with every repeat. If Criterion runs a 1,000 batches of 1,000 repeats, the result is that the channel will have 999,000 items in it by the time the last batch is run. Since GHC GC has to copy the live set for every major GC this means our last set of writes will suffer a lot of noise of the previous repeats.

By allocating a fresh environment for every batch of runs this function should eliminate this effect.

Arguments

 :: (NFData env, NFData b) => (Int64 -> IO env) Create an environment for a batch of N runs. The environment will be evaluated to normal form before running. -> (Int64 -> env -> IO ()) Clean up the created environment. -> (env -> IO b) Function returning the IO action that should be benchmarked with the newly generated environment. -> Benchmarkable

Same as perBatchEnv, but but allows for an additional callback to clean up the environment. Resource clean up is exception safe, that is, it runs even if the Benchmark throws an exception.

Arguments

 :: (NFData env, NFData b) => IO env Action that creates the environment for a single run. -> (env -> IO b) Function returning the IO action that should be benchmarked with the newly genereted environment. -> Benchmarkable

Create a Benchmarkable where a fresh environment is allocated for every run of the operation to benchmark. This is useful for benchmarking mutable operations that need a fresh environment, such as sorting a mutable Vector.

As with env and perBatchEnv the environment is evaluated to normal form before the benchmark is run.

This introduces extra noise and result in reduce accuracy compared to other Criterion benchmarks. But allows easier benchmarking for mutable operations than was previously possible.

Arguments

 :: (NFData env, NFData b) => IO env Action that creates the environment for a single run. -> (env -> IO ()) Clean up the created environment. -> (env -> IO b) Function returning the IO action that should be benchmarked with the newly genereted environment. -> Benchmarkable

Same as perRunEnv, but but allows for an additional callback to clean up the environment. Resource clean up is exception safe, that is, it runs even if the Benchmark throws an exception.

toBenchmarkable :: (Int64 -> IO ()) -> Benchmarkable Source #

Construct a Benchmarkable value from an impure action, where the Int64 parameter indicates the number of times to run the action.

Arguments

 :: String A name to identify the benchmark. -> Benchmarkable An activity to be benchmarked. -> Benchmark

Create a single benchmark.

Arguments

 :: String A name to identify the group of benchmarks. -> [Benchmark] Benchmarks to group under this name. -> Benchmark

Group several benchmarks together under a common name.

## Running a benchmark

nf :: NFData b => (a -> b) -> a -> Benchmarkable Source #

Apply an argument to a function, and evaluate the result to normal form (NF).

whnf :: (a -> b) -> a -> Benchmarkable Source #

Apply an argument to a function, and evaluate the result to weak head normal form (WHNF).

nfIO :: NFData a => IO a -> Benchmarkable Source #

Perform an action, then evaluate its result to normal form. This is particularly useful for forcing a lazy IO action to be completely performed.

Perform an action, then evaluate its result to weak head normal form (WHNF). This is useful for forcing an IO action whose result is an expression to be evaluated down to a more useful value.

# Turning a suite of benchmarks into a program

defaultMain :: [Benchmark] -> IO () Source #

An entry point that can be used as a main function.

import Criterion.Main

fib :: Int -> Int
fib 0 = 0
fib 1 = 1
fib n = fib (n-1) + fib (n-2)

main = defaultMain [
bgroup "fib" [ bench "10" $whnf fib 10 , bench "35"$ whnf fib 35
, bench "37" $whnf fib 37 ] ] defaultMainWith :: Config -> [Benchmark] -> IO () Source # An entry point that can be used as a main function, with configurable defaults. Example: import Criterion.Main.Options import Criterion.Main myConfig = defaultConfig { -- Do not GC between runs. forceGC = False } main = defaultMainWith myConfig [ bench "fib 30"$ whnf fib 30
]

If you save the above example as "Fib.hs", you should be able to compile it as follows:

ghc -O --make Fib

Run "Fib --help" on the command line to get a list of command line options.

Default benchmarking configuration.

# Other useful code

Arguments

 :: MatchType -> [String] Command line arguments. -> Either String (String -> Bool)

Create a function that can tell if a name given on the command line matches a benchmark.

runMode :: Mode -> [Benchmark] -> IO () Source #

Run a set of Benchmarks with the given Mode.

This can be useful if you have a Mode from some other source (e.g. from a one in your benchmark driver's command-line parser).