Add performance analysis: implementation gaps vs structural limits relative to FFTW - #129
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Add performance analysis: implementation gaps vs structural limits relative to FFTW#129pankgeorg wants to merge 5 commits into
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…7-smooth composites), not a uniform SIMD effect
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A written analysis of why FFTA is slower than FFTW on each size class, separating what is an implementation gap (fixable within the current design) from what is structural (pure-Julia, size-generic recursion, no SIMD codelet library), with a per-class ceiling estimate and a prioritised work list.
It is based on the sweep in #128 (
benchmark/REPORT.md, which this document links to — the link resolves once #128 lands) plus targeted experiments: a precomputed-twiddle variant of the O(n²) leaf, a breakdown of the Bluestein andrfftpaths, FFTW's actual plan choices on the same sizes, and a small@generatedstraight-line codelet generator to measure Julia's compile-latency cost for codelets (< 1 s up to 64 points, 1.0–1.6× FFTW).Short version: no size class is structurally stuck at ≥ 4×; the large gaps (composites with factors 5/7, primes, small ND, 2D
rfft) come from twiddles being recomputed per execution, O(n²) leaves without codelets, per-call Bluestein allocation, and themapslices/full-complex real paths. The structural residue is the last ~1.3–2× on cache-resident sizes (SIMD across butterflies) and the cache-blocked large-n path that FFTW'sMEASUREplanner finds.I'll open an issue with the proposed PR sequence so the plan can be discussed before code lands.
Update: a companion run of the suite on an x86-64 (AVX2) machine is now part of #128, and its per-class comparison against the aarch64 run showed that the x86-64 penalty is concentrated on the non-radix-4 kernels (5/7-rich composites up to 3.8× worse relative to FFTW, powers of two only 1.21×), not spread uniformly as a vector-width effect. §5.3a records the data, §5.3 and the ceiling table (§6) are adjusted per class, and radix-5/7 butterflies move up the work list on x86-64.