# Flux and cpu cores

**URL:** https://discourse.julialang.org/t/flux-and-cpu-cores/45318
**Category:** Machine Learning
**Tags:** parallel, multithreading, flux
**Created:** [August 21, 2020, 10:53am UTC](https://discourse.julialang.org/t/flux-and-cpu-cores/45318 "2020-08-21T10:53:12Z")
**Posts on this page:** 1
**Showing post:** 7

<div class="post-metadata">

### Author: ![johnbb](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/johnbb/32/34233_2.png) [@johnbb](https://discourse.julialang.org/u/johnbb)
#### Post date: [September 1, 2020, 9:29am UTC](https://discourse.julialang.org/t/flux-and-cpu-cores/45318/7 "2020-09-01T09:29:57Z")

</div>

I ran a few tests which confirm that `BLAS.set_num_threads(1)` should be set. On my system the code ran ~10 times faster. Please see below for codes and results

```julia
# start Julia with JULIA_NUM_THREADS=1 julia
using Flux
using BenchmarkTools
using LinearAlgebra
n = 100_000
p = 50
x = rand(Float32, p, n)
y = rand(Float32, n)    
trdata = Flux.Data.DataLoader(x, y, batchsize=100)
m = [Chain(Dense(p, 100), Dense(100,100), Dense(100,1)) for i in 1:4]
@btime for i in 1:4
    loss(x, y) = Flux.mse(m[i](x), y)
    Flux.@epochs 1 Flux.train!(loss, Flux.params(m[i]), trdata, Flux.ADAM())
end
# 6.286 s (1992500 allocations: 2.24 GiB)

# start Julia with JULIA_NUM_THREADS=4 julia
using Flux
using BenchmarkTools
using LinearAlgebra
n = 100_000
p = 50
x = rand(Float32, p, n)
y = rand(Float32, n)    
trdata = Flux.Data.DataLoader(x, y, batchsize=100)
m = [Chain(Dense(p, 100), Dense(100,100), Dense(100,1)) for i in 1:4]
@btime Threads.@threads for i in 1:4
    loss(x, y) = Flux.mse(m[i](x), y)
    Flux.@epochs 1 Flux.train!(loss, Flux.params(m[i]), trdata, Flux.ADAM())
end
# 10.864 s (1992523 allocations: 2.24 GiB)  

# start Julia with JULIA_NUM_THREADS=4 julia
using Flux
using BenchmarkTools
using LinearAlgebra
BLAS.set_num_threads(1)
n = 100_000
p = 50
x = rand(Float32, p, n)
y = rand(Float32, n)    
trdata = Flux.Data.DataLoader(x, y, batchsize=100)
m = [Chain(Dense(p, 100), Dense(100,100), Dense(100,1)) for i in 1:4]
@btime Threads.@threads for i in 1:4
    loss(x, y) = Flux.mse(m[i](x), y)
    Flux.@epochs 1 Flux.train!(loss, Flux.params(m[i]), trdata, Flux.ADAM())
end
# 1.076 s (1992515 allocations: 2.24 GiB)

```

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