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-deps = ["LiveServer"] -git-tree-sha1 = "20ec221753e0c6bcac845423089b656538ac4eec" -uuid = "3a985190-f512-4703-8d38-2a7944ed5916" -version = "0.8.22" - -[[deps.HTTP]] -deps = ["Base64", "Dates", "IniFile", "Logging", "MbedTLS", "NetworkOptions", "Sockets", "URIs"] -git-tree-sha1 = "14eece7a3308b4d8be910e265c724a6ba51a9798" -uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "0.9.16" - -[[deps.IOCapture]] -deps = ["Logging", "Random"] -git-tree-sha1 = "f7be53659ab06ddc986428d3a9dcc95f6fa6705a" -uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" -version = "0.2.2" - -[[deps.IniFile]] -deps = ["Test"] -git-tree-sha1 = "098e4d2c533924c921f9f9847274f2ad89e018b8" -uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f" -version = "0.5.0" - -[[deps.InteractiveUtils]] -deps = ["Markdown"] -uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" - -[[deps.JSON]] -deps = ["Dates", "Mmap", "Parsers", "Unicode"] -git-tree-sha1 = "8076680b162ada2a031f707ac7b4953e30667a37" -uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -version = "0.21.2" - -[[deps.LibCURL]] -deps = ["LibCURL_jll", "MozillaCACerts_jll"] -uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" -version = "0.6.3" - -[[deps.LibCURL_jll]] -deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] -uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "7.84.0+0" - -[[deps.LibGit2]] -deps = ["Base64", "NetworkOptions", "Printf", "SHA"] -uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" - -[[deps.LibSSH2_jll]] -deps = ["Artifacts", "Libdl", "MbedTLS_jll"] -uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8" -version = "1.10.2+0" - -[[deps.Libdl]] -uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" - -[[deps.Literate]] -deps = ["Base64", "IOCapture", "JSON", "REPL"] -git-tree-sha1 = "bbebc3c14dbfbe76bfcbabf0937481ac84dc86ef" -uuid = "98b081ad-f1c9-55d3-8b20-4c87d4299306" -version = "2.9.3" - -[[deps.LiveServer]] -deps = ["Crayons", "FileWatching", "HTTP", "Pkg", "Sockets", "Test"] -git-tree-sha1 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-[[deps.Serialization]] -uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" - -[[deps.Sockets]] -uuid = "6462fe0b-24de-5631-8697-dd941f90decc" - -[[deps.TOML]] -deps = ["Dates"] -uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" -version = "1.0.3" - -[[deps.Tar]] -deps = ["ArgTools", "SHA"] -uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" -version = "1.10.0" - -[[deps.Test]] -deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] -uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" - -[[deps.URIs]] -git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355" -uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" -version = "1.3.0" - -[[deps.UUIDs]] -deps = ["Random", "SHA"] -uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" - -[[deps.Unicode]] -uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" - -[[deps.Zlib_jll]] -deps = ["Libdl"] -uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.13+0" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version 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#dee2e6!important}.table-dark{color:inherit}.table-dark tbody+tbody,.table-dark td,.table-dark th,.table-dark thead th{border-color:#dee2e6}.table .thead-dark th{color:inherit;border-color:#dee2e6}} -/*# sourceMappingURL=bootstrap.min.css.map */ \ No newline at end of file diff --git a/previews/PR41/cuda/index.html b/previews/PR41/cuda/index.html deleted file mode 100644 index dda3e3b..0000000 --- a/previews/PR41/cuda/index.html +++ /dev/null @@ -1,211 +0,0 @@ - - - - - - - - - - - - - - - - - - - NVIDIA CUDA ⋅ JuliaGPU - - - -

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NVIDIA CUDA

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The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit, and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014 and can easily be installed on any current version of Julia using the integrated package manager.

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CUDA.jl makes it possible to program NVIDIA GPUs at different abstraction levels:

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  • by using the CuArray type, providing a user-friendly yet powerful abstraction that does not require any GPU programming experience;

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  • by writing CUDA kernels, with the same performance as kernels written in CUDA C;

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  • by interfacing with CUDA APIs and libraries directly, offering the same level of flexibility you would expect from a C-based programming environment.

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The documentation of CUDA.jl demonstrates each of these approaches.

