When code performance is an issue, it is sometimes useful to get absolute performance measurements in order to objectivise what is "slow" or "fast". GFlops.jl leverages the power of Cassette.jl to automatically count the number of floating-point operations in a piece of code. When combined with the accuracy of BenchmarkTools, this allows for easy and absolute performance measurements.

julia> using GFlops

julia> x = rand(1000);

julia> @count_ops sum($x)
Flop Counter:
 add32: 0
 sub32: 0
 mul32: 0
 div32: 0
 add64: 999
 sub64: 0
 mul64: 0
 div64: 0


julia> @gflops sum($x);
  10.03 GFlops,  19.15% peak  (9.99e+02 flop, 9.96e-08 s)

This package is registered and can therefore be simply be installed with

pkg> add GFlops

GFlops.jl does not see what happens outside the realm of Julia code. It especially does not see operations performed in external libraries such as BLAS calls:

julia> using LinearAlgebra

julia> @count_ops dot($x, $x)
Flop Counter:
 add32: 0
 sub32: 0
 mul32: 0
 div32: 0
 add64: 0
 sub64: 0
 mul64: 0
 div64: 0

This is a known issue; we'll try and find a way to circumvent the problem.