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In what way(s) can I benchmark a Julia function?


By : fraba
Date : August 26 2020, 02:00 PM
This might help you using BenchmarkTools is the recommended way to benchmark Julia functions. Unless you are timing something that takes quite a while, use either @benchmark or the less verbose @btime macros exported from it. Because the machinery behind these macros evaluates the target function many times, @time is useful for benchmarking things that run slowly (e.g. where disk access or very time-consuming calculations are involved).
It is important to use @btime or @benchmark correctly, this avoids misleading results. Usually, you are benchmarking a function that takes one or more arguments. When benchmarking, all arguments should be external variables: (without the benchmark macro)
code :
x = 1
f(x)
# do not use f(1)
julia> using BenchmarkTools
julia> a = 1/2;
julia> b = 1/4;
julia> c = 1/8;
julia> a, b, c
(0.5, 0.25, 0.125)

julia> function sum_cosines(x, y, z)
         return cos(x) + cos(y) + cos(z)
       end;

julia> @btime sum_cosines($a, $b, $c);  # the `;` suppresses printing the returned value
  11.899 ns (0 allocations: 0 bytes)    # calling the function takes ~12 ns (nanoseconds)
                                        # the function does not allocate any memory
# if we omit the '$', what we see is misleading
julia> @btime sum_cosines(a, b, c);    # the function appears more than twice slower 
 28.441 ns (1 allocation: 16 bytes)    # the function appears to be allocating memory
# @benchmark can be used the same way that @btime is used
julia> @benchmark sum_cosines($a,$b,$c) # do not use a ';' here
BenchmarkTools.Trial:
  memory estimate:  0 bytes
  allocs estimate:  0
  --------------
  minimum time:     12.111 ns (0.00% GC)
  median time:      12.213 ns (0.00% GC)
  mean time:        12.500 ns (0.00% GC)
  maximum time:     39.741 ns (0.00% GC)
  --------------
  samples:          1500
  evals/sample:     999


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benchmark of simple math functions: why is Fortran and Julia faster than C


By : Nguyen Hoài Anh
Date : March 29 2020, 07:55 AM
this will help Honestly, I wouldn't take these differences too seriously. Different C compilers will give different results too. Try running the C microbenchmarks using GCC and Clang and you will get almost as much difference as C vs. Fortran. Why is GCC sometimes faster than Clang and sometimes not? They just do different optimizations and code generation in different ways. The relative performance is also different on different hardware since it may depend on exact numbers of registers, cache sizes, degree of superscalar throughput, relative speed of various instructions, etc.
It is curious that Fortran is so much faster for the fib benchmark, so if someone figures that one out and posts an answer here, I'll gladly upvote it, but the ≤ 15% difference on the mandel and other benchmarks is just not all that remarkable. The most mysterious thing to me about these benchmarks is why Fortran is so slow at integer parsing. I suspect it's because that code is doing something dumb, but I'm not a Fortran coder so I'm not sure what should be improved. If anyone reading this is a Fortran pro and wants to take a look at this code, it would be greatly appreciated. I suspect that Fortran being 5x slower than C is just wrong.

Interpreting ruby's benchmark results: difference between Benchmark.bm and Benchmark.bmbm


By : awsnewbie
Date : March 29 2020, 07:55 AM
With these it helps There don't seem to be significant differences between the algorithms. What's usually the most relevant number is 'total', which is the total time spent by the CPU on running your code. These are all pretty close to each other.
The difference between Benchmark.bm and Benchmark.bmbm is that the latter runs the benchmark, throws away the result, then runs it again and returns this second run's results. The reason to do this is to avoid unfair disadvantage to some of the subjects: it is possible that there exist common resources between the subjects that only one of them needs to initialize, and with bmbm you have a better chance that everything will be in a 'warm' state and thus obtain a fair result.

