SimSIMD 📏

Computing dot-products, similarity measures, and distances between low- and high-dimensional vectors is ubiquitous in Machine Learning, Scientific Computing, Geo-Spatial Analysis, and Information Retrieval. These algorithms generally have linear complexity in time, constant complexity in space, and are data-parallel. In other words, it is easily parallelizable and vectorizable and often available in packages like BLAS and LAPACK, as well as higher-level numpy and scipy Python libraries. Ironically, even with decades of evolution in compilers and numerical computing, most libraries can be 3-200x slower than hardware potential even on the most popular hardware, like 64-bit x86 and Arm CPUs. SimSIMD attempts to fill that gap. 1️⃣ SimSIMD functions are practically as fast as memcpy. 2️⃣ SimSIMD compiles to more platforms than NumPy (105 vs 35) and has more backends than most BLAS implementations.

Features

SimSIMD provides over 100 SIMD-optimized kernels for various distance and similarity measures, accelerating search in USearch and several DBMS products. Implemented distance functions include:

Euclidean (L2) and Cosine (Angular) spatial distances for Vector Search.
Dot-Products for real & complex vectors for DSP & Quantum computing.
Hamming (~ Manhattan) and Jaccard (~ Tanimoto) bit-level distances.
Kullback-Leibler and Jensen–Shannon divergences for probability distributions.
Haversine and Vincenty's formulae for Geospatial Analysis.
For Levenshtein, Needleman–Wunsch and other text metrics, check StringZilla.

Moreover, SimSIMD...

handles f64, f32, and f16 real & complex vectors.
handles i8 integral and b8 binary vectors.
is a zero-dependency header-only C 99 library.
has bindings for Python, Rust and JavaScript.
has Arm backends for NEON and Scalable Vector Extensions (SVE).
has x86 backends for Haswell, Skylake, Ice Lake, and Sapphire Rapids.

Due to the high-level of fragmentation of SIMD support in different x86 CPUs, SimSIMD uses the names of select Intel CPU generations for its backends. They, however, also work on AMD CPUs. Inel Haswell is compatible with AMD Zen 1/2/3, while AMD Genoa Zen 4 covers AVX-512 instructions added to Intel Skylake and Ice Lake. You can learn more about the technical implementation details in the following blogposts:

Benchmarks

Against NumPy and SciPy

Given 1000 embeddings from OpenAI Ada API with 1536 dimensions, running on the Apple M2 Pro Arm CPU with NEON support, here's how SimSIMD performs against conventional methods:

Kind	`f32` improvement	`f16` improvement	`i8` improvement	Conventional method	SimSIMD
Inner Product	2 x	9 x	18 x	`numpy.inner`	`inner`
Cosine Distance	32 x	79 x	133 x	`scipy.spatial.distance.cosine`	`cosine`
Euclidean Distance ²	5 x	26 x	17 x	`scipy.spatial.distance.sqeuclidean`	`sqeuclidean`
Jensen-Shannon Divergence	31 x	53 x		`scipy.spatial.distance.jensenshannon`	`jensenshannon`

Against GCC Auto-Vectorization

On the Intel Sapphire Rapids platform, SimSIMD was benchmarked against auto-vectorized code using GCC 12. GCC handles single-precision float but might not be the best choice for int8 and _Float16 arrays, which have been part of the C language since 2011.

Kind	GCC 12 `f32`	GCC 12 `f16`	SimSIMD `f16`	`f16` improvement
Inner Product	3,810 K/s	192 K/s	5,990 K/s	31 x
Cosine Distance	3,280 K/s	336 K/s	6,880 K/s	20 x
Euclidean Distance ²	4,620 K/s	147 K/s	5,320 K/s	36 x
Jensen-Shannon Divergence	1,180 K/s	18 K/s	2,140 K/s	118 x

Broader Benchmarking Results:

Using SimSIMD in Python

The package is intended to replace the usage of numpy.inner, numpy.dot, and scipy.spatial.distance. Aside from drastic performance improvements, SimSIMD significantly improves accuracy in mixed precision setups. NumPy and SciPy, processing i8 or f16 vectors, will use the same types for accumulators, while SimSIMD can combine i8 enumeration, i16 multiplication, and i32 accumulation to avoid overflows entirely. The same applies to processing f16 values with f32 precision.

