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Merge pull request #106 from UoB-HPC/julia

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Julia implementation
This commit is contained in:
Tom Deakin 2021-11-25 13:07:27 +00:00 committed by GitHub
commit d1b1722acf
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28
.github/workflows/main.yaml vendored
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@ -3,6 +3,34 @@ on: [push, pull_request]
jobs: jobs:
test-julia:
runs-on: ubuntu-18.04
defaults:
run:
working-directory: ./JuliaStream.jl
steps:
- uses: actions/checkout@v2
- 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 2048
- name: Test run ThreadedStream.jl
if: ${{ ! cancelled() }}
run: julia --threads 2 --project src/ThreadedStream.jl --arraysize 2048
- name: Test run DistributedStream.jl (no flag)
if: ${{ ! cancelled() }}
run: julia --project src/DistributedStream.jl --arraysize 2048
- name: Test run DistributedStream.jl (-p 2)
if: ${{ ! cancelled() }}
run: julia -p 2 --project src/DistributedStream.jl --arraysize 2048
- 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: test:
runs-on: ubuntu-18.04 runs-on: ubuntu-18.04
steps: steps:

2
JuliaStream.jl/.JuliaFormatter.toml Normal file
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indent = 2
margin = 100

5
JuliaStream.jl/.gitignore vendored Normal file
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*.jl.cov
*.jl.*.cov
*.jl.mem
/docs/build/
/docs/Manifest.toml

415
JuliaStream.jl/AMDGPU/Manifest.toml Normal file
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@ -0,0 +1,415 @@
# This file is machine-generated - editing it directly is not advised
[[AMDGPU]]
deps = ["AbstractFFTs", "Adapt", "BinaryProvider", "CEnum", "GPUArrays", "GPUCompiler", "HIP_jll", "LLVM", "Libdl", "LinearAlgebra", "MacroTools", "Pkg", "Printf", "ROCmDeviceLibs_jll", "Random", "Requires", "Setfield", "hsa_rocr_jll"]
git-tree-sha1 = "d64c97447a753cfbf0158d6c7be513f34526d559"
uuid = "21141c5a-9bdb-4563-92ae-f87d6854732e"
version = "0.2.12"
[[AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.0.1"
[[Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "3.3.1"
[[ArgParse]]
deps = ["Logging", "TextWrap"]
git-tree-sha1 = "3102bce13da501c9104df33549f511cd25264d7d"
uuid = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
version = "1.1.4"
[[ArgTools]]
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[[Base64]]
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[[BinaryProvider]]
deps = ["Libdl", "Logging", "SHA"]
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uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
version = "0.5.10"
[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
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[[ConstructionBase]]
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version = "1.3.0"
[[Dates]]
deps = ["Printf"]
uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
[[Downloads]]
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[[Elfutils_jll]]
deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Pkg", "XZ_jll", "Zlib_jll", "argp_standalone_jll", "fts_jll", "obstack_jll"]
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[[GPUArrays]]
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[[GPUCompiler]]
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git-tree-sha1 = "4ed2616d5e656c8716736b64da86755467f26cf5"
uuid = "61eb1bfa-7361-4325-ad38-22787b887f55"
version = "0.12.9"
[[HIP_jll]]
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git-tree-sha1 = "5097d8f7b6842156ab0928371b3d03fefd8decab"
uuid = "2696aab5-0948-5276-aa9a-2a86a37016b8"
version = "4.0.0+1"
[[InteractiveUtils]]
deps = ["Markdown"]
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
[[JLLWrappers]]
deps = ["Preferences"]
git-tree-sha1 = "642a199af8b68253517b80bd3bfd17eb4e84df6e"
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[[LLVM]]
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[[LLVMExtra_jll]]
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git-tree-sha1 = "9c360e5ce980b88bb31a7b086dbb19469008154b"
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version = "0.0.10+0"
[[LibCURL]]
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uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
[[LibCURL_jll]]
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uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
[[LibGit2]]
deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
[[LibSSH2_jll]]
deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
[[Libdl]]
uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
[[Libgcrypt_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll", "Pkg"]
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version = "1.8.7+0"
[[Libglvnd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"]
git-tree-sha1 = "7739f837d6447403596a75d19ed01fd08d6f56bf"
uuid = "7e76a0d4-f3c7-5321-8279-8d96eeed0f29"
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[[Libgpg_error_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
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[[Libiconv_jll]]
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git-tree-sha1 = "42b62845d70a619f063a7da093d995ec8e15e778"
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[[LinearAlgebra]]
deps = ["Libdl"]
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[[Logging]]
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[[MacroTools]]
deps = ["Markdown", "Random"]
git-tree-sha1 = "0fb723cd8c45858c22169b2e42269e53271a6df7"
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[[Markdown]]
deps = ["Base64"]
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[[MbedTLS_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
[[MozillaCACerts_jll]]
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[[NUMA_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "778f9bd14400cff2c32ed357e12766ac0e3d766e"
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[[NetworkOptions]]
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[[Parameters]]
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7
JuliaStream.jl/AMDGPU/Project.toml Normal file
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@ -0,0 +1,7 @@
[deps]
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316
JuliaStream.jl/CUDA/Manifest.toml Normal file
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@ -0,0 +1,316 @@
# This file is machine-generated - editing it directly is not advised
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7
JuliaStream.jl/CUDA/Project.toml Normal file
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@ -0,0 +1,7 @@
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547
JuliaStream.jl/KernelAbstractions/Manifest.toml Normal file
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@ -0,0 +1,547 @@
# This file is machine-generated - editing it directly is not advised
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uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
[[IrrationalConstants]]
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uuid = "92d709cd-6900-40b7-9082-c6be49f344b6"
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[[JLLWrappers]]
deps = ["Preferences"]
git-tree-sha1 = "642a199af8b68253517b80bd3bfd17eb4e84df6e"
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[[KernelAbstractions]]
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git-tree-sha1 = "5e6c70389c1b1e40adb81664ca8cea6ce8127afc"
uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
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[[LLVM]]
deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"]
git-tree-sha1 = "23a47d417a3cd9c2e73c854bac7dd4731c105ef7"
uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
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[[LLVMExtra_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "9c360e5ce980b88bb31a7b086dbb19469008154b"
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[[LazyArtifacts]]
deps = ["Artifacts", "Pkg"]
uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
[[LibCURL]]
deps = ["LibCURL_jll", "MozillaCACerts_jll"]
uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
[[LibCURL_jll]]
deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
[[LibGit2]]
deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
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[[LibSSH2_jll]]
deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
[[Libdl]]
uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
[[Libgcrypt_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll", "Pkg"]
git-tree-sha1 = "64613c82a59c120435c067c2b809fc61cf5166ae"
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[[Libglvnd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"]
git-tree-sha1 = "7739f837d6447403596a75d19ed01fd08d6f56bf"
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[[Libgpg_error_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
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[[Libiconv_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "42b62845d70a619f063a7da093d995ec8e15e778"
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[[LinearAlgebra]]
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[[LogExpFunctions]]
deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
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[[Logging]]
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[[MacroTools]]
deps = ["Markdown", "Random"]
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[[Markdown]]
deps = ["Base64"]
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[[MbedTLS_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
[[Mmap]]
uuid = "a63ad114-7e13-5084-954f-fe012c677804"
[[MozillaCACerts_jll]]
uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
[[NUMA_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "778f9bd14400cff2c32ed357e12766ac0e3d766e"
uuid = "7f51dc2b-bb24-59f8-b771-bb1490e4195d"
version = "2.0.13+1"
[[NetworkOptions]]
uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
[[OpenSpecFun_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "13652491f6856acfd2db29360e1bbcd4565d04f1"
uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
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[[OrderedCollections]]
git-tree-sha1 = "85f8e6578bf1f9ee0d11e7bb1b1456435479d47c"
uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
version = "1.4.1"
[[Parameters]]
deps = ["OrderedCollections", "UnPack"]
git-tree-sha1 = "2276ac65f1e236e0a6ea70baff3f62ad4c625345"
uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a"
version = "0.12.2"
[[Pkg]]
deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
[[Preferences]]
deps = ["TOML"]
git-tree-sha1 = "00cfd92944ca9c760982747e9a1d0d5d86ab1e5a"
uuid = "21216c6a-2e73-6563-6e65-726566657250"
version = "1.2.2"
[[Printf]]
deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
[[REPL]]
deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
[[ROCKernels]]
deps = ["AMDGPU", "Adapt", "Cassette", "KernelAbstractions", "SpecialFunctions", "StaticArrays"]
git-tree-sha1 = "41105b861342637dde17797bdd9aaa537aca646b"
uuid = "7eb9e9f0-4bd3-4c4c-8bef-26bd9629d9b9"
version = "0.2.0"
[[ROCmCompilerSupport_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "ROCmDeviceLibs_jll", "hsa_rocr_jll"]
git-tree-sha1 = "56ddcfb5d8b60c9f8c1bc619886f8d363fd1926d"
uuid = "8fbdd1d2-db62-5cd0-981e-905da1486e17"
version = "4.0.0+1"
[[ROCmDeviceLibs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "d764f0f28b5af89aa004871a6a38e5d061f77257"
uuid = "873c0968-716b-5aa7-bb8d-d1e2e2aeff2d"
version = "4.0.0+0"
[[ROCmOpenCLRuntime_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "ROCmCompilerSupport_jll", "ROCmDeviceLibs_jll", "Xorg_libX11_jll", "Xorg_xorgproto_jll", "hsa_rocr_jll"]
git-tree-sha1 = "f9e3e2cb40a7990535efa7da9b9dd0e0b458a973"
uuid = "10ae2a08-2eea-53f8-8c20-eec175020e9f"
version = "4.0.0+1"
[[Random]]
deps = ["Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[[Random123]]
deps = ["Libdl", "Random", "RandomNumbers"]
git-tree-sha1 = "0e8b146557ad1c6deb1367655e052276690e71a3"
uuid = "74087812-796a-5b5d-8853-05524746bad3"
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[[RandomNumbers]]
deps = ["Random", "Requires"]
git-tree-sha1 = "043da614cc7e95c703498a491e2c21f58a2b8111"
uuid = "e6cf234a-135c-5ec9-84dd-332b85af5143"
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[[Reexport]]
git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
version = "1.2.2"
[[Requires]]
deps = ["UUIDs"]
git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
version = "1.1.3"
[[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
[[Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
[[Setfield]]
deps = ["ConstructionBase", "Future", "MacroTools", "Requires"]
git-tree-sha1 = "fca29e68c5062722b5b4435594c3d1ba557072a3"
uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46"
version = "0.7.1"
[[SharedArrays]]
deps = ["Distributed", "Mmap", "Random", "Serialization"]
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
[[Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
[[SparseArrays]]
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]]
deps = ["ChainRulesCore", "LogExpFunctions", "OpenSpecFun_jll"]
git-tree-sha1 = "a322a9493e49c5f3a10b50df3aedaf1cdb3244b7"
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[[StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
git-tree-sha1 = "3240808c6d463ac46f1c1cd7638375cd22abbccb"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
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[[Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
[[Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
[[Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[TextWrap]]
git-tree-sha1 = "9250ef9b01b66667380cf3275b3f7488d0e25faf"
uuid = "b718987f-49a8-5099-9789-dcd902bef87d"
version = "1.0.1"
[[TimerOutputs]]
deps = ["ExprTools", "Printf"]
git-tree-sha1 = "209a8326c4f955e2442c07b56029e88bb48299c7"
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
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[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"
[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"
[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"
[[XZ_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "a921669cd9a45c23031fd4eb904f5cc3d20de415"
uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800"
version = "5.2.5+2"
[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"
[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"
[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"
[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"
[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"
[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"
[[Xorg_xorgproto_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "9a9eb8ce756fe0bca01b4be16da770e18d264972"
uuid = "c4d99508-4286-5418-9131-c86396af500b"
version = "2019.2.0+2"
[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"
[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
[[argp_standalone_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "feaf9f6293003c2bf53056fd6930d677ed340b34"
uuid = "c53206cc-00f7-50bf-ad1e-3ae1f6e49bc3"
version = "1.3.1+0"
[[fts_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "78732b942383d2cb521df8a1a0814911144e663d"
uuid = "d65627f6-89bd-53e8-8ab5-8b75ff535eee"
version = "1.2.7+1"
[[hsa_rocr_jll]]
deps = ["Artifacts", "Elfutils_jll", "JLLWrappers", "Libdl", "NUMA_jll", "Pkg", "Zlib_jll", "hsakmt_roct_jll"]
git-tree-sha1 = "df8d73efec8b1e53ad527d208f5343c0368f0fcd"
uuid = "dd59ff1a-a01a-568d-8b29-0669330f116a"
version = "4.0.0+0"
[[hsakmt_roct_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "NUMA_jll", "Pkg"]
git-tree-sha1 = "80e0c9940e15cfd6f1f1e9d9f3953ec4d48d3d4a"
uuid = "1cecccd7-a9b6-5045-9cdc-a44c19b16d76"
version = "4.0.0+0"
[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
[[obstack_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "1c4a6b66e934fc6db4649cb2910c72f53bbfea7e"
uuid = "c88a4935-d25e-5644-aacc-5db6f1b8ef79"
version = "1.2.2+0"
[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

11
JuliaStream.jl/KernelAbstractions/Project.toml Normal file
View File

@ -0,0 +1,11 @@
[deps]
AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
CUDAKernels = "72cfdca4-0801-4ab0-bf6a-d52aa10adc57"
KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
ROCKernels = "7eb9e9f0-4bd3-4c4c-8bef-26bd9629d9b9"
[compat]
julia = "1.6"

493
JuliaStream.jl/Manifest.toml Normal file
View File

@ -0,0 +1,493 @@
# This file is machine-generated - editing it directly is not advised
[[AMDGPU]]
deps = ["AbstractFFTs", "Adapt", "BinaryProvider", "CEnum", "GPUArrays", "GPUCompiler", "LLVM", "Libdl", "LinearAlgebra", "MacroTools", "Printf", "Random", "Requires", "Setfield", "hsa_rocr_jll", "hsakmt_roct_jll"]
git-tree-sha1 = "04fdb3923ac6f55fa7347dce0f0f6f10e321e2e9"
uuid = "21141c5a-9bdb-4563-92ae-f87d6854732e"
version = "0.2.7"
[[AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.0.1"
[[Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "3.3.1"
[[ArgParse]]
deps = ["Logging", "TextWrap"]
git-tree-sha1 = "3102bce13da501c9104df33549f511cd25264d7d"
uuid = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
version = "1.1.4"
[[ArgTools]]
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[[Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
[[BFloat16s]]
deps = ["LinearAlgebra", "Test"]
git-tree-sha1 = "4af69e205efc343068dc8722b8dfec1ade89254a"
uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b"
version = "0.1.0"
[[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
[[BinaryProvider]]
deps = ["Libdl", "Logging", "SHA"]
git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058"
uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
version = "0.5.10"
[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
version = "1.0.8+0"
[[CEnum]]
git-tree-sha1 = "215a9aa4a1f23fbd05b92769fdd62559488d70e9"
uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
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19
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name = "JuliaStream"
uuid = "1bdcc9b7-f5ed-4705-bc7b-be1b748ec681"
authors = ["Wei-Chen Lin <wl14928@bristol.ac.uk>"]
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[compat]
julia = "1.6"

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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)
* `oneAPIStream.jl` - Direct port of BabelStream's native SYCL implementation using [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl)
* `KernelAbstractions.jl` - Direct port of miniBUDE's native CUDA implementation using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl)
### Build & Run
Prerequisites
* Julia >= 1.6+
A set of reduced dependency projects are available for the following backend and implementations:
* `AMDGPU` supports:
- `AMDGPUStream.jl`
* `CUDA` supports:
- `CUDAStream.jl`
* `oneAPI` supports:
- `oneAPIStream.jl`
* `KernelAbstractions` supports:
- `KernelAbstractionsStream.jl`
* `Threaded` supports:
- `PlainStream.jl`
- `ThreadedStream.jl`
- `DistributedStream.jl`
With Julia on path, run your selected benchmark with:
```shell
> cd JuliaStream.jl
> julia --project=<BACKEND> -e 'import Pkg; Pkg.instantiate()' # only required on first run
> julia --project=<BACKEND> src/<IMPL>Stream.jl
```
For example. to run the CUDA implementation:
```shell
> cd JuliaStream.jl
> julia --project=CUDA -e 'import Pkg; Pkg.instantiate()'
> julia --project=CUDA src/CUDAStream.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.
***
Alternatively, the top-level project `Project.toml` contains all dependencies needed to run all implementations in `src`.
There may be instances where some packages are locked to an older version because of transitive dependency requirements.
To run the benchmark using the top-level project, run the benchmark with:
```shell
> cd JuliaStream.jl
> julia --project -e 'import Pkg; Pkg.instantiate()'
> julia --project src/<IMPL>Stream.jl
```

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7
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# AMDGPU.jl doesn't support CPU agents, so this isn't a feature-complete ROCmStream, only AMD GPUs
include("Stream.jl")
using AMDGPU
const ROCData = StreamData{T,ROCArray{T}} where {T}
const TBSize = 1024::Int
const DotBlocks = 256::Int
function devices()::Vector{DeviceWithRepr}
try
# AMDGPU.agents()'s internal iteration order isn't stable
sorted = sort(AMDGPU.get_agents(:gpu), by = repr)
map(x -> (x, repr(x), "AMDGPU.jl"), sorted)
catch
# probably unsupported
String[]
end
end
function make_stream(
arraysize::Int,
scalar::T,
device::DeviceWithRepr,
silent::Bool,
)::Tuple{ROCData{T},Nothing} where {T}
if arraysize % TBSize != 0
error("arraysize ($(arraysize)) must be divisible by $(TBSize)!")
end
# XXX AMDGPU doesn't expose an API for setting the default like CUDA.device!()
# but AMDGPU.get_default_agent returns DEFAULT_AGENT so we can do it by hand
AMDGPU.DEFAULT_AGENT[] = device[1]
selected = AMDGPU.get_default_agent()
if !silent
println("Using GPU HSA device: $(AMDGPU.get_name(selected)) ($(repr(selected)))")
println("Kernel parameters : <<<$(arraysize),$(TBSize)>>>")
end
return (
ROCData{T}(
ROCArray{T}(undef, arraysize),
ROCArray{T}(undef, arraysize),
ROCArray{T}(undef, arraysize),
scalar,
arraysize,
),
nothing,
)
end
function init_arrays!(data::ROCData{T}, _, init::Tuple{T,T,T}) where {T}
AMDGPU.fill!(data.a, init[1])
AMDGPU.fill!(data.b, init[2])
AMDGPU.fill!(data.c, init[3])
end
function copy!(data::ROCData{T}, _) where {T}
function kernel(a::AbstractArray{T}, c::AbstractArray{T})
i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x # only workgroupIdx starts at 1
@inbounds c[i] = a[i]
return
end
AMDGPU.wait(
@roc groupsize = TBSize gridsize = data.size kernel(data.a, data.c)
)
end
function mul!(data::ROCData{T}, _) where {T}
function kernel(b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x # only workgroupIdx starts at 1
@inbounds b[i] = scalar * c[i]
return
end
AMDGPU.wait(
@roc groupsize = TBSize gridsize = data.size kernel(data.b, data.c, data.scalar)
)
end
function add!(data::ROCData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T})
i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x # only workgroupIdx starts at 1
@inbounds c[i] = a[i] + b[i]
return
end
AMDGPU.wait(
@roc groupsize = TBSize gridsize = data.size kernel(data.a, data.b, data.c)
)
end
function triad!(data::ROCData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x # only workgroupIdx starts at 1
@inbounds a[i] = b[i] + (scalar * c[i])
return
end
AMDGPU.wait(
@roc groupsize = TBSize gridsize = data.size kernel(
data.a,
data.b,
data.c,
data.scalar,
)
)
end
function nstream!(data::ROCData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x # only workgroupIdx starts at 1
@inbounds a[i] += b[i] + scalar * c[i]
return
end
AMDGPU.wait(
@roc groupsize = TBSize gridsize = data.size kernel(
data.a,
data.b,
data.c,
data.scalar,
)
)
end
function dot(data::ROCData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, size::Int, partial::AbstractArray{T})
tb_sum = ROCDeviceArray((TBSize,), alloc_local(:reduce, T, TBSize))
local_i = workitemIdx().x
@inbounds tb_sum[local_i] = 0.0
# do dot first
i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x # only workgroupIdx starts at 1
while i <= size
@inbounds tb_sum[local_i] += a[i] * b[i]
i += TBSize * DotBlocks # XXX don't use (workgroupDim().x * gridDimWG().x) here
end
# then tree reduction
offset = workgroupDim().x ÷ 2
while offset > 0
sync_workgroup()
if (local_i - 1) < offset
@inbounds tb_sum[local_i] += tb_sum[local_i+offset]
end
offset ÷= 2
end
if (local_i == 1)
@inbounds partial[workgroupIdx().x] = tb_sum[local_i]
end
return
end
partial_sum = ROCArray{T}(undef, DotBlocks)
AMDGPU.wait(
@roc groupsize = TBSize gridsize = TBSize * DotBlocks kernel(
data.a,
data.b,
data.size,
partial_sum,
)
)
return sum(partial_sum)
end
function read_data(data::ROCData{T}, _)::VectorData{T} where {T}
return VectorData{T}(data.a, data.b, data.c, data.scalar, data.size)
end
main()

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include("Stream.jl")
using CUDA
const CuData = StreamData{T,CuArray{T}} where {T}
const TBSize = 1024::Int
const DotBlocks = 256::Int
function devices()::Vector{DeviceWithRepr}
return !CUDA.functional(false) ? String[] :
map(d -> (d, "$(CUDA.name(d)) ($(repr(d)))", "CUDA.jl"), CUDA.devices())
end
function make_stream(
arraysize::Int,
scalar::T,
device::DeviceWithRepr,
silent::Bool,
)::Tuple{CuData{T},Nothing} where {T}
if arraysize % TBSize != 0
error("arraysize ($(arraysize)) must be divisible by $(TBSize)!")
