Sometimes I see the following pattern in code. How can I write this in a more-efficient/elegant way? Without writing explicit loops, of course.
a = rand(5,5);
b = rand(5,5);
delta = maximum(abs, i-j for (i,j) in zip(a,b))
Ideally, I imagine something like:
delta = maximum(abs, -, a, b)
Is this possible in Julia?
julia> a = rand(5,5);
julia> b = rand(5,5);
julia> delta = maximum(abs, i-j for (i,j) in zip(a,b))
0.8146851910185748
julia> maximum(abs, a .- b)
0.8146851910185748
Yes, delta = mapreduce(absā-, max, a, b). However, this doesnāt (yet) have a fast implementation, so the zip may be better.
maximum(x->abs(x[1]-x[2]), zip(a, b))
@jzr, the dot seems to be unnecessary? Same result with: maximum(abs, a - b)
maximum(Base.splat(absā-), zip(a, b))
What does a āfast implementationā mean in this context? Is mapreduce implemented in a not very efficient way and there is potential to improve it?
No, mapreduce is pretty well optimized, but only for specific cases since we can dispatch on different functions for those (functions have their own type after all). absā- is unlikely to be one of those cases so far, so itāll fall back to a generic implementation (which may not SIMD etc.) Depending on the functions and iterators in use, mapreduce may be specialized to those and achieve a greater speedup than the fallback implementation can provide.
Fastest Iāve seen so far:
jl> using Tullio
jl> @tullio (max) maxval := abs(a[i] - b[i])
@mcabbott must there be an output variable inside the macro call? Could one hypothetically write
jl> maxval = @tullio (max) abs(a[i] - b[i])
?
(Oops: fixed missing abs)
For now there must be, although it could be changed I suppose. You can write @tullio (max) _ := abs(a[i] - b[i]) which at least saves thinking up a name.
What does a āfast implementationā mean in this context?
If you do @less mapreduce(absā-, max, a, b) you will see this:
mapreduce(f, op, A::AbstractArrayOrBroadcasted...; kw...) =
reduce(op, map(f, A...); kw...)
i.e. it performs map which allocates the array, then reduce. I think this is a placeholder implementation, to allow the syntax now and make it fast later. Itās very much like maximum(abs.(a.-b)), while with one array maximum(abs, a) is mapreduce(abs, max, a) and this does not allocate abs.(a). The issue about the multiple-argument case is #38558.
Thank you all for your contributions, here I present some benchmarks for different alternatives. Tullio seems to be the only alternative with the same performance as explicit loops.
function max_abs(a,b)
mx = 0.0
for i in eachindex(a,b)
tmp = abs(a[i]-b[i])
tmp > mx && (mx = tmp)
end
mx
end
N = 500
a = rand(N,N)
b = rand(N,N)
@btime max_abs($a,$b) # 188.500 Ī¼s (0 allocations: 0 bytes)
@btime maximum(abs(i-j) for (i,j) in zip($a,$b)) # 442.200 Ī¼s (0 allocations: 0 bytes)
@btime maximum(abs, $a - $b) # 568.800 Ī¼s (2 allocations: 1.91 MiB)
@btime maximum(Base.splat(absā-), zip($a, $b)) # 442.300 Ī¼s (0 allocations: 0 bytes)
@btime mapreduce(absā-, max, $a, $b) # 572.900 Ī¼s (2 allocations: 1.91 MiB)
using Tullio
@btime @tullio (max) delta := abs($a[i] - $b[i]) # 188.800 Ī¼s (1 allocation: 16 bytes)
using LoopVectorization
@btime @tullio (max) delta := abs($a[i] - $b[i]) # 75.300 Ī¼s (1 allocation: 16 bytes)
Not sure if it will make a difference, but I would use
mx = max(mx, tmp)
to avoid the branch, which could in principle prevent vectorization. Itās also clearer.
This is nicer undoubtedly, but the performance drops to the generator version, it needs a @fastmath to keep the same performance in this case.
An extended version of Seif Sheblās excellent benchmark test
I tried the benchmark tests for N = 100, 200, 500, 1000, and 2000.
The results look different for each N.