Installation

Use the following snippet to install SimSIMD and list available hardware acceleration options available on your machine:

pip install simsimd
python -c "import simsimd; print(simsimd.get_capabilities())"

One-to-One Distance

import simsimd
import numpy as np

vec1 = np.random.randn(1536).astype(np.float32)
vec2 = np.random.randn(1536).astype(np.float32)
dist = simsimd.cosine(vec1, vec2)

Supported functions include cosine, inner, sqeuclidean, hamming, and jaccard. Dot products are supported for both real and complex numbers:

vec1 = np.random.randn(768).astype(np.float64) + 1j * np.random.randn(768).astype(np.float64)
vec2 = np.random.randn(768).astype(np.float64) + 1j * np.random.randn(768).astype(np.float64)

dist = simsimd.dot(vec1.astype(np.complex128), vec2.astype(np.complex128))
dist = simsimd.dot(vec1.astype(np.complex64), vec2.astype(np.complex64))
dist = simsimd.vdot(vec1.astype(np.complex64), vec2.astype(np.complex64)) # conjugate, same as `np.vdot`

Unlike SciPy, SimSIMD allows explicitly stating the precision of the input vectors, which is especially useful for mixed-precision setups.

dist = simsimd.cosine(vec1, vec2, "i8")
dist = simsimd.cosine(vec1, vec2, "f16")
dist = simsimd.cosine(vec1, vec2, "f32")
dist = simsimd.cosine(vec1, vec2, "f64")

It also allows using SimSIMD for half-precision complex numbers, which NumPy does not support. For that, view data as continuous even-length np.float16 vectors and override type-resolution with complex32 string.

vec1 = np.random.randn(1536).astype(np.float16)
vec2 = np.random.randn(1536).astype(np.float16)
simd.dot(vec1, vec2, "complex32")
simd.vdot(vec1, vec2, "complex32")

One-to-Many Distances

Every distance function can be used not only for one-to-one but also one-to-many and many-to-many distance calculations. For one-to-many:

vec1 = np.random.randn(1536).astype(np.float32) # rank 1 tensor
batch1 = np.random.randn(1, 1536).astype(np.float32) # rank 2 tensor
batch2 = np.random.randn(100, 1536).astype(np.float32)

dist_rank1 = simsimd.cosine(vec1, batch2)
dist_rank2 = simsimd.cosine(batch1, batch2)

Many-to-Many Distances

All distance functions in SimSIMD can be used to compute many-to-many distances. For two batches of 100 vectors to compute 100 distances, one would call it like this:

batch1 = np.random.randn(100, 1536).astype(np.float32)
batch2 = np.random.randn(100, 1536).astype(np.float32)
dist = simsimd.cosine(batch1, batch2)

Input matrices must have identical shapes. This functionality isn't natively present in NumPy or SciPy, and generally requires creating intermediate arrays, which is inefficient and memory-consuming.

Many-to-Many All-Pairs Distances

One can use SimSIMD to compute distances between all possible pairs of rows across two matrices (akin to scipy.spatial.distance.cdist). The resulting object will have a type DistancesTensor, zero-copy compatible with NumPy and other libraries. For two arrays of 10 and 1,000 entries, the resulting tensor will have 10,000 cells:

import numpy as np
from simsimd import cdist, DistancesTensor

matrix1 = np.random.randn(1000, 1536).astype(np.float32)
matrix2 = np.random.randn(10, 1536).astype(np.float32)
distances: DistancesTensor = simsimd.cdist(matrix1, matrix2, metric="cosine") # zero-copy
distances_array: np.ndarray = np.array(distances, copy=True) # now managed by NumPy

Multithreading

By default, computations use a single CPU core. To optimize and utilize all CPU cores on Linux systems, add the threads=0 argument. Alternatively, specify a custom number of threads:

distances = simsimd.cdist(matrix1, matrix2, metric="cosine", threads=0)