end
CUDA.device!(device[1])
selected = CUDA.device()
# show_reason is set to true here so it dumps CUDA info
# for us regardless of whether it's functional
if !CUDA.functional(true)
error("Non-functional CUDA configuration")
end
if !silent
println("Using CUDA device: $(CUDA.name(selected)) ($(repr(selected)))")
println("Kernel parameters: <<<$(arraysize ÷ TBSize),$(TBSize)>>>")
end
return (
CuData{T}(
CuArray{T}(undef, arraysize),
CuArray{T}(undef, arraysize),
CuArray{T}(undef, arraysize),
scalar,
arraysize,
),
nothing,
)
end
function init_arrays!(data::CuData{T}, _, init::Tuple{T,T,T}) where {T}
fill!(data.a, init[1])
fill!(data.b, init[2])
fill!(data.c, init[3])
end
function copy!(data::CuData{T}, _) where {T}
function kernel(a::AbstractArray{T}, c::AbstractArray{T})
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x
@inbounds c[i] = a[i]
return
end
@cuda blocks = data.size ÷ TBSize threads = TBSize kernel(data.a, data.c)
CUDA.synchronize()
end
function mul!(data::CuData{T}, _) where {T}
function kernel(b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x
@inbounds b[i] = scalar * c[i]
return
end
@cuda blocks = data.size ÷ TBSize threads = TBSize kernel(data.b, data.c, data.scalar)
CUDA.synchronize()
end
function add!(data::CuData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T})
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x
@inbounds c[i] = a[i] + b[i]
return
end
@cuda blocks = data.size ÷ TBSize threads = TBSize kernel(data.a, data.b, data.c)
CUDA.synchronize()
end
function triad!(data::CuData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x
@inbounds a[i] = b[i] + (scalar * c[i])
return
end
@cuda blocks = data.size ÷ TBSize threads = TBSize kernel(
data.a,
data.b,
data.c,
data.scalar,
)
CUDA.synchronize()
end
function nstream!(data::CuData{T}, _) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x
@inbounds a[i] += b[i] + scalar * c[i]
return
end
@cuda blocks = data.size ÷ TBSize threads = TBSize kernel(
data.a,
data.b,
data.c,
data.scalar,
)
CUDA.synchronize()
end
function dot(data::CuData{T}, _) where {T}
# direct port of the reduction in CUDAStream.cu
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, size::Int, partial::AbstractArray{T})
tb_sum = @cuStaticSharedMem(T, TBSize)
local_i = threadIdx().x
@inbounds tb_sum[local_i] = 0.0
# do dot first
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x
while i <= size
@inbounds tb_sum[local_i] += a[i] * b[i]
i += blockDim().x * gridDim().x
end
# then tree reduction
offset = blockDim().x ÷ 2
while offset > 0
sync_threads()
if (local_i - 1) < offset
@inbounds tb_sum[local_i] += tb_sum[local_i+offset]
end
offset ÷= 2
end
if (local_i == 1)
@inbounds partial[blockIdx().x] = tb_sum[local_i]
end
return
end
partial_sum = CuArray{T}(undef, DotBlocks)
@cuda blocks = DotBlocks threads = TBSize kernel(data.a, data.b, data.size, partial_sum)
return sum(partial_sum)
end
function read_data(data::CuData{T}, _)::VectorData{T} where {T}
return VectorData{T}(data.a, data.b, data.c, data.scalar, data.size)
end
main()

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using Distributed
@everywhere using Pkg
@everywhere Pkg.activate("."; io = devnull) # don't spam `Activating environment at...`
@everywhere include("StreamData.jl")
@everywhere include("Stream.jl")
@everywhere using SharedArrays
@everywhere const SharedArrayData = StreamData{T,SharedArray{T}} where {T}
function devices()::Vector{DeviceWithRepr}
return [(undef, "CPU (localhost) $(nworkers())P", "Distributed.jl")]
end
function make_stream(
arraysize::Int,
scalar::T,
_::DeviceWithRepr,
silent::Bool,
)::Tuple{SharedArrayData{T},Nothing} where {T}
if !silent
println("Using max $(nworkers()) process(es) + 1 master")
end
return (
SharedArrayData{T}(
SharedArray{T}(arraysize),
SharedArray{T}(arraysize),
SharedArray{T}(arraysize),
scalar,
arraysize,
),
nothing,
)
end
function init_arrays!(data::SharedArrayData{T}, _, init::Tuple{T,T,T}) where {T}
@sync @distributed for i = 1:data.size
@inbounds data.a[i] = init[1]
@inbounds data.b[i] = init[2]
@inbounds data.c[i] = init[3]
end
end
function copy!(data::SharedArrayData{T}, _) where {T}
@sync @distributed for i = 1:data.size
@inbounds data.c[i] = data.a[i]
end
end
function mul!(data::SharedArrayData{T}, _) where {T}
@sync @distributed for i = 1:data.size
@inbounds data.b[i] = data.scalar * data.c[i]
end
end
function add!(data::SharedArrayData{T}, _) where {T}
@sync @distributed for i = 1:data.size
@inbounds data.c[i] = data.a[i] + data.b[i]
end
end
function triad!(data::SharedArrayData{T}, _) where {T}
@sync @distributed for i = 1:data.size
@inbounds data.a[i] = data.b[i] + (data.scalar * data.c[i])
end
end
function nstream!(data::SharedArrayData{T}, _) where {T}
@sync @distributed for i = 1:data.size
@inbounds data.a[i] += data.b[i] + data.scalar * data.c[i]
end
end
function dot(data::SharedArrayData{T}, _) where {T}
return @distributed (+) for i = 1:data.size
@inbounds data.a[i] * data.b[i]
end
end
function read_data(data::SharedArrayData{T}, _)::VectorData{T} where {T}
