Experimental Multi-Vector Chamfer Distance with SIMD & BLAS Optimizations

[suri-kumkaran]

Summary

Experimental multi-vector support with fast Chamfer distance for f32 embeddings and benchmarking infrastructure.

Changes

Core Types

• MultiVector - Row-major token embeddings
• TransposedMultiVector - Block-transposed layout (16 vectors/block, SIMD-optimized)
• Chamfer<Approach> - Generic distance using Inner Product (implements DistanceFunction)

Implementations

• NaiveApproach - Scalar baseline
• SimdApproach - SIMD via diskann_vector::InnerProduct
• TransposedApproach - Block-transposed SIMD
• TransposedWithTilingApproach - Query tiling (transposes docs, processes query pairs)
• QueryTransposedWithTilingApproach - Doc tiling (transposes query, processes doc pairs)
• SgemmApproach - BLAS SGEMM + SIMD row-max (via faer library)

Benchmark Results (100 points, 10 iterations)

Machine: Intel Core i7-1365U, AVX2 supported, AVX-512 not supported, 32 GB RAM
Note: Times are median over 50 measurements, each measuring 10 consecutive distance computations across 100 points.

Speedup vs SIMD Baseline (Median, Lower Latency = Better)

Configuration	SIMD (µs)	transposed_simd	transposed_tiling	query_transposed_tiling	sgemm
dim=128, doc=32, query=8	2,237	1.34x	1.75x	1.05x	1.13x
dim=128, doc=64, query=16	9,224	1.42x	2.07x	2.35x	1.48x
dim=128, doc=128, query=32	47,882	1.32x	1.86x	2.64x	1.88x
dim=256, doc=32, query=8	4,654	1.26x	1.69x	1.13x	0.96x
dim=256, doc=64, query=16	25,809	1.56x	1.94x	2.40x	1.87x
dim=256, doc=128, query=32	101,093	1.41x	1.71x	2.67x	1.96x
dim=256, doc=16, query=32	8,239	1.22x	1.77x	2.02x	1.57x
dim=384, doc=32, query=8	8,412	1.41x	1.65x	1.30x	1.24x
dim=384, doc=64, query=16	38,162	1.30x	1.47x	1.70x	1.66x
dim=384, doc=128, query=32	171,431	1.53x	1.94x	2.04x	2.16x
Average	—	1.38x	1.79x	1.93x	1.59x

Dimension 256

Approach	Q×D	Mean (µs)	Speedup
SIMD	8×32	3328	1.0x
Tiling	8×32	2207	1.5x
QueryTiling	8×32	3243	1.0x
SGEMM	8×32	2675	1.2x
SIMD	16×64	18464	1.0x
Tiling	16×64	10458	1.8x
QueryTiling	16×64	6326	2.9x
SGEMM	16×64	7014	2.6x
SIMD	32×128	72197	1.0x
Tiling	32×128	39453	1.8x
QueryTiling	32×128	25357	2.8x
SGEMM	32×128	19727	3.7x

Dimension 384

Approach	Q×D	Mean (µs)	Speedup
SIMD	8×32	7026	1.0x
Tiling	8×32	3609	1.9x
QueryTiling	8×32	4816	1.5x
SGEMM	8×32	4077	1.7x
SIMD	16×64	27518	1.0x
QueryTiling	16×64	9750	2.8x
SGEMM	16×64	10070	2.7x
SIMD	32×128	107772	1.0x
Tiling	32×128	50072	2.2x
QueryTiling	32×128	50669	2.1x
SGEMM	32×128	28764	3.7x

Future Work

• Add RFC based on findings for DiskANN integration
• Support for additional element types (f16, u8 quantized, etc.)
• AVX-512 support: Larger registers could enable tile size 4 (processing 4 queries simultaneously)
• SIMD-accelerated horizontal MinMax: Hardware-accelerated horizontal min/max reductions across SIMD lanes for faster per-query score aggregation

Testing

cargo build --release -p multi-vector
cargo run --release -p multi-vector --bin multivec-bench -- run \
    --input-file multi-vector/examples/bench.json --output-file results.json

Contributing

This work is experimental and will be submitted as separate PRs.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 69.63087% with 362 lines in your changes missing coverage. Please review.
✅ Project coverage is 88.72%. Comparing base (f3fcaae) to head (0134ebd).

Files with missing lines	Patch %	Lines
...ctor-bench/src/distance/query_transposed_tiling.rs	49.53%	163 Missing ⚠️
...lti-vector-bench/src/distance/transposed_tiling.rs	43.50%	113 Missing ⚠️
experimental-multi-vector-bench/src/bench/mod.rs	70.88%	23 Missing ⚠️
...ntal-multi-vector-bench/src/distance/transposed.rs	75.00%	23 Missing ⚠️
experimental-multi-vector-bench/src/bench/input.rs	68.00%	16 Missing ⚠️
...xperimental-multi-vector-bench/src/bench/runner.rs	95.37%	10 Missing ⚠️
diskann-quantization/src/multi_vector/matrix.rs	63.63%	4 Missing ⚠️
...ental-multi-vector-bench/src/bin/multivec_bench.rs	87.87%	4 Missing ⚠️
...erimental-multi-vector-bench/src/distance/sgemm.rs	97.79%	3 Missing ⚠️
...xperimental-multi-vector-bench/src/multi_vector.rs	87.50%	3 Missing ⚠️

❌ Your patch status has failed because the patch coverage (69.63%) is below the target coverage (90.00%). You can increase the patch coverage or adjust the target coverage.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #730      +/-   ##
==========================================
- Coverage   89.01%   88.72%   -0.29%     
==========================================
  Files         428      440      +12     
  Lines       78294    79483    +1189     
==========================================
+ Hits        69692    70521     +829     
- Misses       8602     8962     +360     

Files with missing lines	Coverage Δ
...xperimental-multi-vector-bench/src/distance/mod.rs	100.00% <100.00%> (ø)
...erimental-multi-vector-bench/src/distance/naive.rs	100.00% <100.00%> (ø)
...perimental-multi-vector-bench/src/distance/simd.rs	100.00% <100.00%> (ø)
...erimental-multi-vector-bench/src/distance/sgemm.rs	97.79% <97.79%> (ø)
...xperimental-multi-vector-bench/src/multi_vector.rs	87.50% <87.50%> (ø)
diskann-quantization/src/multi_vector/matrix.rs	97.44% <63.63%> (-0.71%)	⬇️
...ental-multi-vector-bench/src/bin/multivec_bench.rs	87.87% <87.87%> (ø)
...xperimental-multi-vector-bench/src/bench/runner.rs	95.37% <95.37%> (ø)
experimental-multi-vector-bench/src/bench/input.rs	68.00% <68.00%> (ø)
experimental-multi-vector-bench/src/bench/mod.rs	70.88% <70.88%> (ø)
... and 3 more

... and 3 files with indirect coverage changes

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[hildebrandmw]

Something to think about: the creep of Standard specific methods is not something I think we should lean into - especially if we want this to replace diskann_utils::views::Matrix and friends.

I've found myself needing something like this for some other multi-vector related work and I think it makes sense to have something like

trait Dense: Repr {
    type Element;
    unsafe fn as_slice(&self, ptr: NonNull<u8>) -> &[Self::Element];

}

trait DenseMut: Dense + ReprMut {
    unsafe fn as_slice_mut(&mut self, ptr: NonNull<u8>) -> &mut [Self::Element];
}

This way, MinMax, transposed, blocked etc. can all opt-in to this as well. That said, I feel that the lack of ability to add inherent methods to be a little unfortunate.

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