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A hands-on exploration of AVX-512 SIMD programming through implementing K-Means clustering for image segmentation. The author benchmarks scalar, auto-vectorized, and hand-written intrinsics code, achieving 7-8.5x speedup over scalar (half the theoretical 16x) and 4x faster than compiler auto-vectorization. Compares SIMD's explicit programming model with CUDA's SIMT abstraction, finding SIMD more transparent for performance reasoning despite verbose syntax. Includes speed-of-light analysis showing hand-optimized code reaching near-theoretical limits (344ms vs 337ms estimate) and experiments with LLMs successfully generating AVX-512 intrinsics from scalar code.

AVX-512: First Impressions on Performance and Programmability