Using Python API with USearch

Want to use it in Python with USearch? You can wrap the raw C function pointers SimSIMD backends into a CompiledMetric and pass it to USearch, similar to how it handles Numba's JIT-compiled code.

from usearch.index import Index, CompiledMetric, MetricKind, MetricSignature
from simsimd import pointer_to_sqeuclidean, pointer_to_cosine, pointer_to_inner

metric = CompiledMetric(
    pointer=pointer_to_cosine("f16"),
    kind=MetricKind.Cos,
    signature=MetricSignature.ArrayArraySize,
)

index = Index(256, metric=metric)

Using SimSIMD in Rust

To install, add the following to your Cargo.toml:

[dependencies]
simsimd = "..."

Before using the SimSIMD library, ensure you have imported the necessary traits and types into your Rust source file. The library provides several traits for different distance/similarity kinds - SpatialSimilarity, BinarySimilarity, and ProbabilitySimilarity.

Spatial Similarity: Cosine and Euclidean Distances

use simsimd::SpatialSimilarity;

fn main() {
    let vector_a: Vec<f32> = vec![1.0, 2.0, 3.0];
    let vector_b: Vec<f32> = vec![4.0, 5.0, 6.0];

    // Compute the cosine similarity between vector_a and vector_b
    let cosine_similarity = f32::cosine(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Cosine Similarity: {}", cosine_similarity);

    // Compute the squared Euclidean distance between vector_a and vector_b
    let sq_euclidean_distance = f32::sqeuclidean(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Squared Euclidean Distance: {}", sq_euclidean_distance);
}

Spatial similarity functions are available for f64, f32, f16, and i8 types.

Dot-Products: Inner and Complex Inner Products

use simsimd::SpatialSimilarity;
use simsimd::ComplexProducts;

fn main() {
    let vector_a: Vec<f32> = vec![1.0, 2.0, 3.0, 4.0];
    let vector_b: Vec<f32> = vec![5.0, 6.0, 7.0, 8.0];

    // Compute the inner product between vector_a and vector_b
    let inner_product = SpatialSimilarity::dot(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Inner Product: {}", inner_product);

    // Compute the complex inner product between complex_vector_a and complex_vector_b
    let complex_inner_product = ComplexProducts::dot(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    let complex_conjugate_inner_product = ComplexProducts::vdot(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Complex Inner Product: {:?}", complex_inner_product); // -18, 69
    println!("Complex C. Inner Product: {:?}", complex_conjugate_inner_product); // 70, -8
}

Complex inner products are available for f64, f32, and f16 types.

Probability Distributions: Jensen-Shannon and Kullback-Leibler Divergences

use simsimd::SpatialSimilarity;

fn main() {
    let vector_a: Vec<f32> = vec![1.0, 2.0, 3.0];
    let vector_b: Vec<f32> = vec![4.0, 5.0, 6.0];

    let cosine_similarity = f32::jensenshannon(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Cosine Similarity: {}", cosine_similarity);

    let sq_euclidean_distance = f32::kullbackleibler(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Squared Euclidean Distance: {}", sq_euclidean_distance);
}

Probability similarity functions are available for f64, f32, and f16 types.

Binary Similarity: Hamming and Jaccard Distances

Similar to spatial distances, one can compute bit-level distance functions between slices of unsigned integers:

use simsimd::BinarySimilarity;

fn main() {
    let vector_a = &[0b11110000, 0b00001111, 0b10101010];
    let vector_b = &[0b11110000, 0b00001111, 0b01010101];

    // Compute the Hamming distance between vector_a and vector_b
    let hamming_distance = u8::hamming(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Hamming Distance: {}", hamming_distance);

    // Compute the Jaccard distance between vector_a and vector_b
    let jaccard_distance = u8::jaccard(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Jaccard Distance: {}", jaccard_distance);
}

Binary similarity functions are available only for u8 types.

