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What Shapes Do Matrix Multiplications Like? [medium]

thonking.ai · 2,271 words · saved by 1 readers

A while back, Karpathy tweeted that increasing the size of his matmul made it run faster. Surprisingly, it’s not just relatively faster, it takes less absolute time. In other words, despite doing more work, it is executing in less time. This may seem intuitively quite strange. Is cuBLAS just messing up somehow? Why doesn’t the matrix multiplication kernel just pad it to a larger shape? It has become tribal knowledge that the particular shapes chosen for matmuls has a surprisingly large effect on their performance. But … why? Can this be understood by mere mortals? Let’s take a crack at it. First, let’s plot FLOPs achieved for square matmuls. By the end of this article, I will aim to explain all the strange squiggly lines. There are 3 general concepts to understand that explain the majority of performance variation among matmul shapes. Compute Intensity/Parallelization: This explains the general upward trend Tiling: This explains the multiple tiers of lines. Wave Quantization: This expl

A while back, Karpathy tweeted that increasing the size of his matmul made it run faster. Surprisingly, it’s not just relatively faster, it takes less absolute time. In other words, despite doing more work, it is executing in less time. This may seem intuitively quite strange. Is cuBLAS just messing up somehow? Why doesn’t the matrix multiplication kernel just pad it to a larger shape? It has become tribal knowledge that the particular shapes chosen for matmuls has a surprisingly large effect on their performance. But … why? Can this be understood by mere mortals? Let’s take a crack at…

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