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GPU Systems 10 - Tiled Matrix Multiplication and Shared Memory
Why tiled matrix multiplication and shared memory create such a big performance difference
Why tiled matrix multiplication and shared memory create such a big performance difference
Why shared memory is not automatically fast and how bank conflicts appear
Why warp-level primitives matter for reductions and lighter-weight cooperation
Using reduction kernels to connect shared memory, warp primitives, and synchronization
How softmax combines reductions, memory traffic, and numerical stability in one kernel
Why normalization kernels are often memory-bound and structurally important
How wider memory operations and alignment affect bandwidth utilization
Why using more registers can improve local efficiency but still reduce total throughput
How tensor cores change performance in compute-heavy kernels and why mixed precision matters