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Multi-vector MaxSim benchmark with BYOTE factory #1027

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77cc936
Add benchmark crate for multi-vector
suri-kumkaran May 6, 2026
54a21ec
Move some repetetive code to macros and add more benchmark cases
May 6, 2026
f3a5d9f
Move some repetetive code to macros and add more benchmark cases
May 6, 2026
8efdbcd
Move some repetetive code to macros and add more benchmark cases
May 6, 2026
74d1cae
Merge branch 'main' into users/suryangupta/multi-vector-benchmark
suri-kumkaran May 7, 2026
3a89c37
Add Cargo.lock
suri-kumkaran May 7, 2026
96d17b3
Remove unused scalar benchmark config file
suri-kumkaran May 7, 2026
6b33719
Revert "Remove unused scalar benchmark config file"
suri-kumkaran May 7, 2026
d06df7e
Fold the new crate to existing diskann-benchmark crate
suri-kumkaran May 12, 2026
41ee380
Merge branch 'main' into users/suryangupta/multi-vector-benchmark
suri-kumkaran May 12, 2026
a64279e
Revamp the benchmark to be also kernel-research seam
suri-kumkaran May 14, 2026
94fd8de
Minor doc fix
May 14, 2026
9624da4
Merge branch 'main' into users/suryangupta/multi-vector-benchmark
suri-kumkaran May 18, 2026
f51bc2a
MaxSim benchmark with BYOTE factory
suri-kumkaran May 18, 2026
597330a
Address review comments
suri-kumkaran May 19, 2026
725ad08
Merge remote-tracking branch 'origin/main' into users/suryangupta/mul…
May 19, 2026
03d6119
Fix after main merge
May 19, 2026
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3 changes: 3 additions & 0 deletions diskann-benchmark/Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -63,6 +63,9 @@ scalar-quantization = []
# Enable minmax-quantization based algorithms
minmax-quantization = []

# Enable multi-vector MaxSim distance benchmarks
multi-vector = []

# Enable Disk Index benchmarks
disk-index = [
"diskann-disk/perf_test",
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47 changes: 47 additions & 0 deletions diskann-benchmark/example/multi-vector.json
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@@ -0,0 +1,47 @@
{
"search_directories": [],
"jobs": [
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "auto",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 2, "num_measurements": 1 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 2, "num_measurements": 1 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "scalar",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 2, "num_measurements": 1 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "reference",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 2, "num_measurements": 1 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 2, "num_measurements": 1 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float16",
"isa": "auto",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 2, "num_measurements": 1 }
]
}
}
]
}
16 changes: 16 additions & 0 deletions diskann-benchmark/perf_test_inputs/multi-vector-tolerance.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,16 @@
{
"checks": [
{
"input": {
"type": "multi-vector-op",
"content": {}
},
"tolerance": {
"type": "multi-vector-tolerance",
"content": {
"min_time_regression": 0.05
}
}
}
]
}
149 changes: 149 additions & 0 deletions diskann-benchmark/perf_test_inputs/multi-vector.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,149 @@
{
"search_directories": [],
"jobs": [
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "auto",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "scalar",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "x86-64-v3",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "x86-64-v4",
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@hildebrandmw hildebrandmw May 22, 2026

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Including AVX-512 in the perf test will run into an issue I constantly run into in diskann-benchmark-simd and haven't yet fixed and that is including an option to skip if the arch is unsupported rather than failing. Mainly as an ergonomics thing.

Also, running this on a non-AVX-512 supported machine fails at at runtime, i.e.:

######################
# Running Job 4 of 8 #
######################

Multi-Vector Operation

               tag: multi-vector-op
      element type: float32
               isa: x86-64-v4
    number of runs: 9

Error: x86-64-v4 not supported: AVX-512 unavailable on this CPU

instead of at load or match time. Please bake the check into Benchmark::try_match as much as possible so we error out as early as possible.

"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float32",
"isa": "reference",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float16",
"isa": "x86-64-v3",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float16",
"isa": "x86-64-v4",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
},
{
"type": "multi-vector-op",
"content": {
"element_type": "float16",
"isa": "reference",
"runs": [
{ "num_query_vectors": 8, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 500, "num_measurements": 50 },
{ "num_query_vectors": 16, "num_doc_vectors": 64, "dim": 256, "loops_per_measurement": 100, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 128, "dim": 384, "loops_per_measurement": 20, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 16, "dim": 256, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 264, "loops_per_measurement": 50, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 1250, "dim": 128, "loops_per_measurement": 10, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 1250, "dim": 512, "loops_per_measurement": 2, "num_measurements": 50 },
{ "num_query_vectors": 64, "num_doc_vectors": 32, "dim": 128, "loops_per_measurement": 200, "num_measurements": 50 },
{ "num_query_vectors": 32, "num_doc_vectors": 32, "dim": 512, "loops_per_measurement": 50, "num_measurements": 50 }
]
}
}
]
}
2 changes: 2 additions & 0 deletions diskann-benchmark/src/backend/mod.rs
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Expand Up @@ -9,11 +9,13 @@ mod disk_index;
mod exhaustive;
mod filters;
mod index;
mod multi_vector;

pub(crate) fn register_benchmarks(registry: &mut Registry) -> anyhow::Result<()> {
exhaustive::register_benchmarks(registry)?;
disk_index::register_benchmarks(registry)?;
index::register_benchmarks(registry)?;
filters::register_benchmarks(registry)?;
multi_vector::register_benchmarks(registry)?;
Ok(())
}
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