Julia
Julia is a programming language designed for high performance.[1]
Installed Versions
Multiple versions of Julia are installed on Picotte. Use the appropriate modulefile:
julia/1.5.2
julia/1.5.3
julia/1.6.0
julia/1.6.5
julia/1.6.7 (LTS version)
julia/1.7.1
julia/1.7.3
julia/1.8.2
julia/1.8.3
julia/1.8.4
“module avail julia” will show all available versions.
Jupyter Notebook
- See: Julia in Jupyter
Accelerated math/linear algebra with MKL
MKL is Intel's Math Kernel Library
See: https://github.com/JuliaLinearAlgebra/MKL.jl
julia> using Pkg; Pkg.add("MKL")
GPUs using CUDA
See: https://cuda.juliagpu.org/stable/
Example:
[juser@picotte001 ~]$ srun -p gpu --gpus=1 --time=1:00:00 --mem=12G --ntasks=12 --pty /bin/bash
[juser@gpu002 ~]$ module load julia
[juser@gpu002 ~]$ julia
_
_ _ _(_)_ | Documentation: https://docs.julialang.org
(_) | (_) (_) |
_ _ _| |_ __ _ | Type "?" for help, "]?" for Pkg help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 1.8.3 (2022-11-14)
_/ |__'_|_|_|__'_| | Official https://julialang.org/ release
|__/ |
julia> using CUDA
julia> CUDA.versioninfo()
Downloaded artifact: CUDA_compat
Downloaded artifact: CUDA
CUDA toolkit 11.7, artifact installation
NVIDIA driver 470.57.2, for CUDA 11.4
CUDA driver 11.7
Libraries:
- CUBLAS: 11.10.1
- CURAND: 10.2.10
- CUFFT: 10.7.2
- CUSOLVER: 11.3.5
- CUSPARSE: 11.7.3
- CUPTI: 17.0.0
- NVML: 11.0.0+470.57.2
Downloaded artifact: CUDNN
- CUDNN: 8.30.2 (for CUDA 11.5.0)
Downloaded artifact: CUTENSOR
- CUTENSOR: 1.4.0 (for CUDA 11.5.0)
Toolchain:
- Julia: 1.8.3
- LLVM: 13.0.1
- 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, 7.1, 7.2
- 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, sm_86
1 device:
0: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 31.749 GiB available)
julia> Pkg.test("CUDA")
...
┌ Info: System information:
│ CUDA toolkit 11.7, artifact installation
│ NVIDIA driver 470.57.2, for CUDA 11.4
│ CUDA driver 11.7
│
│ Libraries:
│ - CUBLAS: 11.10.1
│ - CURAND: 10.2.10
│ - CUFFT: 10.7.2
│ - CUSOLVER: 11.3.5
│ - CUSPARSE: 11.7.3
│ - CUPTI: 17.0.0
│ - NVML: 11.0.0+470.57.2
│ - CUDNN: 8.30.2 (for CUDA 11.5.0)
│ - CUTENSOR: 1.4.0 (for CUDA 11.5.0)
│
│ Toolchain:
│ - Julia: 1.8.3
│ - LLVM: 13.0.1
│ - 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, 7.1, 7.2
│ - 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, sm_86
│
│ 1 device:
└ 0: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 31.749 GiB available)
[ Info: Testing using 1 device(s): 0. Tesla V100-SXM2-32GB (UUID 554db877-f017-2c7f-5ed4-af8fc0e606ad)
Downloaded artifact: cuQuantum
| | ---------------- GPU ---------------- | ---------------- CPU ---------------- |
Test (Worker) | Time (s) | GC (s) | GC % | Alloc (MB) | RSS (MB) | GC (s) | GC % | Alloc (MB) | RSS (MB) |
initialization (2) | 6.94 | 0.00 | 0.0 | 0.00 | 335.00 | 0.02 | 0.3 | 108.20 | 824.66 |
gpuarrays/indexing scalar (2) | 35.96 | 0.00 | 0.0 | 0.01 | 373.00 | 1.38 | 3.9 | 4348.39 | 824.66 |
...
Julia with CUDA is able to utilize multiple GPU devices:
[juser@gpu005 ~]$ nvidia-smi
...
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 8044 C ...opt/julia/1.8.3/bin/julia 557MiB |
| 1 N/A N/A 8044 C ...opt/julia/1.8.3/bin/julia 557MiB |
| 2 N/A N/A 8044 C ...opt/julia/1.8.3/bin/julia 557MiB |
| 3 N/A N/A 8044 C ...opt/julia/1.8.3/bin/julia 1063MiB |
+-----------------------------------------------------------------------------+
See Also
- Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs, K. Fischer & E. Saba, arXiv:1810.09868 [cs.PL]
- GPU Jobs on Picotte