Panorama Optimizations

[aknayar]

Note: Should be merged before #4970 (IVFPQPanorama).

Changes

Performance

This PR implements various optimizations to Panorama (L2Flat and IVFFlat).

1. Disaggregate distance computation from pruning decisions to avoid branches in distance computation hotpath.
2. Early batch processing termination when no points are remaining.
3. Manually unrolled distance kernel.
4. Template distance computation on level width for autovectorization.
5. if constexpr (C::is_max) instead of C::cmp for autovectorized pruning.
6. Byteset for vectorized compacting of active indices using _pext_u64.
7. Template distance computation and pruning on first level (no active_indices indirection) to let it autovectorize.
8. Hoist buffer allocations into IndexFlat/IVFFlatScannerPanorama.

Other

• Define kDefaultBatchSize once in Panorama.h (previously defined in 5 separate locations).
• Allow bench_flat_l2_panorama.py and bench_ivf_flat_panorama.py to accept gist1M or sift1M as dataset to bench on.

Results

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[aknayar]

Unfortunately C::cmp() kills autovectorization here so we resort to this workaround.