return VectorData{T}(data.a, data.b, data.c, data.scalar, data.size)
end
main()

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module JuliaStream
end
println("Please run benchmarks directly via `julia --project src/<IMPL>Stream.jl`")

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using ROCKernels, CUDAKernels, KernelAbstractions, CUDA, AMDGPU
include("Stream.jl")
const CuData = StreamData{T,CUDA.CuArray{T}} where {T}
const ROCData = StreamData{T,AMDGPU.ROCArray{T}} where {T}
const TBSize = 1024::Int
const DotBlocks = 256::Int
@enum Backend cuda rocm cpu
struct Context
backend::Backend
device::Device
end
function list_rocm_devices()::Vector{DeviceWithRepr}
try
# AMDGPU.agents()'s internal iteration order isn't stable
sorted = sort(AMDGPU.get_agents(:gpu), by = repr)
map(x -> (x, repr(x), rocm), sorted)
catch
# probably unsupported
[]
end
end
function list_cuda_devices()::Vector{DeviceWithRepr}
return !CUDA.functional(false) ? String[] :
map(d -> (d, "$(CUDA.name(d)) ($(repr(d)))", cuda), CUDA.devices())
end
function devices()::Vector{DeviceWithRepr}
cudas = list_cuda_devices()
rocms = list_rocm_devices()
cpus = [(undef, "$(Sys.cpu_info()[1].model) ($(Threads.nthreads())T)", cpu)]
vcat(cpus, cudas, rocms)
end
function make_stream(
arraysize::Int,
scalar::T,
device::DeviceWithRepr,
silent::Bool,
) where {T}
if arraysize % TBSize != 0
error("arraysize ($(arraysize)) must be divisible by $(TBSize)!")
end
(selected, _, backend) = device
if backend == cpu
if !silent
println("Using CPU with max $(Threads.nthreads()) threads")
end
partialsum = Vector{T}(undef, DotBlocks)
data = VectorData{T}(
Vector{T}(undef, arraysize),
Vector{T}(undef, arraysize),
Vector{T}(undef, arraysize),
scalar,
arraysize,
)
backenddevice = CPU()
elseif backend == cuda
CUDA.device!(selected)
if CUDA.device() != selected
error("Cannot select CUDA device, expecting $selected, but got $(CUDA.device())")
end
if !CUDA.functional(true)
error("Non-functional CUDA configuration")
end
if !silent
println("Using CUDA device: $(CUDA.name(selected)) ($(repr(selected)))")
end
partialsum = CuArray{T}(undef, DotBlocks)
data = CuData{T}(
CuArray{T}(undef, arraysize),
CuArray{T}(undef, arraysize),
CuArray{T}(undef, arraysize),
scalar,
arraysize,
)
backenddevice = CUDADevice()
elseif backend == rocm
AMDGPU.DEFAULT_AGENT[] = selected
if AMDGPU.get_default_agent() != selected
error(
"Cannot select HSA device, expecting $selected, but got $(AMDGPU.get_default_agent())",
)
end
if !silent
println("Using GPU HSA device: $(AMDGPU.get_name(selected)) ($(repr(selected)))")
end
partialsum = ROCArray{T}(undef, DotBlocks)
data = ROCData{T}(
ROCArray{T}(undef, arraysize),
ROCArray{T}(undef, arraysize),
ROCArray{T}(undef, arraysize),
scalar,
arraysize,
)
backenddevice = ROCDevice()
else
error("unsupported backend $(backend)")
end
if !silent
println("Kernel parameters : <<<$(data.size),$(TBSize)>>>")
end
return (data, Context(backend, backenddevice))
end
function init_arrays!(
data::StreamData{T,C},
context::Context,
init::Tuple{T,T,T},
) where {T,C}
if context.backend == cpu
Threads.@threads for i = 1:data.size
@inbounds data.a[i] = init[1]
@inbounds data.b[i] = init[2]
@inbounds data.c[i] = init[3]
end
elseif context.backend == cuda
CUDA.fill!(data.a, init[1])
CUDA.fill!(data.b, init[2])
CUDA.fill!(data.c, init[3])
elseif context.backend == rocm
AMDGPU.fill!(data.a, init[1])
AMDGPU.fill!(data.b, init[2])
AMDGPU.fill!(data.c, init[3])
else
error("unsupported backend $(backend)")
end
end
function copy!(data::StreamData{T,C}, context::Context) where {T,C}
@kernel function kernel(@Const(a::AbstractArray{T}), c)
i = @index(Global)
@inbounds c[i] = a[i]
end
wait(kernel(context.device, TBSize)(data.a, data.c, ndrange = data.size))
end
function mul!(data::StreamData{T,C}, context::Context) where {T,C}
@kernel function kernel(b::AbstractArray{T}, @Const(c::AbstractArray{T}), scalar::T)
i = @index(Global)
@inbounds b[i] = scalar * c[i]
end
wait(kernel(context.device, TBSize)(data.b, data.c, data.scalar, ndrange = data.size))
end
function add!(data::StreamData{T,C}, context::Context) where {T,C}
@kernel function kernel(@Const(a::AbstractArray{T}), @Const(b::AbstractArray{T}), c)
i = @index(Global)
@inbounds c[i] = a[i] + b[i]
end
wait(kernel(context.device, TBSize)(data.a, data.b, data.c, ndrange = data.size))
end
function triad!(data::StreamData{T,C}, context::Context) where {T,C}
@kernel function kernel(a::AbstractArray{T}, @Const(b::AbstractArray{T}), @Const(c), scalar::T)
i = @index(Global)
@inbounds a[i] = b[i] + (scalar * c[i])
end
wait(
kernel(context.device, TBSize)(
data.a,
data.b,
data.c,
data.scalar,
ndrange = data.size,
),
)
end
function nstream!(data::StreamData{T,C}, context::Context) where {T,C}
@kernel function kernel(a::AbstractArray{T}, @Const(b::AbstractArray{T}), @Const(c), scalar::T)
i = @index(Global)
@inbounds a[i] += b[i] + scalar * c[i]
end
wait(
kernel(context.device, TBSize)(
data.a,
data.b,
data.c,
data.scalar,
ndrange = data.size,
),
)
end
function dot(data::StreamData{T,C}, context::Context) where {T,C}