Half-Precision Floating-Point Numbers

Rust has no native support for half-precision floating-point numbers, but SimSIMD provides a f16 type. It has no functionality - it is a transparent wrapper around u16 and can be used with half or any other half-precision library.

use simsimd::SpatialSimilarity;
use simsimd::f16 as SimF16;
use half::f16 as HalfF16;

fn main() {
    let vector_a: Vec<HalfF16> = ...
    let vector_b: Vec<HalfF16> = ...

    let buffer_a: &[SimF16] = unsafe { std::slice::from_raw_parts(a_half.as_ptr() as *const SimF16, a_half.len()) };
    let buffer_b: &[SimF16] = unsafe { std::slice::from_raw_parts(b_half.as_ptr() as *const SimF16, b_half.len()) };

    // Compute the cosine similarity between vector_a and vector_b
    let cosine_similarity = SimF16::cosine(&vector_a, &vector_b)
        .expect("Vectors must be of the same length");

    println!("Cosine Similarity: {}", cosine_similarity);
}

Dynamic Dispatch

SimSIMD provides a dynamic dispatch mechanism to select the most advanced micro-kernel for the current CPU. You can query supported backends and use the SimSIMD::capabilities function to select the best one.

println!("uses neon: {}", capabilties::uses_neon());
println!("uses sve: {}", capabilties::uses_sve());
println!("uses haswell: {}", capabilties::uses_haswell());
println!("uses skylake: {}", capabilties::uses_skylake());
println!("uses ice: {}", capabilties::uses_ice());
println!("uses sapphire: {}", capabilties::uses_sapphire());

Using SimSIMD in JavaScript

To install, choose one of the following options depending on your environment:

npm install --save simsimd
yarn add simsimd
pnpm add simsimd
bun install simsimd

The package is distributed with prebuilt binaries, but if your platform is not supported, you can build the package from the source via npm run build. This will automatically happen unless you install the package with the --ignore-scripts flag or use Bun. After you install it, you will be able to call the SimSIMD functions on various TypedArray variants:

const { sqeuclidean, cosine, inner, hamming, jaccard } = require('simsimd');

const vectorA = new Float32Array([1.0, 2.0, 3.0]);
const vectorB = new Float32Array([4.0, 5.0, 6.0]);

const distance = sqeuclidean(vectorA, vectorB);
console.log('Squared Euclidean Distance:', distance);

Other numeric types and precision levels are supported as well. For double-precsion floating-point numbers, use Float64Array:

const vectorA = new Float64Array([1.0, 2.0, 3.0]);
const vectorB = new Float64Array([4.0, 5.0, 6.0]);
const distance = cosine(vectorA, vectorB);

When doing machine learning and vector search with high-dimensional vectors you may want to quantize them to 8-bit integers. You may want to project values from the $[-1, 1]$ range to the $[-100, 100]$ range and then cast them to Uint8Array:

const quantizedVectorA = new Uint8Array(vectorA.map(v => (v * 100)));
const quantizedVectorB = new Uint8Array(vectorB.map(v => (v * 100)));
const distance = cosine(quantizedVectorA, quantizedVectorB);

A more extreme quantization case would be to use binary vectors. You can map all positive values to 1 and all negative values and zero to 0, packing eight values into a single byte. After that, Hamming and Jaccard distances can be computed.

const { toBinary, hamming } = require('simsimd');

const binaryVectorA = toBinary(vectorA);
const binaryVectorB = toBinary(vectorB);
const distance = hamming(binaryVectorA, binaryVectorB);

Using SimSIMD in C

For integration within a CMake-based project, add the following segment to your CMakeLists.txt:

FetchContent_Declare(
    simsimd
    GIT_REPOSITORY https://github.com/ashvardanian/simsimd.git
    GIT_SHALLOW TRUE
)
FetchContent_MakeAvailable(simsimd)

After that, you can use the SimSIMD library in your C code in several ways. Simplest of all, you can include the headers, and the compiler will automatically select the most recent CPU extensions that SimSIMD will use.

