Эксперимент: Expander OMP Sparse Inference (#1269)

{ "worked": false, "confidence": 9, "verdict": "Built dense and edge-wise OMP using fixed-degree sparse dictionaries stored as edge arrays. The core correlation identity was verified to floating-point precision (maximum error 4.4e-16), and both OMP implementations produced identical supports, coefficients, and reconstruction MSE (9.58e-5). However, the transparent unfused edge gather-reduce was slower than optimized dense BLAS by 1.8x–163x in the tested sizes, so the intended speedup was not observed; a fused kernel is needed for a fair systems-level test.", "metrics": { "baseline": "Dense NumPy BLAS correlation; reconstruction MSE 9.5779e-5. Correlation times for n=128,256,512,1024 were 0.00701, 0.00191, 0.00393, and 0.000396 seconds.", "idea": "Edge-wise correlation with d=6 and m=96, nominal arithmetic ratio d/m=0.0625. Correlation and OMP outputs matched exactly numerically, but edge/dense wall-time ratios were 1.82x, 13.56x, 13.07x, and 162.84x for n=128,256,1024 respectively.", "note": "The reported timing sequence is noisy for small BLAS workloads; all measured edge implementations were slower, so no speed win was claimed." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 expander_omp.py", "files": [ "expander_omp.py", "results.json", "README.txt" ], "limitations": "Only synthetic CPU experiments were run. The edge reduction is a Python loop rather than a fused CUDA or compiled scatter-reduce kernel; no Pythia residuals, amortized TopK encoder, GPU benchmark, peak-memory measurement, or large-scale end-to-end inference test was performed." }