Importing multiple versions/branches of a module to benchmark in Julia


By : walter krejcar
Date : March 29 2020, 07:55 AM
it should still fix some issue You can just git checkout a different version of the module and then use benchmarkTools.jl to benchmark. However it may be better to use multiple scripts though (or at least ignore the first trial) (See this comment Importing multiple versions of the same Module/Package for Benchmarking for more info).
e.g.
code :
packagedir = Pkg.dir("DSP")
version2checkout = "v0.0.7"
run(`cd $packagedir`); run(`git checkout tags/$version2checkout`)
import DSP
# do all your benmarking stuff
# start again
for i in readlines(`git tag`)
    version2checkout = chomp(i)
    # checkout version and benchmark
end

Difference between benchmark and time macro in Julia


By : JammastaJake
Date : March 29 2020, 07:55 AM
may help you . I've recently discovered a huge difference between two macros: @benchmark and @time in terms of memory allocation information and time. For example: , The reason is precompilation overhead. To see this define:
code :
julia> h() = quadgk(x -> x, 0., 1.)
h (generic function with 1 method)

julia> @time h()
  1.151921 seconds (915.60 k allocations: 48.166 MiB, 1.64% gc time)
(0.5, 0.0)

julia> @time h()
  0.000013 seconds (21 allocations: 720 bytes)
(0.5, 0.0)
julia> @time quadgk(x -> x, 0., 1.)
  0.312454 seconds (217.94 k allocations: 11.158 MiB, 2.37% gc time)
(0.5, 0.0)

julia> @time quadgk(x -> x, 0., 1.)
  0.279686 seconds (180.17 k allocations: 9.234 MiB)
(0.5, 0.0)
julia> g(x) = x
g (generic function with 1 method)

julia> @time quadgk(g, 0., 1.)
  1.184723 seconds (951.18 k allocations: 49.977 MiB, 1.58% gc time)
(0.5, 0.0)

julia> @time quadgk(g, 0., 1.)
  0.000020 seconds (23 allocations: 752 bytes)
(0.5, 0.0)

How do I do a correct micro-benchmark in Julia?


By : Daniel
Date : March 29 2020, 07:55 AM
Hope that helps From a statistical point of view, @benchmark is much better than @time
TL;DR The BenchmarkTools @benchmark macro is a great micro-benchmark tool. Use the @time macro with caution and don't take the first run seriously.
code :
julia> # Fresh Julia 1.0.0 REPL

julia> # Add BenchmarkTools package using ] key package manager

(v1.0) pkg> add BenchmarkTools  
julia> # Press backspace key to get back to Julia REPL

# Load BenchmarkTools package into current REPL
julia> using BenchmarkTools

julia> # Definine a function with a known elapsed time
julia> f(n) = sleep(n)  # n is in seconds
f (generic function with 1 method)

# Expect just over 500 ms for elapsed time
julia> @benchmark f(0.5)
BenchmarkTools.Trial:
  memory estimate:  192 bytes
  allocs estimate:  5
  --------------
  minimum time:     501.825 ms (0.00% GC)
  median time:      507.386 ms (0.00% GC)
  mean time:        508.069 ms (0.00% GC)
  maximum time:     514.496 ms (0.00% GC)
  --------------
  samples:          10
  evals/sample:     1

julia> # Try second run to compare consistency
julia> # Note the very close consistency in ms for both median and mean times

julia> @benchmark f(0.5)
BenchmarkTools.Trial:
  memory estimate:  192 bytes
  allocs estimate:  5
  --------------
  minimum time:     502.603 ms (0.00% GC)
  median time:      508.716 ms (0.00% GC)
  mean time:        508.619 ms (0.00% GC)
  maximum time:     515.602 ms (0.00% GC)
  --------------
  samples:          10
  evals/sample:     1


julia> # Define the same function with new name for @time macro tests
julia> g(n) = sleep(n)
g (generic function with 1 method)

# First run suffers from compilation time, so 518 ms
julia> @time sleep(0.5)
  0.517897 seconds (83 allocations: 5.813 KiB)

# Second run drops to 502 ms, 16 ms drop
julia> @time sleep(0.5)
  0.502038 seconds (9 allocations: 352 bytes)

# Third run similar to second
julia> @time sleep(0.5)
  0.503606 seconds (9 allocations: 352 bytes)

# Fourth run increases over second by about 13 ms
julia> @time sleep(0.5)
  0.514629 seconds (9 allocations: 352 bytes)
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