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Performance

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Julia on the CPU is known for its good performance, approaching that of statically compiled languages like C. The same holds for programming NVIDIA GPUs with kernels written using CUDA.jl, where we have shown the performance to approach and even sometimes exceed that of CUDA C on a selection[1] of applications from the Rodinia benchmark suite:

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- Relative performance of Rodinia benchmarks implemented in Julia with CUDA.jl. -

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[1]Since porting applications from one programming language to another is labour
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- JuliaGPU

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- High-performance GPU programming in a high-level language. -

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JuliaGPU is a Github organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.

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Several GPU platforms are supported, but there are large differences in features and stability. On this website, you can find a brief introduction of the supported platforms and links to the respective home pages.

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Supported platforms

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The best supported GPU platform in Julia is NVIDIA CUDA, with mature and full-featured packages for both low-level kernel programming as well as working with high-level operations on arrays. All versions of Julia are supported, on Linux and Windows, and the functionality is actively used by a variety of applications and libraries.

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Similar, but much newer capabilities exist for Intel GPUs with oneAPI. Currently, full-featured kernel programming capabilities are available, but there is no support for vendor libraries such as oneMKL or oneDNN yet.

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Maturing support exists for AMD GPUs running on the ROCm stack. These GPUs can again be programmed in Julia at the kernel level or using high-level operations on arrays. Latest versions of Julia are supported, and the functionality is increasingly used by a variety of applications and libraries.

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Experimental support also exists for Apple GPUs. Array programming and kernel programming are both supported.

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Applications

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Almost 300 packages rely directly or indirectly on Julia's GPU capabilities. A few noteworthy examples are:

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  • Oceananigans.jl to accelerate a non-hydrostatic ocean modeling application

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  • Yao.jl framework for quantum information research

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  • KernelAbstractions.jl for working with CPUs and GPUs alike using vendor-neutral abstractions

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  • GemmKernels.jl providing flexible and performant GEMM kernels

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Many other Julia applications and libraries can be used with GPUs, too: By means of GPU-specific array types like CuArray from CUDA.jl or ROCArray from AMDGPU.jl, existing software that uses the Julia array interfaces can often be executed as-is on a GPU.

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Publications

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Much of Julia's GPU support was developed as part of academic research. If you would like to help support it, please star the relevant repositories as such metrics may help us secure funding in the future. If you use our software as part of your research, teaching, or other activities, we would be grateful if you could cite our work:

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Community

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If you need help, or have questions about GPU programming in Julia, you can find members of the community at:

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Learn

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Currently, the Julia CUDA stack is the most mature, easiest to install, and full-featured. The CUDA.jl documentation is a central place for information on all relevant packages. Start with the instructions on how to install the stack, and follow with this introductory tutorial.

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If you prefer videos, the presentations below highlight different aspects of the toolchain.

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Concurrent GPU computing in CUDA.jl 3.0

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Introduction to concurrent GPU computing:

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Effective CUDA GPU computing in Julia

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  • Design and benefits of the Julia GPU stack

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  • Composability with existing (non-GPU) software

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  • Performance killers and tools for optimization

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l=i(15),t=Math.max,r=Math.min;e.exports=function(e,n){return(e=l(e))<0?t(e+n,0):r(e,n)}},function(e,n,i){var l=i(23),t=i(21),r=i(46);e.exports=function(e){return function(n,i,o){var a,c=l(n),y=t(c.length),p=r(o,y);if(e&&i!=i){for(;y>p;)if((a=c[p++])!=a)return!0}else for(;y>p;p++)if((e||p in c)&&c[p]===i)return e||p||0;return!e&&-1}}},function(e,n,i){var l=i(22);e.exports=Object("z").propertyIsEnumerable(0)?Object:function(e){return"String"==l(e)?e.split(""):Object(e)}},function(e,n,i){var l=i(3),t=i(23),r=i(47)(!1),o=i(9)("IE_PROTO");e.exports=function(e,n){var i,a=t(e),c=0,y=[];for(i in a)i!=o&&l(a,i)&&y.push(i);for(;n.length>c;)l(a,i=n[c++])&&(~r(y,i)||y.push(i));return y}},function(e,n,i){var l=i(49),t=i(19);e.exports=Object.keys||function(e){return l(e,t)}},function(e,n,i){var l=i(6),t=i(5),r=i(50);e.exports=i(4)?Object.defineProperties:function(e,n){t(e);for(var i,o=r(n),a=o.length,c=0;a>c;)l.f(e,i=o[c++],n[i]);return e}},function(e,n,i){var l=i(5),t=i(51),r=i(19),o=i(9)("IE_PROTO"),a=function(){},c=function(){var e,n=i(26)("iframe"),l=r.length;for(n.style.display="none",i(45).appendChild(n),n.src="javascript:",(e=n.contentWindow.document).open(),e.write(" - - - - - - - - diff --git a/previews/PR41/oneapi/index.html b/previews/PR41/oneapi/index.html deleted file mode 100644 index 67e968d..0000000 --- a/previews/PR41/oneapi/index.html +++ /dev/null @@ -1,195 +0,0 @@ - - - - - - - - - - - - - - - - - - - Intel oneAPI ⋅ JuliaGPU - - - - - - -
-
- - - - -