#include <simsimd/simsimd.h>

int main() {
    simsimd_f32_t vector_a[1536];
    simsimd_f32_t vector_b[1536];
    simsimd_metric_punned_t distance_function = simsimd_metric_punned(
        simsimd_metric_cos_k, // Metric kind, like the angular cosine distance
        simsimd_datatype_f32_k, // Data type, like: f16, f32, f64, i8, b8, and complex variants
        simsimd_cap_any_k); // Which CPU capabilities are we allowed to use
    simsimd_distance_t distance;
    distance_function(vector_a, vector_b, 1536, &distance);
    return 0;
}

Dynamic Dispatch

To avoid hard-coding the backend, you can rely on c/lib.c to prepackage all possible backends in one binary, and select the most recent CPU features at runtime. That feature of the C library is called dynamic dispatch and is extensively used in the Python, JavaScript, and Rust bindings. To test which CPU features are available on the machine at runtime, use the following APIs:

int uses_neon = simsimd_uses_neon();
int uses_sve = simsimd_uses_sve();
int uses_haswell = simsimd_uses_haswell();
int uses_skylake = simsimd_uses_skylake();
int uses_ice = simsimd_uses_ice();
int uses_sapphire = simsimd_uses_sapphire();

simsimd_capability_t capabilities = simsimd_capabilities();

To differentiate between runtime and compile-time dispatch, define the following macro:

#define SIMSIMD_DYNAMIC_DISPATCH 1 // or 0

Spatial Distances: Cosine and Euclidean Distances

#include <simsimd/simsimd.h>

int main() {
    simsimd_f64_t f64s[1536];
    simsimd_f32_t f32s[1536];
    simsimd_f16_t f16s[1536];
    simsimd_i8_t i8[1536];
    simsimd_distance_t distance;

    // Cosine distance between two vectors
    simsimd_cos_i8(i8s, i8s, 1536, &distance);
    simsimd_cos_f16(f16s, f16s, 1536, &distance);
    simsimd_cos_f32(f32s, f32s, 1536, &distance);
    simsimd_cos_f64(f64s, f64s, 1536, &distance);
    
    // Euclidean distance between two vectors
    simsimd_l2sq_i8(i8s, i8s, 1536, &distance);
    simsimd_l2sq_f16(f16s, f16s, 1536, &distance);
    simsimd_l2sq_f32(f32s, f32s, 1536, &distance);
    simsimd_l2sq_f64(f64s, f64s, 1536, &distance);

    return 0;
}

Dot-Products: Inner and Complex Inner Products

#include <simsimd/simsimd.h>

int main() {
    simsimd_f64_t f64s[1536];
    simsimd_f32_t f32s[1536];
    simsimd_f16_t f16s[1536];
    simsimd_distance_t distance;

    // Inner product between two vectors
    simsimd_dot_f16(f16s, f16s, 1536, &distance);
    simsimd_dot_f32(f32s, f32s, 1536, &distance);
    simsimd_dot_f64(f64s, f64s, 1536, &distance);

    // Complex inner product between two vectors
    simsimd_dot_f16c(f16s, f16s, 1536, &distance);
    simsimd_dot_f32c(f32s, f32s, 1536, &distance);
    simsimd_dot_f64c(f64s, f64s, 1536, &distance);

    // Complex conjugate inner product between two vectors
    simsimd_vdot_f16c(f16s, f16s, 1536, &distance);
    simsimd_vdot_f32c(f32s, f32s, 1536, &distance);
    simsimd_vdot_f64c(f64s, f64s, 1536, &distance);

    return 0;
}

Binary Distances: Hamming and Jaccard Distances

#include <simsimd/simsimd.h>

int main() {
    simsimd_b8_t b8s[1536 / 8]; // 8 bits per word
    simsimd_distance_t distance;

    // Hamming distance between two vectors
    simsimd_hamming_b8(b8s, b8s, 1536 / 8, &distance);

    // Jaccard distance between two vectors
    simsimd_jaccard_b8(b8s, b8s, 1536 / 8, &distance);

    return 0;
}

Probability Distributions: Jensen-Shannon and Kullback-Leibler Divergences

#include <simsimd/simsimd.h>

int main() {
    simsimd_f64_t f64s[1536];
    simsimd_f32_t f32s[1536];
    simsimd_f16_t f16s[1536];
    simsimd_distance_t distance;

