As was discussed in the SciPy integration thread, Python libraries use double-precision floating-point numbers by default. So in this release I've extended the spatial distance functions - cosine, sqeuclidean, inner with support for double arguments with specialized implementations on AVX-512-capable x86 CPUs and SVE-capable Arm CPUs.
Benchmarking SimSIMD vs. SciPy on Intel Sapphire Rapids CPU
- Vector dimensions: 1536
- Vectors count: 1000
- Hardware capabilities:
serial, x86_avx2, x86_avx512, x86_avx2fp16, x86_avx512fp16, x86_avx512vpopcntdq, x86_avx512vnni
- NumPy BLAS dependency:
openblas64
- NumPy LAPACK dependency:
dep140640983012528
Between 2 Vectors, Batch Size: 1
| Datatype |
Method |
Ops/s |
SimSIMD Ops/s |
SimSIMD Improvement |
f64 |
scipy.cosine |
63,612 |
572,605 |
9.00 x |
f64 |
scipy.sqeuclidean |
238,547 |
915,596 |
3.84 x |
f64 |
numpy.inner |
449,499 |
986,522 |
2.19 x |
Between 2 Vectors, Batch Size: 1,000
| Datatype |
Method |
Ops/s |
SimSIMD Ops/s |
SimSIMD Improvement |
f64 |
scipy.cosine |
68,962 |
1,457,172 |
21.13 x |
f64 |
scipy.sqeuclidean |
247,727 |
1,535,547 |
6.20 x |
f64 |
numpy.inner |
463,509 |
1,512,004 |
3.26 x |
Benchmarking SimSIMD vs. SciPy on AWS Graviton 3
- Vector dimensions: 1536
- Vectors count: 1000
- Hardware capabilities:
serial, arm_neon, arm_sve
- NumPy BLAS dependency:
openblas64
- NumPy LAPACK dependency:
openblas64
Between 2 Vectors, Batch Size: 1
| Datatype |
Method |
Ops/s |
SimSIMD Ops/s |
SimSIMD Improvement |
f64 |
scipy.cosine |
40,729 |
725,382 |
17.81 x |
f64 |
scipy.sqeuclidean |
160,812 |
728,114 |
4.53 x |
f64 |
numpy.inner |
473,443 |
767,374 |
1.62 x |
f64 |
scipy.jensenshannon |
15,684 |
38,528 |
2.46 x |
f64 |
scipy.kl_div |
49,983 |
61,811 |
1.24 x |
Between 2 Vectors, Batch Size: 1,000
| Datatype |
Method |
Ops/s |
SimSIMD Ops/s |
SimSIMD Improvement |
f64 |
scipy.cosine |
41,130 |
1,460,850 |
35.52 x |
f64 |
scipy.sqeuclidean |
162,147 |
1,486,255 |
9.17 x |
f64 |
numpy.inner |
473,856 |
1,580,136 |
3.33 x |