Intel oneAPI

- -

- - - -

- -

oneAPI is an open standard for programming hardware accelerators, originally designed by Intel. The oneAPI.jl package offers a Julia interface to this programming model. The package is in early development, but already provides most features for application development.

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Similarly to other GPU support packages in Julia, oneAPI.jl makes it possible to work with accelerators at three distinct abstraction levels:

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    -

  • high-level, using the oneArray array type and Julia's powerful array abstractions;

    -
    • -

  • by writing your own kernels and launching them using the @oneapi macro;

    -
    • -

  • using the low-level Level Zero wrappers in the oneL0 submodule.

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    • -
-

For more information, refer to the following blog posts:

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- - - - - - - - - - - - - - - - - diff --git a/previews/PR41/other/index.html b/previews/PR41/other/index.html deleted file mode 100644 index 439d3b1..0000000 --- a/previews/PR41/other/index.html +++ /dev/null @@ -1,174 +0,0 @@ - - - - - - - - - - - - - - - - - - - Other ⋅ JuliaGPU - - - - - - -
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- - - - -

Other

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Several other back-ends exist, not all of them with the same level of polish or support as the NVIDIA and AMD back-ends.

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OpenCL

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Programming OpenCL GPUs in Julia is much more limited than other supported platforms. On recent versions of Julia, only OpenCL.jl is available. This package can be used to compile and execute GPU kernels written in OpenCL C.

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ArrayFire

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ArrayFire is a general-purpose software library that targets CPUs, GPUs, and other accelerator hardware. The ArrayFire.jl package provides a Julia interface to this library, and makes it possible to program accelerators using an array abstraction built on the ArrayFire library.

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SX-Aurora

-

The NEC SX-Aurora Tsubasa is a PCIe card which works as a Vector Computer. It can be programmed from Julia using the VectorEngine.jl package, which at the moment requires a custom Julia build using a LLVM fork. Support is expected to improve due NECs involement.

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-
- - - - - - - - - - - - - - - - - diff --git a/previews/PR41/post/2019-12-12-new_site/index.html b/previews/PR41/post/2019-12-12-new_site/index.html deleted file mode 100644 index 1e59789..0000000 --- a/previews/PR41/post/2019-12-12-new_site/index.html +++ /dev/null @@ -1,181 +0,0 @@ - - - - - - - - - - - - - - - - - - - New website for JuliaGPU ⋅ JuliaGPU - - - - - - -
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- - - - - -

New website for JuliaGPU

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- -Tim Besard - - -

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This post is located at /new_site/

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Welcome to the new landing page for the JuliaGPU organization. This website serves as an introduction to the several packages for programming GPUs in Julia, with pointers to relevant resources for new users.

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The sources for this website are hosted at GitHub and generated using Hugo, feel free to open an issue or pull request if you think it could be improved.