@kernel function kernel(@Const(a::AbstractArray{T}), @Const(b::AbstractArray{T}), size::Int, partial::AbstractArray{T})
local_i = @index(Local)
group_i = @index(Group)
tb_sum = @localmem T TBSize
@inbounds tb_sum[local_i] = 0.0
# do dot first
i = @index(Global)
while i <= size
@inbounds tb_sum[local_i] += a[i] * b[i]
i += TBSize * DotBlocks
end
# then tree reduction
# FIXME this does not compile when targeting CPUs:
# see https://github.com/JuliaGPU/KernelAbstractions.jl/issues/262
offset = @private Int64 (1,)
@inbounds begin
offset[1] = @groupsize()[1] ÷ 2
while offset[1] > 0
@synchronize
if (local_i - 1) < offset[1]
tb_sum[local_i] += tb_sum[local_i+offset[1]]
end
offset[1] ÷= 2
end
end
if (local_i == 1)
@inbounds partial[group_i] = tb_sum[local_i]
end
end
if context.backend == cpu
partial_sum = Vector{T}(undef, DotBlocks)
elseif context.backend == cuda
partial_sum = CuArray{T}(undef, DotBlocks)
elseif context.backend == rocm
partial_sum = ROCArray{T}(undef, DotBlocks)
else
error("unsupported backend $(backend)")
end
wait(
kernel(context.device, TBSize)(
data.a,
data.b,
data.size,
partial_sum,
ndrange = TBSize * DotBlocks,
),
)
return sum(partial_sum)
end
function read_data(data::StreamData{T,C}, _::Context)::VectorData{T} where {T,C}
return VectorData{T}(data.a, data.b, data.c, data.scalar, data.size)
end
main()

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include("Stream.jl")
function devices()::Vector{DeviceWithRepr}
return [(undef, "CPU", "Palin")]
end
function make_stream(
arraysize::Int,
scalar::T,
_::DeviceWithRepr,
silent::Bool,
)::Tuple{VectorData{T},Nothing} where {T}
return (
VectorData{T}(
Vector{T}(undef, arraysize),
Vector{T}(undef, arraysize),
Vector{T}(undef, arraysize),
scalar,
arraysize,
),
nothing
)
end
function init_arrays!(data::VectorData{T}, _, init::Tuple{T,T,T}) where {T}
for i = 1:data.size
@inbounds data.a[i] = init[1]
@inbounds data.b[i] = init[2]
@inbounds data.c[i] = init[3]
end
end
function copy!(data::VectorData{T}, _) where {T}
for i = 1:data.size
@inbounds data.c[i] = data.a[i]
end
end
function mul!(data::VectorData{T}, _) where {T}
for i = 1:data.size
@inbounds data.b[i] = data.scalar * data.c[i]
end
end
function add!(data::VectorData{T}, _) where {T}
for i = 1:data.size
@inbounds data.c[i] = data.a[i] + data.b[i]
end
end
function triad!(data::VectorData{T}, _) where {T}
for i = 1:data.size
@inbounds data.a[i] = data.b[i] + (data.scalar * data.c[i])
end
end
function nstream!(data::VectorData{T}, _) where {T}
for i = 1:data.size
@inbounds data.a[i] += data.b[i] + data.scalar * data.c[i]
end
end
function dot(data::VectorData{T}, _) where {T}
sum = zero(T)
for i = 1:data.size
@inbounds sum += data.a[i] * data.b[i]
end
return sum
end
function read_data(data::VectorData{T}, _)::VectorData{T} where {T}
return data
end
main()

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using ArgParse
using Parameters
using Printf
using Base: Float64, Int
include("StreamData.jl")
const VectorData = StreamData{T,Vector{T}} where {T}
const DeviceWithRepr = Tuple{Any,String,Any}
struct Timings
copy::Vector{Float64}
mul::Vector{Float64}
add::Vector{Float64}
triad::Vector{Float64}
dot::Vector{Float64}
Timings(n) = new(zeros(n), zeros(n), zeros(n), zeros(n), zeros(n))
end
@enum Benchmark All Triad Nstream
function run_all!(data::StreamData{T,C}, context, times::Int)::Tuple{Timings,T} where {T,C}
timings = Timings(times)
lastSum::T = 0
for i = 1:times
@inbounds timings.copy[i] = @elapsed copy!(data, context)
@inbounds timings.mul[i] = @elapsed mul!(data, context)
@inbounds timings.add[i] = @elapsed add!(data, context)
@inbounds timings.triad[i] = @elapsed triad!(data, context)
@inbounds timings.dot[i] = @elapsed lastSum = dot(data, context)
end
return (timings, lastSum)
end
function run_triad!(data::StreamData{T,C}, context, times::Int)::Float64 where {T,C}
return @elapsed for _ = 1:times
triad!(data, context)
end
end
function run_nstream!(
data::StreamData{T,C},
context,
times::Int,
)::Vector{Float64} where {T,C}
timings::Vector{Float64} = zeros(times)
for i = 1:times
@inbounds timings[i] = @elapsed nstream!(data, context)
end
return timings
end
function check_solutions(
data::StreamData{T,C},
times::Int,
init::Tuple{T,T,T},
benchmark::Benchmark,
dot::Union{T,Nothing},
) where {T,C}
(gold_a, gold_b, gold_c) = init
for _ = 1:times
if benchmark == All
gold_c = gold_a
gold_b = data.scalar * gold_c
gold_c = gold_a + gold_b
gold_a = gold_b + data.scalar * gold_c
elseif benchmark == Triad
gold_a = gold_b + data.scalar * gold_c
elseif benchmark == Nstream
gold_a += gold_b + data.scalar * gold_c
else
error("Unknown benchmark", benchmark)
end
end
tolerance = eps(T) * 100
function validate_xs(name::String, xs::AbstractArray{T}, from::T)
error = (map(x -> abs(x - from), xs) |> sum) / length(xs)
failed = error > tolerance
if failed
println("Validation failed on $name. Average error $error")
end
!failed
end
a_valid = validate_xs("a", data.a, gold_a)
b_valid = validate_xs("b", data.b, gold_b)
c_valid = validate_xs("c", data.c, gold_c)
dot_valid =
dot !== nothing ?