Julia script:
# maxabstest.jl
using BenchmarkTools
N = isempty(ARGS) ? 500 : parse(Int, ARGS[1])
function max_abs(a, b)
m = zero(promote_type(eltype(a), eltype(b)))
for i in eachindex(a, b)
tmp = abs(a[i] - b[i])
tmp > m && (m = tmp)
end
m
end
a = randn(N, N)
b = randn(N, N)
@show VERSION
@show Threads.nthreads()
println()
@show N
print("simple for loop: ")
@btime max_abs($a, $b)
print("mapreduce absā- max: ")
@btime mapreduce(absā-, max, $a, $b)
print("maximum(abs, a - b): ")
@btime maximum(abs, $a - $b)
print("maximum(generator): ")
@btime maximum(abs(i - j) for (i, j) in zip($a, $b))
print("maximum splat(absā-):")
@btime maximum(Base.splat(absā-), zip($a, $b))
using Tullio
print("Tullio: ")
@btime @tullio (max) _ := abs($a[i] - $b[i])
print("Tullio (LoopVect.): ")
using LoopVectorization
@btime @tullio (max) _ := abs($a[i] - $b[i])
function max_abs_turbo(a, b)
m = zero(promote_type(eltype(a), eltype(b)))
@turbo for i in eachindex(a, b)
m = max(m, abs(a[i] - b[i]))
end
m
end
function max_abs_tturbo(a, b)
m = zero(promote_type(eltype(a), eltype(b)))
@tturbo for i in eachindex(a, b)
m = max(m, abs(a[i] - b[i]))
end
m
end
print("LoopVect. @turbo: ")
@btime max_abs_turbo($a, $b)
print("LoopVect. @tturbo: ")
@btime max_abs_tturbo($a, $b)
Results for N = 100, 200, 500, 1000, 2000
$ julia maxabstest.jl 100
VERSION = v"1.6.1"
Threads.nthreads() = 12
N = 100
simple for loop: 8.733 Ī¼s (0 allocations: 0 bytes)
maximum(abs, a - b): 10.100 Ī¼s (2 allocations: 78.20 KiB)
maximum(generator): 18.100 Ī¼s (0 allocations: 0 bytes)
maximum splat(absā-): 18.100 Ī¼s (0 allocations: 0 bytes)
mapreduce absā- max: 11.300 Ī¼s (2 allocations: 78.20 KiB)
Tullio: 8.767 Ī¼s (1 allocation: 16 bytes)
Tullio (LoopVect.): 1.290 Ī¼s (1 allocation: 16 bytes)
LoopVect. @turbo: 1.240 Ī¼s (0 allocations: 0 bytes)
LoopVect. @tturbo: 722.689 ns (0 allocations: 0 bytes)
$ julia maxabstest.jl 200
VERSION = v"1.6.1"
Threads.nthreads() = 12
N = 200
simple for loop: 34.900 Ī¼s (0 allocations: 0 bytes)
maximum(abs, a - b): 34.500 Ī¼s (2 allocations: 312.58 KiB)
maximum(generator): 71.600 Ī¼s (0 allocations: 0 bytes)
maximum splat(absā-): 71.700 Ī¼s (0 allocations: 0 bytes)
mapreduce absā- max: 42.300 Ī¼s (2 allocations: 312.58 KiB)
Tullio: 34.900 Ī¼s (1 allocation: 16 bytes)
Tullio (LoopVect.): 8.400 Ī¼s (1 allocation: 16 bytes)
LoopVect. @turbo: 9.600 Ī¼s (0 allocations: 0 bytes)
LoopVect. @tturbo: 2.010 Ī¼s (0 allocations: 0 bytes)
$ julia maxabstest.jl 500
VERSION = v"1.6.1"
Threads.nthreads() = 12
N = 500
simple for loop: 213.400 Ī¼s (0 allocations: 0 bytes)
maximum(abs, a - b): 444.500 Ī¼s (2 allocations: 1.91 MiB)
maximum(generator): 447.000 Ī¼s (0 allocations: 0 bytes)
maximum splat(absā-): 447.100 Ī¼s (0 allocations: 0 bytes)
mapreduce absā- max: 454.600 Ī¼s (2 allocations: 1.91 MiB)
Tullio: 213.400 Ī¼s (1 allocation: 16 bytes)
Tullio (LoopVect.): 55.900 Ī¼s (1 allocation: 16 bytes)
LoopVect. @turbo: 56.100 Ī¼s (0 allocations: 0 bytes)
LoopVect. @tturbo: 12.700 Ī¼s (0 allocations: 0 bytes)
$ julia maxabstest.jl 1000
VERSION = v"1.6.1"
Threads.nthreads() = 12
N = 1000
simple for loop: 1.053 ms (0 allocations: 0 bytes)
mapreduce absā- max: 2.354 ms (2 allocations: 7.63 MiB)
maximum(abs, a - b): 2.240 ms (2 allocations: 7.63 MiB)
maximum(generator): 1.939 ms (0 allocations: 0 bytes)
maximum splat(absā-): 1.937 ms (0 allocations: 0 bytes)
Tullio: 263.000 Ī¼s (93 allocations: 4.73 KiB)
Tullio (LoopVect.): 257.600 Ī¼s (93 allocations: 4.73 KiB)
LoopVect. @turbo: 410.200 Ī¼s (0 allocations: 0 bytes)
LoopVect. @tturbo: 207.600 Ī¼s (0 allocations: 0 bytes)
$ julia maxabstest.jl 2000
VERSION = v"1.6.1"
Threads.nthreads() = 12
N = 2000
simple for loop: 4.193 ms (0 allocations: 0 bytes)
mapreduce absā- max: 10.075 ms (2 allocations: 30.52 MiB)
maximum(abs, a - b): 11.004 ms (2 allocations: 30.52 MiB)
maximum(generator): 7.903 ms (0 allocations: 0 bytes)
maximum splat(absā-): 7.896 ms (0 allocations: 0 bytes)
Tullio: 2.388 ms (199 allocations: 10.17 KiB)
Tullio (LoopVect.): 2.855 ms (200 allocations: 10.20 KiB)
LoopVect. @turbo: 2.540 ms (0 allocations: 0 bytes)
LoopVect. @tturbo: 2.275 ms (0 allocations: 0 bytes)
Just to mention another option Home Ā· Transducers.jl supports map/reduce operations.