# Speed up Julia code for simple Monte Carlo Pi estimation (compared to Numba)

**URL:** <https://discourse.julialang.org/t/speed-up-julia-code-for-simple-monte-carlo-pi-estimation-compared-to-numba/59808>\
**Category:** Performance\
**Tags:** performance\
**Created:** [April 22, 2021, 2:25pm UTC](https://discourse.julialang.org/t/speed-up-julia-code-for-simple-monte-carlo-pi-estimation-compared-to-numba/59808 "2021-04-22T14:25:16Z")\
**Posts on this page:** 1\
**Showing post:** 22

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**Author:** ![genkuroki](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/genkuroki/32/18030_2.png) [@genkuroki](https://discourse.julialang.org/u/genkuroki)\
**Post date:** [August 22, 2021, 4:45am UTC](https://discourse.julialang.org/t/speed-up-julia-code-for-simple-monte-carlo-pi-estimation-compared-to-numba/59808/22 "2021-08-22T04:45:45Z")

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In my Win 10 environment, the timing results of the Monte Carlo estimation of \pi by CPU are as follows:

- CPU 1 thread: 223 ms
- CPU 12 threads: 38 ms

I have tried the GPU CUDA version. ([Jupyter notebook](https://github.com/genkuroki/public/blob/main/0017/CUDA%20mcpi.ipynb))

CPU 1 thread version for `Float32`:

```julia
using BenchmarkTools
using Random
function mcpi_f32(n)
    rng = MersenneTwister()
    4count(_ -> rand(rng, Float32)^2 + rand(rng, Float32)^2 ≤ 1, 1:n)/n
end
@show mcpi_f32(10^8)
@btime mcpi_f32(10^8);

```

```julia
mcpi_f32(10 ^ 8) = 3.14160556
  239.164 ms (12 allocations: 19.66 KiB)

```

My very simple only 1 line CUDA version for `Float32`:

```julia
using BenchmarkTools
using CUDA
mcpi_f32_cu(n) = 4count(x -> x^2 + rand(Float32)^2 ≤ 1, CUDA.rand(n))/n
@show mcpi_f32_cu(10^8)
@btime mcpi_f32_cu(10^8);

```

```julia
mcpi_f32_cu(10 ^ 8) = 3.14173908
  6.894 ms (8189 allocations: 257.55 KiB)

```

CPU 1 thread version for `Float64`:

```julia
using BenchmarkTools
using Random
function mcpi_f64(n)
    rng = MersenneTwister()
    4count(_ -> rand(rng)^2 + rand(rng)^2 ≤ 1, 1:n)/n
end
@show mcpi_f64(10^8)
@btime mcpi_f64(10^8);

```

```julia
mcpi_f64(10 ^ 8) = 3.14161428
  225.706 ms (12 allocations: 19.66 KiB)

```

My very simple only 1 line CUDA version for `Float64`:

```julia
using BenchmarkTools
using CUDA
mcpi_f64_cu(n) = 4count(x -> x^2 + rand(Float64)^2 ≤ 1, CUDA.rand(Float64, n))/n
@show mcpi_f64_cu(10^8)
@btime mcpi_f64_cu(10^8);

```

```julia
mcpi_f64_cu(10 ^ 8) = 3.1418442
  12.518 ms (15601 allocations: 489.17 KiB)

```

> **My CUDA versioninfo**
>
> ```julia
> CUDA.versioninfo()
> 
> ```
> 
> ```julia
> CUDA toolkit 11.4.1, artifact installation
> CUDA driver 11.2.0
> NVIDIA driver 462.31.0
> 
> Libraries: 
> - CUBLAS: 11.5.4
> - CURAND: 10.2.5
> - CUFFT: 10.5.1
> - CUSOLVER: 11.2.0
> - CUSPARSE: 11.6.0
> - CUPTI: 14.0.0
> - NVML: 11.0.0+462.31
> - CUDNN: 8.20.2 (for CUDA 11.4.0)
> - CUTENSOR: 1.3.0 (for CUDA 11.2.0)
> 
> Toolchain:
> - Julia: 1.6.2
> - LLVM: 11.0.1
> - PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
> - Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80
> 
> 1 device:
> 0: GeForce GTX 1650 Ti (sm_75, 833.594 MiB / 4.000 GiB available)
> 
> ```

**Summary:** n = 10^8

Previous post:

- Float64 CPU 1 thread: 223 ms
- Float64 CPU 12 threads: 38 ms

Only 1 line versions:

- Float32 CPU 1 thread: 239 ms
- Float32 GPU CUDA: 6.9 ms
- Float64 CPU 1 thread: 226 ms
- Float64 GPU CUDA: 12.5 ms

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