begin
gold_sum = gold_a * gold_b * data.size
error = abs((dot - gold_sum) / gold_sum)
failed = error > 1.0e-8
if failed
println(
"Validation failed on sum. Error $error \nSum was $dot but should be $gold_sum",
)
end
!failed
end : true
a_valid && b_valid && c_valid && dot_valid
end
@with_kw mutable struct Config
list::Bool = false
device::Int = 1
numtimes::Int = 100
arraysize::Int = 33554432
float::Bool = false
triad_only::Bool = false
nstream_only::Bool = false
csv::Bool = false
mibibytes::Bool = false
end
function parse_options(given::Config)
s = ArgParseSettings()
@add_arg_table s begin
"--list"
help = "List available devices"
action = :store_true
"--device", "-d"
help = "Select device at DEVICE, NOTE: Julia is 1-indexed"
arg_type = Int
default = given.device
"--numtimes", "-n"
help = "Run the test NUMTIMES times (NUM >= 2)"
arg_type = Int
default = given.numtimes
"--arraysize", "-s"
help = "Use ARRAYSIZE elements in the array"
arg_type = Int
default = given.arraysize
"--float"
help = "Use floats (rather than doubles)"
action = :store_true
"--triad_only"
help = "Only run triad"
action = :store_true
"--nstream_only"
help = "Only run nstream"
action = :store_true
"--csv"
help = "Output as csv table"
action = :store_true
"--mibibytes"
help = "Use MiB=2^20 for bandwidth calculation (default MB=10^6)"
action = :store_true
end
args = parse_args(s)
# surely there's a better way than doing this:
for (arg, val) in args
setproperty!(given, Symbol(arg), val)
end
end
const DefaultInit = (0.1, 0.2, 0.0)
const DefaultScalar = 0.4
const Version = "3.4.0"
function main()
config::Config = Config()
parse_options(config)
if config.list
for (i, (_,repr, impl)) in enumerate(devices())
println("[$i] ($impl) $repr")
end
exit(0)
end
ds = devices()
# TODO implement substring device match
if config.device < 1 || config.device > length(ds)
error(
"Device $(config.device) out of range (1..$(length(ds))), NOTE: Julia is 1-indexed",
)
else
device = ds[config.device]
end
type = config.float ? Float32 : Float64
if config.nstream_only && !config.triad_only
benchmark = Nstream
elseif !config.nstream_only && config.triad_only
benchmark = Triad
elseif !config.nstream_only && !config.triad_only
benchmark = All
elseif config.nstream_only && config.triad_only
error("Both triad and nstream are enabled, pick one or omit both to run all benchmarks")
else
error("Invalid config: $(repr(config))")
end
array_bytes = config.arraysize * sizeof(type)
total_bytes = array_bytes * 3
(mega_scale, mega_suffix, giga_scale, giga_suffix) =
!config.mibibytes ? (1.0e-6, "MB", 1.0e-9, "GB") : (2^-20, "MiB", 2^-30, "GiB")
if !config.csv
println("""BabelStream
Version: $Version
Implementation: Julia; $(PROGRAM_FILE)""")
println("Running kernels $(config.numtimes) times")
if benchmark == Triad
println("Number of elements: $(config.arraysize)")
end
println("Precision: $(config.float ? "float" : "double")")
r1 = n -> round(n; digits = 1)
println(
"Array size: $(r1(mega_scale * array_bytes)) $mega_suffix(=$(r1(giga_scale * array_bytes)) $giga_suffix)",
)
println(
"Total size: $(r1(mega_scale * total_bytes)) $mega_suffix(=$(r1(giga_scale * total_bytes)) $giga_suffix)",
)
end
function mk_row(xs::Vector{Float64}, name::String, total_bytes::Int)
tail = Base.rest(xs)
min = Iterators.minimum(tail)
max = Iterators.maximum(tail)
avg = Iterators.sum(tail) / Iterators.length(tail)
mbps = mega_scale * total_bytes / min
if config.csv
return [
("function", name),
("num_times", config.numtimes),
("n_elements", config.arraysize),
("sizeof", total_bytes),
("max_m$( config.mibibytes ? "i" : "")bytes_per_sec", mbps),
("min_runtime", min),
("max_runtime", max),
("avg_runtime", avg),
]
else
return [
("Function", name),
("M$(config.mibibytes ? "i" : "")Bytes/sec", round(mbps; digits = 3)),
("Min (sec)", round(min; digits = 5)),
("Max", round(max; digits = 5)),
("Average", round(avg; digits = 5)),
]
end
end
function tabulate(rows::Vector{Tuple{String,Any}}...)
header = Base.first(rows)
padding = config.csv ? 0 : 12
sep = config.csv ? "," : ""
map(x -> rpad(x[1], padding), header) |> x -> join(x, sep) |> println
for row in rows
map(x -> rpad(x[2], padding), row) |> x -> join(x, sep) |> println
end
end
init::Tuple{type,type,type} = DefaultInit
scalar::type = DefaultScalar
GC.enable(false)
(data, context) = make_stream(config.arraysize, scalar, device, config.csv)
init_arrays!(data, context, init)
if benchmark == All
(timings, sum) = run_all!(data, context, config.numtimes)
valid = check_solutions(read_data(data, context), config.numtimes, init, benchmark, sum)
tabulate(
mk_row(timings.copy, "Copy", 2 * array_bytes),
mk_row(timings.mul, "Mul", 2 * array_bytes),
mk_row(timings.add, "Add", 3 * array_bytes),
mk_row(timings.triad, "Triad", 3 * array_bytes),
mk_row(timings.dot, "Dot", 2 * array_bytes),
)
elseif benchmark == Nstream
timings = run_nstream!(data, context, config.numtimes)
valid =
check_solutions(read_data(data, context), config.numtimes, init, benchmark, nothing)
tabulate(mk_row(timings, "Nstream", 4 * array_bytes))
elseif benchmark == Triad
elapsed = run_triad!(data, context, config.numtimes)
valid =
check_solutions(read_data(data, context), config.numtimes, init, benchmark, nothing)
total_bytes = 3 * array_bytes * config.numtimes
bandwidth = mega_scale * (total_bytes / elapsed)
println("Runtime (seconds): $(round(elapsed; digits=5))")
println("Bandwidth ($giga_suffix/s): $(round(bandwidth; digits=3)) ")
else
error("Bad benchmark $(benchmark)")
end
GC.enable(true)
if !valid
exit(1)
end
end

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struct StreamData{T,C<:AbstractArray{T}}
a::C
b::C
c::C
scalar::T
size::Int
end

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include("Stream.jl")
function devices()::Vector{DeviceWithRepr}
return [(undef, "$(Sys.cpu_info()[1].model) ($(Threads.nthreads())T)", "Threaded")]
end
function make_stream(
arraysize::Int,
scalar::T,
_::DeviceWithRepr,
silent::Bool,
)::Tuple{VectorData{T},Nothing} where {T}
if !silent
println("Using max $(Threads.nthreads()) threads")
end
return (
VectorData{T}(
Vector{T}(undef, arraysize),
Vector{T}(undef, arraysize),
Vector{T}(undef, arraysize),
scalar,
arraysize,
),
nothing
)
end
function init_arrays!(data::VectorData{T}, _, init::Tuple{T,T,T}) where {T}