    // Jensen-Shannon divergence between two vectors
    simsimd_js_f16(f16s, f16s, 1536, &distance);
    simsimd_js_f32(f32s, f32s, 1536, &distance);
    simsimd_js_f64(f64s, f64s, 1536, &distance);

    // Kullback-Leibler divergence between two vectors
    simsimd_kl_f16(f16s, f16s, 1536, &distance);
    simsimd_kl_f32(f32s, f32s, 1536, &distance);
    simsimd_kl_f64(f64s, f64s, 1536, &distance);

    return 0;
}

Half-Precision Floating-Point Numbers

If you aim to utilize the _Float16 functionality with SimSIMD, ensure your development environment is compatible with C 11. For other SimSIMD functionalities, C 99 compatibility will suffice. To explicitly disable half-precision support, define the following macro before imports:

#define SIMSIMD_NATIVE_F16 0 // or 1
#include <simsimd/simsimd.h>

Target Specific Backends

SimSIMD exposes all kernels for all backends, and you can select the most advanced one for the current CPU without relying on built-in dispatch mechanisms. All of the function names follow the same pattern: simsimd_{function}_{type}_{backend}.

The backend can be serial, haswell, skylake, ice, sapphire, neon, or sve.
The type can be f64, f32, f16, f64c, f32c, f16c, i8, or b8.
The function can be dot, vdot, cos, l2sq, hamming, jaccard, kl, or js.

To avoid hard-coding the backend, you can use the simsimd_metric_punned_t to pun the function pointer and the simsimd_capabilities function to get the available backends at runtime.

simsimd_dot_f64_sve
simsimd_cos_f64_sve
simsimd_l2sq_f64_sve
simsimd_dot_f64_skylake
simsimd_cos_f64_skylake
simsimd_l2sq_f64_skylake
simsimd_dot_f64_serial
simsimd_cos_f64_serial
simsimd_l2sq_f64_serial
simsimd_js_f64_serial
simsimd_kl_f64_serial
simsimd_dot_f32_sve
simsimd_cos_f32_sve
simsimd_l2sq_f32_sve
simsimd_dot_f32_neon
simsimd_cos_f32_neon
simsimd_l2sq_f32_neon
simsimd_js_f32_neon
simsimd_kl_f32_neon
simsimd_dot_f32_skylake
simsimd_cos_f32_skylake
simsimd_l2sq_f32_skylake
simsimd_js_f32_skylake
simsimd_kl_f32_skylake
simsimd_dot_f32_serial
simsimd_cos_f32_serial
simsimd_l2sq_f32_serial
simsimd_js_f32_serial
simsimd_kl_f32_serial
simsimd_dot_f16_sve
simsimd_cos_f16_sve
simsimd_l2sq_f16_sve
simsimd_dot_f16_neon
simsimd_cos_f16_neon
simsimd_l2sq_f16_neon
simsimd_js_f16_neon
simsimd_kl_f16_neon
simsimd_dot_f16_sapphire
simsimd_cos_f16_sapphire
simsimd_l2sq_f16_sapphire
simsimd_js_f16_sapphire
simsimd_kl_f16_sapphire
simsimd_dot_f16_haswell
simsimd_cos_f16_haswell
simsimd_l2sq_f16_haswell
simsimd_js_f16_haswell
simsimd_kl_f16_haswell
simsimd_dot_f16_serial
simsimd_cos_f16_serial
simsimd_l2sq_f16_serial
simsimd_js_f16_serial
simsimd_kl_f16_serial
simsimd_cos_i8_neon
simsimd_cos_i8_neon
simsimd_l2sq_i8_neon
simsimd_cos_i8_ice
simsimd_cos_i8_ice
simsimd_l2sq_i8_ice
simsimd_cos_i8_haswell
simsimd_cos_i8_haswell
simsimd_l2sq_i8_haswell
simsimd_cos_i8_serial
simsimd_cos_i8_serial
simsimd_l2sq_i8_serial
simsimd_hamming_b8_sve
simsimd_jaccard_b8_sve
simsimd_hamming_b8_neon
simsimd_jaccard_b8_neon
simsimd_hamming_b8_ice
simsimd_jaccard_b8_ice
simsimd_hamming_b8_haswell
simsimd_jaccard_b8_haswell
simsimd_hamming_b8_serial
simsimd_jaccard_b8_serial
simsimd_dot_f32c_sve
simsimd_vdot_f32c_sve
simsimd_dot_f32c_neon
simsimd_vdot_f32c_neon
simsimd_dot_f32c_haswell
simsimd_vdot_f32c_haswell
simsimd_dot_f32c_skylake
simsimd_vdot_f32c_skylake
simsimd_dot_f32c_serial
simsimd_vdot_f32c_serial
simsimd_dot_f64c_sve
simsimd_vdot_f64c_sve
simsimd_dot_f64c_skylake
simsimd_vdot_f64c_skylake
simsimd_dot_f64c_serial
simsimd_vdot_f64c_serial
simsimd_dot_f16c_sve
simsimd_vdot_f16c_sve
simsimd_dot_f16c_neon
simsimd_vdot_f16c_neon
simsimd_dot_f16c_haswell
simsimd_vdot_f16c_haswell
simsimd_dot_f16c_sapphire
simsimd_vdot_f16c_sapphire
simsimd_dot_f16c_serial
simsimd_vdot_f16c_serial

