SIMD acceleration for spatial interaction kernels - #590
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…ential) - Add float SLEEF macros to sleef_config.h (AVX2: 8 floats, NEON: 4 floats) - Add exp_kernel_float32() and normal_kernel_float32() to eidos_simd.h - Use SIMD kernels in FillSparseVectorForReceiverStrengths() - Add benchmark script for spatial interaction kernels
Modify FillSparseVectorForReceiverStrengths() to use the two-pass distance-then-transform path for most kernel types in 2D, enabling SIMD optimizations for Exponential and Normal kernels. The Fixed kernel retains the original single-pass special-case path since it doesn't benefit from SIMD (just assigns a constant). Benchmarks show 22% overall speedup at high neighbor counts (~2200), with Exponential and Normal kernels seeing 38-42% improvement.
Add SLEEF pow() function support (Sleef_powf8_u10avx2 for AVX2, Sleef_powf4_u10advsimd for NEON) and implement tdist_kernel_float32() to vectorize the Student's T distribution kernel calculation. The kernel computes: strength = fmax / pow(1 + (d/tau)^2 / nu, (nu+1)/2) Benchmarks show 62% speedup for Student's T kernel (130s -> 49s), contributing to 38% overall speedup for spatial interaction benchmarks.
Implement cauchy_kernel_float32() using AVX2/NEON intrinsics for the Cauchy kernel calculation: strength = fmax / (1 + (d/lambda)^2) Unlike exp/normal/tdist kernels, Cauchy uses only basic arithmetic operations (multiply, divide, add) so no SLEEF functions are needed. Benchmarks show 18% speedup vs original (40s -> 33s).
Implement linear_kernel_float32() using AVX2/NEON intrinsics for the Linear kernel calculation: strength = fmax * (1 - d / max_distance) Rewritten as: strength = fmax - d * (fmax / max_distance) Simple arithmetic (multiply + subtract) so gains are modest (~2%), but provides consistency with other SIMD-optimized kernels.
Remove sleef_benchmark_spatial_interaction.slim (only tested Gaussian/Exponential) and add benchmark_all_kernels.slim which tests all 6 kernel types: Fixed, Linear, Exponential, Normal, Cauchy, and Student's T.
Add documentation for benchmark_all_kernels.slim script including: - Entry in Contents section describing the 6 kernel types tested - Performance results table showing SIMD speedups on AVX2 - Usage instructions for running with SIMD vs scalar builds - Notes on adjusting neighbor density via W parameter 🤖 Generated with [Claude Code](https://claude.com/claude-code)
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This looks good to me. Awesome performance improvements. You are knocking it out of the park! |
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Adds SIMD-optimized (AVX2/NEON) kernel functions for spatial interactions using SLEEF for transcendental functions. All kernel types except Fixed now use a two-pass approach (build distances, then batch transform) enabling vectorized strength calculations.
Summary Benchmark
50k individuals, ~2262 neighbors