Threads.@threads for i = 1:data.size
@inbounds data.a[i] = init[1]
@inbounds data.b[i] = init[2]
@inbounds data.c[i] = init[3]
end
end
function copy!(data::VectorData{T}, _) where {T}
Threads.@threads for i = 1:data.size
@inbounds data.c[i] = data.a[i]
end
end
function mul!(data::VectorData{T}, _) where {T}
Threads.@threads for i = 1:data.size
@inbounds data.b[i] = data.scalar * data.c[i]
end
end
function add!(data::VectorData{T}, _) where {T}
Threads.@threads for i = 1:data.size
@inbounds data.c[i] = data.a[i] + data.b[i]
end
end
function triad!(data::VectorData{T}, _) where {T}
Threads.@threads for i = 1:data.size
@inbounds data.a[i] = data.b[i] + (data.scalar * data.c[i])
end
end
function nstream!(data::VectorData{T}, _) where {T}
Threads.@threads for i = 1:data.size
@inbounds data.a[i] += data.b[i] + data.scalar * data.c[i]
end
end
# Threads.@threads/Threads.@spawn doesn't support OpenMP's firstprivate, etc
function static_par_ranged(f::Function, range::Int, n::Int)
stride = range ÷ n
rem = range % n
strides = map(0:n) do i
width = stride + (i < rem ? 1 : 0)
offset = i < rem ? (stride + 1) * i : ((stride + 1) * rem) + (stride * (i - rem))
(offset, width)
end
ccall(:jl_enter_threaded_region, Cvoid, ())
try
foreach(wait, map(1:n) do group
(offset, size) = strides[group]
task = Task(() -> f(group, offset+1, offset+size))
task.sticky = true
ccall(:jl_set_task_tid, Cvoid, (Any, Cint), task, group-1) # ccall, so 0-based for group
schedule(task)
end)
finally
ccall(:jl_exit_threaded_region, Cvoid, ())
end
end
function dot(data::VectorData{T}, _) where {T}
partial = Vector{T}(undef, Threads.nthreads())
static_par_ranged(data.size, Threads.nthreads()) do group, startidx, endidx
acc = zero(T)
@simd for i = startidx:endidx
@inbounds acc += data.a[i] * data.b[i]
end
@inbounds partial[group] = acc
end
return sum(partial)
# This doesn't do well on aarch64 because of the excessive Threads.threadid() ccall
# and inhibited vectorisation from the lack of @simd
# partial = zeros(T, Threads.nthreads())
# Threads.@threads for i = 1:data.size
# @inbounds partial[Threads.threadid()] += (data.a[i] * data.b[i])
# end
# return sum(partial)
end
function read_data(data::VectorData{T}, _)::VectorData{T} where {T}
return data
end
main()

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using Base.Iterators: println
using Base.Iterators: println
using Printf: Iterators
include("Stream.jl")
using oneAPI
const oneData = StreamData{T,oneArray{T}} where {T}
const DotWGSize = 256::Int
function devices()::Vector{DeviceWithRepr}
all = map(oneL0.devices, oneL0.drivers()) |> Iterators.flatten |> Iterators.collect
map(dev -> (dev, repr("text/plain", dev), "oneAPi.jl"), all)
end
function make_stream(
arraysize::Int,
scalar::T,
device::DeviceWithRepr,
silent::Bool,
)::Tuple{oneData{T},Int} where {T}
oneAPI.allowscalar(false)
oneAPI.device!(device[1])
props = oneL0.compute_properties(oneAPI.device())
groupsize = min(props.maxTotalGroupSize, arraysize)
if arraysize % groupsize != 0
error("arraysize ($(arraysize)) must be divisible by $(groupsize)!")
end
if !silent
println("Using L0 device: $(repr("text/plain",device[1]))")
println("Kernel parameters : <<<$(arraysize),$(groupsize)>>>")
end
return (
oneData{T}(
oneArray{T}(undef, arraysize),
oneArray{T}(undef, arraysize),
oneArray{T}(undef, arraysize),
scalar,
arraysize,
),
groupsize,
)
end
function init_arrays!(data::oneData{T}, _, init::Tuple{T,T,T}) where {T}
oneAPI.fill!(data.a, init[1])
oneAPI.fill!(data.b, init[2])
oneAPI.fill!(data.c, init[3])
end
function copy!(data::oneData{T}, groupsize::Int) where {T}
function kernel(a::AbstractArray{T}, c::AbstractArray{T})
i = get_global_id()
@inbounds c[i] = a[i]
return
end
@oneapi items = groupsize groups = data.size ÷ groupsize kernel( #
data.a,
data.c,
)
oneAPI.synchronize()
end
function mul!(data::oneData{T}, groupsize::Int) where {T}
function kernel(b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = get_global_id()
@inbounds b[i] = scalar * c[i]
return
end
@oneapi items = groupsize groups = data.size ÷ groupsize kernel( #
data.b,
data.c,
data.scalar,
)
oneAPI.synchronize()
end
function add!(data::oneData{T}, groupsize::Int) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T})
i = get_global_id()
@inbounds c[i] = a[i] + b[i]
return
end
@oneapi items = groupsize groups = data.size ÷ groupsize kernel( #
data.a,
data.b,
data.c,
)
oneAPI.synchronize()
end
function triad!(data::oneData{T}, groupsize::Int) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = get_global_id()
@inbounds a[i] = b[i] + (scalar * c[i])
return
end
@oneapi items = groupsize groups = data.size ÷ groupsize kernel( #
data.a,
data.b,
data.c,
data.scalar,
)
oneAPI.synchronize()
end
function nstream!(data::oneData{T}, groupsize::Int) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, c::AbstractArray{T}, scalar::T)
i = get_global_id()
@inbounds a[i] += b[i] + scalar * c[i]
return
end
@oneapi items = groupsize groups = data.size ÷ groupsize kernel( #
data.a,
data.b,
data.c,
data.scalar,
)
oneAPI.synchronize()
end
function dot(data::oneData{T}, groupsize::Int) where {T}
function kernel(a::AbstractArray{T}, b::AbstractArray{T}, size::Int, partial::AbstractArray{T})
wg_sum = @LocalMemory(T, (DotWGSize,))
li = get_local_id()
@inbounds wg_sum[li] = 0.0
# do dot first
i = get_global_id()
while i <= size
@inbounds wg_sum[li] += a[i] * b[i]
i += get_global_size()
end
# then tree reduction
offset = get_local_size() ÷ 2
while offset > 0
barrier()
if li <= offset
@inbounds wg_sum[li] += wg_sum[li+offset]
end
offset ÷= 2
end
if li == 1
@inbounds partial[get_group_id()] = wg_sum[li]
end
return
end
partial_sum = oneArray{T}(undef, groupsize)
@oneapi items = groupsize groups = DotWGSize kernel(
data.a,
data.b,
data.size,
partial_sum,
)
oneAPI.synchronize()
return sum(partial_sum)
end
function read_data(data::oneData{T}, _)::VectorData{T} where {T}
return VectorData{T}(data.a, data.b, data.c, data.scalar, data.size)
end
main()

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#!/bin/bash
# shellcheck disable=SC2034 disable=SC2153
for BACKEND in "." "AMDGPU" "CUDA" "oneAPI" "Threaded" "KernelAbstractions"
do
julia --project="$BACKEND" -e 'import Pkg; Pkg.resolve(); Pkg.instantiate(); Pkg.update(); Pkg.gc();'
done