Name		Name	Last commit message	Last commit date
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.github/workflows		.github/workflows
.vscode		.vscode
c		c
cpp		cpp
golang		golang
include		include
javascript		javascript
python		python
rust		rust
swift		swift
.clang-format		.clang-format
.gitignore		.gitignore
.npmignore		.npmignore
.releaserc		.releaserc
CMakeLists.txt		CMakeLists.txt
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
Package.swift		Package.swift
README.md		README.md
VERSION		VERSION
binding.gyp		binding.gyp
build.rs		build.rs
go.mod		go.mod
package-ci.json		package-ci.json
package-lock.json		package-lock.json
package.json		package.json
pyproject.toml		pyproject.toml
setup.py		setup.py

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SimSIMD 📏

Features

Benchmarks

Against NumPy and SciPy

Against GCC Auto-Vectorization

Using SimSIMD in Python

Installation

One-to-One Distance

One-to-Many Distances

Many-to-Many Distances

Many-to-Many All-Pairs Distances

Multithreading

Using Python API with USearch

Using SimSIMD in Rust

Spatial Similarity: Cosine and Euclidean Distances

Dot-Products: Inner and Complex Inner Products

Probability Distributions: Jensen-Shannon and Kullback-Leibler Divergences

Binary Similarity: Hamming and Jaccard Distances

Half-Precision Floating-Point Numbers

Dynamic Dispatch

Using SimSIMD in JavaScript

Using SimSIMD in C

Dynamic Dispatch

Spatial Distances: Cosine and Euclidean Distances

Dot-Products: Inner and Complex Inner Products

Binary Distances: Hamming and Jaccard Distances

Probability Distributions: Jensen-Shannon and Kullback-Leibler Divergences

Half-Precision Floating-Point Numbers

Target Specific Backends

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SoftwareApplicationsInc/SimSIMD

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SimSIMD 📏

Features

Benchmarks

Against NumPy and SciPy

Against GCC Auto-Vectorization

Using SimSIMD in Python

Installation

One-to-One Distance

One-to-Many Distances

Many-to-Many Distances

Many-to-Many All-Pairs Distances

Multithreading

Using Python API with USearch

Using SimSIMD in Rust

Spatial Similarity: Cosine and Euclidean Distances

Dot-Products: Inner and Complex Inner Products

Probability Distributions: Jensen-Shannon and Kullback-Leibler Divergences

Binary Similarity: Hamming and Jaccard Distances

Half-Precision Floating-Point Numbers

Dynamic Dispatch

Using SimSIMD in JavaScript

Using SimSIMD in C

Dynamic Dispatch

Spatial Distances: Cosine and Euclidean Distances

Dot-Products: Inner and Complex Inner Products

Binary Distances: Hamming and Jaccard Distances

Probability Distributions: Jensen-Shannon and Kullback-Leibler Divergences

Half-Precision Floating-Point Numbers

Target Specific Backends

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