My previous CUDA Fortran post covered the mechanics of using shared memory, including static and dynamic allocation. In this post I will show some of the performance gains achievable using shared memory. Specifically, I will optimize a matrix transpose to show how to use shared memory to reorder strided global memory accesses into coalesced accesses. The code we wish to optimize is a transpose��
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