simsimd 1.3.0

SIMD-accelerated similarity measures for x86 and Arm

SimSIMD 📏

SIMD-accelerated similarity measures, metrics, and distance functions for x86 and Arm. They are tuned for Machine Learning applications and mid-size vectors with 100-1024 dimensions. One can expect the following performance for Cosine (Angular) distance, the most common metric in AI.

Method	Vectors	Any Length	Speed on 256b	Speed on 1024b
Serial	f32	✅	5 GB/s	5 GB/s
SVE	f32	✅	34 GB/s	40 GB/s
SVE	f16	✅	28 GB/s	35 GB/s
NEON	f16	❌	16 GB/s	18 GB/s

The benchmarks were done on Arm-based "Graviton 3" CPUs powering AWS c7g.metal instances. We only use Arm NEON implementation with vector lengths multiples of 128 bits, avoiding additional head or tail for loops for misaligned data. By default, we use GCC12, -O3, -march=native for benchmarks. Serial versions imply auto-vectorization pragmas.

Need something like this in your CMake-based project?

FetchContent_Declare(
    simsimd
    GIT_REPOSITORY https://github.com/ashvardanian/simsimd.git
    GIT_SHALLOW TRUE
)
FetchContent_MakeAvailable(simsimd)
include_directories(${simsimd_SOURCE_DIR}/include)

Want to use it in Python with USearch?

from usearch import Index, CompiledMetric, MetricKind, MetricSignature
from simsimd import to_int, cos_f32x4_neon

metric = CompiledMetric(
    pointer=to_int(cos_f32x4_neon),
    kind=MetricKind.Cos,
    signature=MetricSignature.ArrayArraySize,
)

index = Index(256, metric=metric)

Available Metrics

In the C99 interface, all functions are prepended with the simsimd_ namespace prefix. The signature defines the number of arguments:

• two pointers, and length,
• two pointers.

The latter is intended for cases where the number of dimensions is hard-coded. Constraints define the limitations on the number of dimensions an argument vector can have.

Name	Signature	ISA Extension	Constraints
dot_f32_sve	✳️✳️📏	Arm SVE
dot_f32x4_neon	✳️✳️📏	Arm NEON	d % 4 == 0
cos_f32_sve	✳️✳️📏	Arm SVE
cos_f16_sve	✳️✳️📏	Arm SVE
cos_f16x4_neon	✳️✳️📏	Arm NEON	d % 4 == 0
cos_i8x16_neon	✳️✳️📏	Arm NEON	d % 16 == 0
cos_f32x4_neon	✳️✳️📏	Arm NEON	d % 4 == 0
cos_f16x16_avx512	✳️✳️📏	x86 AVX-512	d % 16 == 0
cos_f32x4_avx2	✳️✳️📏	x86 AVX2	d % 4 == 0
l2sq_f32_sve	✳️✳️📏	Arm SVE
l2sq_f16_sve	✳️✳️📏	Arm SVE
hamming_b1x8_sve	✳️✳️📏	Arm SVE	d % 8 == 0
hamming_b1x128_sve	✳️✳️📏	Arm SVE	d % 128 == 0
hamming_b1x128_avx512	✳️✳️📏	x86 AVX-512	d % 128 == 0
tanimoto_b1x8_naive	✳️✳️📏		d % 8 == 0
tanimoto_maccs_naive	✳️✳️		d == 166
tanimoto_maccs_neon	✳️✳️	Arm NEON	d == 166
tanimoto_maccs_sve	✳️✳️	Arm SVE	d == 166
tanimoto_maccs_avx512	✳️✳️	x86 AVX-512	d == 166

Benchmarks

The benchmarks are repeated for every function with a different number of cores involved. Light-weight distance functions would be memory bound, implying that multi-core performance may be lower if the bus bandwidth cannot saturate all the cores. Similarly, heavy-weight distance functions running on all cores may result in CPU frequency downclocking. This is well illustrated by the single-core performance of the Intel i9-13950HX, equipped with DDR5 memory.

Method	Threads	Vector Size	Speed
dot_f32x4_avx2	1	1024 b	96.2 GB/s
dot_f32x4_avx2	32	1024 b	23.6 GB/s
cos_f32_naive	1	1024 b	15.3 GB/s
cos_f32_naive	32	1024 b	4.5 GB/s
cos_f32x4_avx2	1	1024 b	56.3 GB/s
cos_f32x4_avx2	32	1024 b	13.9 GB/s
tanimoto_maccs_naive	1	21 b	2.8 GB/s
tanimoto_maccs_naive	32	21 b	1.2 GB/s

Switching to the Intel Sapphire Rapids server platform, we can also evaluate some of the AVX-512 extensions, including VPOPCNTDQ and F16.

Method	Threads	Vector Size	Speed
dot_f32x4_avx2	1	1024 b	57.8 GB/s
dot_f32x4_avx2	224	1024 b	16.1 GB/s
cos_f32_naive	1	1024 b	10.7 GB/s
cos_f32_naive	224	1024 b	3.0 GB/s
cos_f32x4_avx2	1	1024 b	39.5 GB/s
cos_f32x4_avx2	224	1024 b	15.1 GB/s
cos_f16x16_avx512	1	1024 b	50.6 GB/s
cos_f16x16_avx512	224	1024 b	15.9 GB/s
hamming_b1x128_avx512	1	1024 b	790.3 GB/s
hamming_b1x128_avx512	224	1024 b	259.3 GB/s
tanimoto_maccs_naive	1	21 b	3.0 GB/s
tanimoto_maccs_naive	224	21 b	1.3 GB/s
tanimoto_maccs_avx512	1	21 b	13.1 GB/s
tanimoto_maccs_avx512	224	21 b	3.7 GB/s

To replicate this on your hardware, please run the following on Linux:

git clone https://github.com/ashvardanian/SimSIMD.git && cd SimSIMD
cmake -DCMAKE_BUILD_TYPE=Release -DSIMSIMD_BUILD_BENCHMARKS=1 \
    -DCMAKE_CXX_COMPILER="g++-12" -DCMAKE_C_COMPILER="gcc-12" \
    -B ./build && make -C ./build && ./build/simsimd_bench

MacOS:

brew install llvm
git clone https://github.com/ashvardanian/SimSIMD.git && cd SimSIMD
cmake -B ./build \
    -DCMAKE_C_COMPILER="/opt/homebrew/opt/llvm/bin/clang" \
    -DCMAKE_CXX_COMPILER="/opt/homebrew/opt/llvm/bin/clang++" \
    -DSIMSIMD_BUILD_BENCHMARKS=1 \
    && \
    make -C ./build -j && ./build/simsimd_bench

Install and test locally:

pip install -e . && pytest python/test.py -s -x