AVX2 implementation

2026-05-25 11:43:58 +02:00
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+# Optimization: AVX2 vectorization of the ped-ped loop
+
+- **Author**: Linus
+- **Predecessor**: variant_inlining_7_SoA.cpp
+- **File(s)**: variants/variant_inlining_7_SoA_AVX2.cpp
+- **Status**: TBD, positive locally (approx.2 times)
+
+## What this changes
+
+First AVX2 pass through the ped-ped inner loop. The `j` loop strides by
+4 and processes 4 pedestrians at once on `__m256d` registers. SoA from
+the predecessor variant is what makes the loads contiguous, otherwise
+this would not have been clean to write.
+
+The `i == j` skip is now a mask instead of `continue`. I also pad all
+the per-pedestrian arrays to a multiple of 4 (`n_p_pad`) so the loop
+doesn't need a scalar tail. Padding positions are set to `1e9` so even
+without the mask the lane would contribute ~0 via exp(very negative),
+but the mask makes it deterministic. Same mask is also used to zero out
+the i==j lane after the body.
+
+The scalar `exp()` is still called 4 times per j step into a scratch
+array. 
+
+## Code (relevant snippet)
+
+See `variants/variant_inlining_7_SoA_AVX2.cpp` for the full inner loop.
+Structure is: build the active mask from `j_idx` vs `i` and `n_p`, run
+the vector body on 4 lanes at a time, blend in a safe denom so the
+i==j lane does not produce a NaN, mask the per-lane force contribution,
+then accumulate into `acc_x` and `acc_y`. After the j loop a horizontal
+sum folds the accumulators into the scalar `f_alpha_dt`.
+
+## Benchmark
+
+Conditions: min of N runs (TBD), N_ITER=1000, -O3 -march=native -flto.
+Diff is vs `variant_inlining_7_SoA`.
+
+local runs show a clear speedup over the SoA-only predecessor. exact
+numbers TBD on the test machine, but the qualitative picture is that
+the 4 scalar `exp()` calls inside the j loop now dominate.
+### x86
+
+**Machine**: TBD
+
+**Frequency**: TBD
+
+| N    | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Predecessor |
+| ---- | ------------ | ------------- | ------------------------- | ------------------- |
+| 50   |              |               |                           |                     |
+| 100  |              |               |                           |                     |
+| 200  |              |               |                           |                     |
+| 500  |              |               |                           |                     |
+| 1000 |              |               |                           |                     |
+
+### ARM (Apple)
+
+Not applicable, this variant uses AVX2 intrinsics
+## Analysis
+
+The win comes from doing 4 ped-ped interactions per j step instead of 1.
+Loads are contiguous thanks to SoA, and the inner body is mostly
+straight-line `sub`, `fmadd`, `mul`, `sqrt`, one `div`, plus a `blendv`
+and a couple of `cmp`/`and` for the mask. No branches in the j loop
+other than the loop counter.
+
+I expected `exp()` to become the bottleneck once everything around it
+was vectorized, and the profile confirmed it. The horizontal sum at the
+end of the j loop only happens once per outer iter, not per j step, so
+its cost is small.
+
+
+
+## Takeaway
+
+First real perf jump for the ped-ped force, but limited by the scalar
+`exp` call still inside the loop. Sets up the polyexp variant.