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-
- - - - - - - - - - - - - - - - - diff --git a/previews/PR41/post/2020-03-25-cudanative_3.0-cuarrays_2.0/index.html b/previews/PR41/post/2020-03-25-cudanative_3.0-cuarrays_2.0/index.html deleted file mode 100644 index 734426c..0000000 --- a/previews/PR41/post/2020-03-25-cudanative_3.0-cuarrays_2.0/index.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - - - - - - - - - - - - - CUDAnative.jl 3.0 and CuArrays.jl 2.0 ⋅ JuliaGPU - - - - - - -
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CUDAnative.jl 3.0 and CuArrays.jl 2.0

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- -Tim Besard - - -

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This post is located at /cudanative_3.0-cuarrays_2.0/

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This release of the Julia CUDA stack contains some exciting new features: automatic installation of CUDA using artifacts, full support for GPU method redefinitions, and experimental support for multitasking and multithreading. The release is technically breaking, but most end-users should not be affected.

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API changes

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Changes to certain APIs require these releases to be breaking, however, most users should not be affected and chances are you can just bump your Compat entries without any additional changes. Flux.jl users will have to wait a little longer though, as the package uses non-public APIs that have changed and requires an update.

-

Artifacts

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CUDA and its dependencies will now be automatically installed using artifacts generated by BinaryBuilder.jl. This greatly improves usability, and only requires a functioning NVIDIA driver:

-
julia> ENV["JULIA_DEBUG"] = "CUDAnative"
-
-julia> using CUDAnative
-
-julia> CUDAnative.version()
-┌ Debug: Trying to use artifacts...
-└ @ CUDAnative CUDAnative/src/bindeps.jl:52
-┌ Debug: Using CUDA 10.2.89 from an artifact at /depot/artifacts/...
-└ @ CUDAnative CUDAnative/src/bindeps.jl:108
-v"10.2.89"
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Use of a local installation is still possible by setting the environment variable JULIA_CUDA_USE_BINARYBUILDER to false. For more details, refer to the documentation.

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Relevant PRs: CUDAnative.jl#492 and CuArrays.jl#490

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Method redefinitions

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CUDAnative 3.0 now fully supports method redefinitions, commonly referred to as Julia issue #265, and makes it possible to use interactive programming tools like Revise.jl:

-
julia> child() = 0
-julia> parent() = (@cuprintln(child()); return)
-julia> @cuda parent()
-0
-
-julia> parent() = (@cuprintln(child() + 1); return)
-julia> @cuda parent()
-1
-
-
-julia> child() = 1
-julia> @cuda parent()
-2
-

Relevant PRs: CUDAnative.jl#581

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Experimental: Multitasking and multithreading

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With CUDAnative 3.0 and CuArrays 2.0 you can now use Julia tasks and threads to organize your code. In combination with CUDA streams, this makes it possible to execute kernels and other GPU operations in parallel:

-
@sync begin
-    function my_expensive_kernel()
-        return
-    end
-    @async @cuda stream=CuStream() my_expensive_kernel()
-    @async @cuda stream=CuStream() my_expensive_kernel()
-end
-

Every task, whether it runs on a separate thread or not, can work with a different device, as well as independently work with CUDA libraries like CUBLAS and CUFFT.

-

Note that this support is experimental, and lacks certain features to be fully effective. For one, the CuArrays memory allocator is not device-aware, and it is currently not possible to configure the CUDA stream for operations like map or broadcast.

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Relevant PRs: CUDAnative.jl#609 and CuArrays.jl#645

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Minor changes

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GPU kernels are now name-mangled like C++, which offers better integration with NVIDIA tools (CUDAnative.jl#559).

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A better N-dimensional mapreducedim! kernel, properly integrating with all Base interfaces (CuArrays.jl#602 and GPUArrays#246).

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A CuIterator type for batching arrays to the GPU (by @jrevels, CuArrays.jl#467).

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Integration with Base's 5-arg mul! (by @haampie, CuArrays.jl#641 and GPUArrays#253).

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Integration with Cthulhu.jl for interactive inspection of generated code (CUDAnative.jl#597).

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Known issues

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With a release as big as this one there's bound to be some bugs, e.g., with the installation of artifacts on exotic systems, or due to the many changes to make the libraries thread-safe. If you need absolute stability, please wait for a point release.

-

There are also some known issues. CUDAnative is currently not compatible with Julia 1.5 due to Base compiler changes (julia#34993), the new mapreducedim! kernel appears to be slower in some cases (CuArrays.jl#611), and there are some remaining thread-safety issues when using the non-default memory pool (CuArrays.jl#647).

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