Introducing @reduce for group level reduction #379
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lib/CUDAKernels/src/CUDAKernels.jl
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| threadIdx = KernelAbstractions.@index(Local) | ||
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| # shared mem for a complete reduction | ||
| shared = KernelAbstractions.@localmem(T, 1024) |
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Maybe this is the moment we need dynamic shared memory support?
lib/CUDAKernels/src/CUDAKernels.jl
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| # perform the reduction | ||
| d = 1 | ||
| while d < threads | ||
| KernelAbstractions.@synchronize() |
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You are inside CUDAKernels here and as such you can use CUDA.jl functionality directly.
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Thats correct! But a implementation with KA.jl macros would allow for a single implementation that can run on all supported back-end. Because of this I am not sure what the best place is for the code for this implementation.
Also, the main difference between different back-end would the size of local memory but the use of dynamic memory would be a solution to this.
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Looks like a great start! Will have to add it to |
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To make a more generalized @reduce operation, I would work with a Config struct. An example of this can be found in the GemmKernels.jl Config. Based on this struct, the reduction could use atomics and lane/warp reductions. |
The @reduce macro performs a group level reduction.
TODOs: