Use addprocs() for DistributedStream

.github/workflows/main.yaml

@@ -13,14 +13,19 @@ jobs:
       - name: Setup project
         run: julia             --project -e 'import Pkg; Pkg.instantiate()'
       - name: Test run PlainStream.jl
+        if: ${{ ! cancelled() }}
         run: julia             --project src/PlainStream.jl       --arraysize 100
       - name: Test run ThreadedStream.jl
+        if: ${{ ! cancelled() }}
         run: julia --threads 2 --project src/ThreadedStream.jl    --arraysize 100
       - name: Test run DistributedStream.jl
+        if: ${{ ! cancelled() }}
         run: julia         -p2 --project src/DistributedStream.jl --arraysize 100
       - name: Test run CUDAStream.jl
+        if: ${{ ! cancelled() }}
         run: julia             --project src/CUDAStream.jl        --list
       - name: Test run AMDGPUStream.jl
+        if: ${{ ! cancelled() }}
         run: julia             --project src/AMDGPUStream.jl      --list
 
   test:

JuliaStream.jl/README.md

@@ -26,5 +26,5 @@ With Julia on path, run the benchmark with:
 **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=$(nproc)`) otherwise it defaults to 1
- * You must *prepend* the number of processes needed for `DistributedStream`, e.g `julia -p$(nproc) --project src/DistributedStream.jl`
+ * `DistributedStream` uses `addprocs()` call directly which defaults to `$(nproc)`, **do not use the `-p <N>` flag** 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.

JuliaStream.jl/src/DistributedStream.jl

@@ -2,6 +2,8 @@ using Distributed
 
 include("Stream.jl")
 
+addprocs()
+
 @everywhere include("StreamData.jl")
 @everywhere using SharedArrays
 @everywhere const SharedArrayData = StreamData{T,SharedArray{T}} where {T}