31 lines
1.4 KiB
Markdown
31 lines
1.4 KiB
Markdown
JuliaStream.jl
|
|
==============
|
|
|
|
This is an implementation of BabelStream in Julia which contains the following variants:
|
|
|
|
* `PlainStream.jl` - Single threaded `for`
|
|
* `ThreadedStream.jl` - Threaded implementation with `Threads.@threads` macros
|
|
* `DistributedStream.jl` - Process based parallelism with `@distributed` macros
|
|
* `CUDAStream.jl` - Direct port of BabelStream's native CUDA implementation using [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl)
|
|
* `AMDGPUStream.jl` - Direct port of BabelStream's native HIP implementation using [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl)
|
|
|
|
### Build & Run
|
|
|
|
Prerequisites
|
|
|
|
* Julia >= 1.6+
|
|
|
|
With Julia on path, run the benchmark with:
|
|
|
|
```shell
|
|
> cd JuliaStream.jl
|
|
> julia --project -e 'import Pkg; Pkg.instantiate()' # only required on first run
|
|
> julia --project src/<IMPL>Stream.jl
|
|
```
|
|
|
|
**Important:**
|
|
* Julia is 1-indexed, so N >= 1 in `--device N`.
|
|
* Thread count for `ThreadedStream` must be set via the `JULIA_NUM_THREADS` environment variable (e.g `export JULIA_NUM_THREADS=$(nproc)`) otherwise it defaults to 1.
|
|
* Worker count for `DistributedStream` is set with `-p <N>` as per the [documentation](https://docs.julialang.org/en/v1/manual/distributed-computing).
|
|
* Certain implementations such as CUDA and AMDGPU will do hardware detection at runtime and may download and/or compile further software packages for the platform.
|