CodeCosts.jl

`@code_costs`: a variant of `@code_typed` with estimated costs
Author kimikage
Popularity
8 Stars
Updated Last
6 Months Ago
Started In
March 2020

CodeCosts.jl

This package provides a variant of @code_typed with estimated costs for the inlining. This helps find the factors which are preventing the SIMD vectorization.

julia> using CodeCosts

julia> f(x::T) where T = convert(T, max(x * 10.0, x / 3))
f (generic function with 1 method)

julia> @code_costs f(1.0f0)
CodeCostsInfo(
     CodeInfo(
   1 1%1  = Base.fpext(Base.Float64, x)::Float64
   4%2  = Base.mul_float(%1, 10.0)::Float64
  20%3  = Base.div_float(x, 3.0f0)::Float32
   1%4  = Base.fpext(Base.Float64, %3)::Float64
   2%5  = Base.lt_float(%2, %4)::Bool
   1%6  = Base.bitcast(Base.Int64, %4)::Int64
   1%7  = Base.slt_int(%6, 0)::Bool
   1%8  = Base.bitcast(Base.Int64, %2)::Int64
   1%9  = Base.slt_int(%8, 0)::Bool
   1%10 = Base.not_int(%7)::Bool
   1%11 = Base.and_int(%9, %10)::Bool
   1%12 = Base.or_int(%5, %11)::Bool
   2%13 = Base.ne_float(%2, %2)::Bool
   1%14 = Base.Math.ifelse(%13, %2, %4)::Float64
   2%15 = Base.ne_float(%4, %4)::Bool
   1%16 = Base.Math.ifelse(%15, %4, %2)::Float64
   1%17 = Base.Math.ifelse(%12, %14, %16)::Float64
   1%18 = Base.fptrunc(Base.Float32, %17)::Float32
   0 └──       return %18
     )
, CodeCostsSummary(
     zero:  1|
    cheap: 13| 1111111111111
   middle: 10| 4===2=2=2=
expensive: 20| 20==================
    total: 43| 100 (default threshold)
))