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For kernel programming, https://github.com/JuliaGPU/KernelAbstractions.jl (shortened to KA) is what the JuliaGPU team has been developing as a unified programming interface for GPUs of any flavor. It's not significantly different from the (basically identical) interfaces exposed by CUDA.jl and AMDGPU.jl, so it's easy to transition to. I think the event system in KA is also far superior to CUDA's native synchronization system, since it allows one to easily express graphs of dependencies between kernels and data transfers.
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A fun fact is that the GPUCompiler, which compiles the code to run in GPU's, is the current way to generate binaries without hiding the whole ~200mb of julia runtime in the binary.
https://github.com/JuliaGPU/GPUCompiler.jl/ https://github.com/tshort/StaticCompiler.jl/
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A fun fact is that the GPUCompiler, which compiles the code to run in GPU's, is the current way to generate binaries without hiding the whole ~200mb of julia runtime in the binary.
https://github.com/JuliaGPU/GPUCompiler.jl/ https://github.com/tshort/StaticCompiler.jl/
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https://github.com/JuliaGPU/AMDGPU.jl
https://github.com/JuliaGPU/oneAPI.jl
These are both less mature than CUDA.jl, but are in active development.
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https://github.com/JuliaGPU/AMDGPU.jl
https://github.com/JuliaGPU/oneAPI.jl
These are both less mature than CUDA.jl, but are in active development.
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I wish more attention would be towards open source alternatives for CUDA, such as AMD's ROCm[1][2]. It's sad to see so many people praise NVIDIA which is the enemy of open source, openly hostile to anything except their oversized proprietary binary blobs.
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