[HN]score: 0.10
Matrix Transpose Optimized from 3.90 to 0.35 Cycles via SIMD and Blocking
May 24, 2026
A step-by-step optimization of in-memory matrix transposition reduces cycles per element from 3.90 (naive) to 0.35 using cache blocking, 256-bit SIMD, and output buffering to address cache aliasing and memory latency.
HOW THIS AFFECTS YOU
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builderYou can apply these blocking and SIMD techniques directly to custom matrix ops or ML inference kernels where transpose is a bottleneck.
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researcherConcrete worked example of cache-aware algorithm design with measured cycle counts at each optimization stage — useful reference for low-level ML kernel work.