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Update to CUDA 11.8 (#1620)
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‎.buildkite/pipeline.yml

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@@ -101,6 +101,7 @@ steps:
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if: build.message !~ /\[skip tests\]/ && !build.pull_request.draft
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timeout_in_minutes: 120
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matrix:
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- "11.8"
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- "11.7"
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- "11.6"
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- "11.5"

‎Artifacts.toml

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@@ -332,7 +332,49 @@ os = "windows"
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[[CUDA.download]]
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sha256 = "e5d11545d9d93d33625c05a2139b982d55cebf90ac01ab2580d80ec4cfb85a91"
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url = "https://github.com/JuliaBinaryWrappers/CUDA_loader_jll.jl/releases/download/CUDA_loader-v0.2.2+0/CUDA_loader.v0.2.2.x86_64-w64-mingw32-cuda+11.7.tar.gz"
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[[CUDA]]
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arch = "aarch64"
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cuda = "11.8"
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git-tree-sha1 = "44b0a045d405d46dc9f5ad7af5f7bdad3d137875"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUDA.download]]
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sha256 = "cec0f6c321dd2cb00ff82ddbb65fc8e6d21d15f277872fe67356dc299a5680e6"
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url = "https://github.com/JuliaBinaryWrappers/CUDA_loader_jll.jl/releases/download/CUDA_loader-v0.2.2+0/CUDA_loader.v0.2.2.aarch64-linux-gnu-cuda+11.8.tar.gz"
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[[CUDA]]
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arch = "powerpc64le"
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cuda = "11.8"
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git-tree-sha1 = "c44d0f3c874bb8f4650e1621537c79dd0fa1edb5"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUDA.download]]
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sha256 = "85affa0c4b2ce039f45601428c3c4d432a5c5e8487ceea93eaf713bc24d1bb72"
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url = "https://github.com/JuliaBinaryWrappers/CUDA_loader_jll.jl/releases/download/CUDA_loader-v0.2.2+0/CUDA_loader.v0.2.2.powerpc64le-linux-gnu-cuda+11.8.tar.gz"
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[[CUDA]]
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arch = "x86_64"
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cuda = "11.8"
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git-tree-sha1 = "b16cd92510f6afd31e7fc2a7614fb883419ae396"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUDA.download]]
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sha256 = "1ded0cd43e8029ee2c253e506e2df21da8c1a0a4f341b25de1fe933823b36759"
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url = "https://github.com/JuliaBinaryWrappers/CUDA_loader_jll.jl/releases/download/CUDA_loader-v0.2.2+0/CUDA_loader.v0.2.2.x86_64-linux-gnu-cuda+11.8.tar.gz"
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[[CUDA]]
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arch = "x86_64"
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cuda = "11.8"
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git-tree-sha1 = "5c0bc670b541bf96e910b493e136787d85137d33"
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lazy = true
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os = "windows"
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[[CUDA.download]]
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sha256 = "15e8cf8bf27daeb0eeba5b4781b79e94e15a55c15a6c6a9ff629be6bbbb3781e"
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url = "https://github.com/JuliaBinaryWrappers/CUDA_loader_jll.jl/releases/download/CUDA_loader-v0.2.2+0/CUDA_loader.v0.2.2.x86_64-w64-mingw32-cuda+11.8.tar.gz"
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# CUDA forwards compatibility package
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[[CUDNN.download]]
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sha256 = "8b6c04bfb07a55a0f3cae180897ff4de232dca642d9651a1ce79317c1715a3eb"
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url = "https://github.com/JuliaBinaryWrappers/CUDNN_jll.jl/releases/download/CUDNN-v8.3.2+0/CUDNN.v8.3.2.aarch64-linux-gnu-cuda+11.6.tar.gz"
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[[CUDNN]]
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arch = "x86_64"
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cuda = "11.8"
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git-tree-sha1 = "b0757335df76c8a6732f8261b705210afd7d2583"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUDNN.download]]
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sha256 = "fb809d5ab36633e80e218c49a7b3a1b95c6b3b6098a0ec8ea76cd353269c36e9"
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url = "https://github.com/JuliaBinaryWrappers/CUDNN_jll.jl/releases/download/CUDNN-v8.3.2+0/CUDNN.v8.3.2.x86_64-linux-gnu-cuda+11.6.tar.gz"
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[[CUDNN]]
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arch = "powerpc64le"
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cuda = "11.8"
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git-tree-sha1 = "6c10051e0b34a71f72eccd271c73bad6e655b8e2"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUDNN.download]]
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sha256 = "0415214f5ab9280bd15e75f892de959d007ab066be06c73b807b0b2158b92aeb"
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url = "https://github.com/JuliaBinaryWrappers/CUDNN_jll.jl/releases/download/CUDNN-v8.3.2+0/CUDNN.v8.3.2.powerpc64le-linux-gnu-cuda+11.6.tar.gz"
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[[CUDNN]]
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arch = "x86_64"
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cuda = "11.8"
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git-tree-sha1 = "1a54a5d914c297394fc55b6aa7e4e68ff283303e"
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lazy = true
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os = "windows"
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[[CUDNN.download]]
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sha256 = "671f1251eacce5573f45081e9d8c805ac94e31eb35f46d6072abc43b0d45b7f0"
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url = "https://github.com/JuliaBinaryWrappers/CUDNN_jll.jl/releases/download/CUDNN-v8.3.2+0/CUDNN.v8.3.2.x86_64-w64-mingw32-cuda+11.6.tar.gz"
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[[CUDNN]]
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arch = "aarch64"
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cuda = "11.8"
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git-tree-sha1 = "01dda996448415c814dc52e114125bd8bfadb33e"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUDNN.download]]
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sha256 = "8b6c04bfb07a55a0f3cae180897ff4de232dca642d9651a1ce79317c1715a3eb"
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url = "https://github.com/JuliaBinaryWrappers/CUDNN_jll.jl/releases/download/CUDNN-v8.3.2+0/CUDNN.v8.3.2.aarch64-linux-gnu-cuda+11.6.tar.gz"
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# CUTENSOR
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[[CUTENSOR.download]]
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sha256 = "19a7aaa8eeaddfb0b31d0f60bcb72ee5cc15056f7ce54ee9d843ae6fe3c66157"
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url = "https://github.com/JuliaBinaryWrappers/CUTENSOR_jll.jl/releases/download/CUTENSOR-v1.4.0+0/CUTENSOR.v1.4.0.aarch64-linux-gnu-cuda+11.6.tar.gz"
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[[CUTENSOR]]
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arch = "x86_64"
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cuda = "11.8"
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git-tree-sha1 = "b4714d43eda0a77581c8664d279f7456a0adfb47"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUTENSOR.download]]
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sha256 = "605602626651915883f214200d88e4f530fe2cee611d8023fb51f259285f5bef"
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url = "https://github.com/JuliaBinaryWrappers/CUTENSOR_jll.jl/releases/download/CUTENSOR-v1.4.0+0/CUTENSOR.v1.4.0.x86_64-linux-gnu-cuda+11.6.tar.gz"
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[[CUTENSOR]]
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arch = "powerpc64le"
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cuda = "11.8"
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git-tree-sha1 = "e18afddb05570941824a8389c5a0c702a17ad129"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUTENSOR.download]]
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sha256 = "bad14cbcb6db9f7242f118e39a20f47d92d37b6e1ce98f2f3d338aa12797ddf1"
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url = "https://github.com/JuliaBinaryWrappers/CUTENSOR_jll.jl/releases/download/CUTENSOR-v1.4.0+0/CUTENSOR.v1.4.0.powerpc64le-linux-gnu-cuda+11.6.tar.gz"
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[[CUTENSOR]]
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arch = "x86_64"
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cuda = "11.8"
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git-tree-sha1 = "b82bb42c0e83d6eff5dca5be2acac39a1f088b91"
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lazy = true
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os = "windows"
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[[CUTENSOR.download]]
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sha256 = "4cfe59a3ac846d043656187b6ee63e2b71d8747e1ea2b6b61c6d36c66516a922"
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url = "https://github.com/JuliaBinaryWrappers/CUTENSOR_jll.jl/releases/download/CUTENSOR-v1.4.0+0/CUTENSOR.v1.4.0.x86_64-w64-mingw32-cuda+11.6.tar.gz"
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[[CUTENSOR]]
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arch = "aarch64"
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cuda = "11.8"
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git-tree-sha1 = "3b6d5b634f1c753b20c4d950260a0bc1828497c1"
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lazy = true
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libc = "glibc"
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os = "linux"
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[[CUTENSOR.download]]
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sha256 = "19a7aaa8eeaddfb0b31d0f60bcb72ee5cc15056f7ce54ee9d843ae6fe3c66157"
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url = "https://github.com/JuliaBinaryWrappers/CUTENSOR_jll.jl/releases/download/CUTENSOR-v1.4.0+0/CUTENSOR.v1.4.0.aarch64-linux-gnu-cuda+11.6.tar.gz"
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# NCCL

‎deps/bindeps.jl

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# NOTE: we don't use autogenerated JLLs, because we have multiple artifacts and need to
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# decide at run time (i.e. not via package dependencies) which one to use.
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const cuda_toolkits = [
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(release=v"11.8", preferred=true),
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(release=v"11.7", preferred=true),
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(release=v"11.6", preferred=true),
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(release=v"11.5", preferred=true),

‎src/utilities.jl

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end
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println(io, "- CUPTI: ", has_cupti() ? CUPTI.version() : "missing")
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println(io, "- NVML: ", has_nvml() ? NVML.version() : "missing")
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println(io, "- CUDNN: ", has_cudnn() ? "$(CUDNN.version()) (for CUDA $(CUDNN.cuda_version()))" : "missing")
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println(io, "- CUTENSOR: ", has_cutensor() ? "$(CUTENSOR.version()) (for CUDA $(CUTENSOR.cuda_version()))" : "missing")
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println(io, "- CUDNN: ", has_cudnn() ? "$(CUDNN.version())" : "missing")
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println(io, "- CUTENSOR: ", has_cutensor() ? "$(CUTENSOR.version())" : "missing")
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println(io)
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println(io, "Toolchain:")

‎test/cusparse/generic.jl

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using Adapt
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using CUDA.CUSPARSE
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using SparseArrays
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using LinearAlgebra
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if CUSPARSE.version() >= v"11.4.1"
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@testset "generic mv!" for T in [Float32, Float64]

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