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SIMD support via Go assembly for arithmetic, bitwise, maximum, and minimum operations. Allowing for parallel element-wise computations. Resulting in a 100% to 400% speedup. Currently AMD64 (x86_64) and ARM64 processors are supported.

Function Documentation

SIMD Support

AMD64 (x86_64) ARM64
AddFloat32 SSE / AVX / AVX512VL NEON
AddFloat64 SSE2 / AVX / AVX512VL NEON
AddInt32 SSE2 / AVX2 / AVX512VL NEON
AddInt64 SSE2 / AVX2 / AVX512VL NEON
AndInt32 SSE2 / AVX2 / AVX512VL NEON
AndInt64 SSE2 / AVX2 / AVX512VL NEON
DivFloat32 SSE / AVX / AVX512VL
DivFloat64 SSE2 / AVX / AVX512VL
DivInt32
DivInt64
MaxFloat32 SSE / AVX / AVX512VL
MaxFloat64 SSE2 / AVX / AVX512VL
MaxInt32 SSE4.1 / AVX2 / AVX512VL
MaxInt64 AVX512VL
MinFloat32 SSE / AVX / AVX512VL
MinFloat64 SSE2 / AVX / AVX512VL
MinInt32 SSE4.1 / AVX2 / AVX512VL
MinInt64 AVX512VL
MulFloat32 SSE / AVX / AVX512VL NEON
MulFloat64 SSE2 / AVX / AVX512VL NEON
MulInt32 SSE4.1 / AVX2 / AVX512VL NEON
MulInt64 AVX512VL
OrInt32 SSE2 / AVX2 / AVX512VL NEON
OrInt64 SSE2 / AVX2 / AVX512VL NEON
SubFloat32 SSE / AVX / AVX512VL NEON
SubFloat64 SSE2 / AVX / AVX512VL NEON
SubInt32 SSE2 / AVX2 / AVX512VL NEON
SubInt64 SSE2 / AVX2 / AVX512VL NEON
XorInt32 SSE2 / AVX2 / AVX512VL
XorInt64 SSE2 / AVX2 / AVX512VL

Make Targets

Tests

Command Description
make test Compiles and runs tests natively on hardware.
make test_amd64 Cross compiles for amd64 and runs tests via QEMU (qemu-x86_64).
make test_arm64 Cross compiles for arm64 and runs tests via QEMU (qemu-aarch64).

AMD64 Performance (AMD Ryzen 7 7840U / DDR5 SO-DIMM)

Elements Go ns/op SIMD ns/op Performance x
Small Vectors
100 38.33 7.580 5.056
200 79.59 12.80 6.217
300 117.0 18.45 9.593
400 154.5 16.20 9.537
500 191.5 20.38 9.396
600 228.6 26.37 8.668
700 265.6 33.70 7.881
800 303.1 29.38 10.31
900 340.3 33.54 10.14
Medium Vectors
1000 377.4 39.60 9.530
2000 751.2 69.45 10.81
3000 1153 148.3 7.774
4000 1499 325.1 4.610
5000 1871 431.6 4.335
6000 2243 523.6 4.283
7000 2614 614.1 4.256
8000 2987 701.6 4.257
9000 3360 792.5 4.239
Large Vectors
10000 3725 878.5 4.240
20000 7458 1754 4.251
30000 11187 2631 4.251
40000 14908 3509 4.248
50000 18677 4373 4.270
60000 22363 5276 4.238
70000 26107 6319 4.131
80000 29854 7820 3.817
90000 33613 9222 3.644

ARM64 Performance (Apple M1 Pro / LPDDR5 SDRAM)

Elements Go ns/op SIMD ns/op Performance x
Small Vectors
100 51.81 13.68 3.787
200 102.2 24.24 4.216
300 152.8 35.93 4.252
400 209.0 47.71 4.380
500 258.7 64.88 3.987
600 309.8 73.42 4.219
700 359.6 89.01 4.039
800 410.6 101.9 4.029
900 460.3 112.5 4.091
Medium Vectors
1000 511.5 124.3 4.115
2000 1015 241.0 4.211
3000 1520 356.9 4.258
4000 2024 473.1 4.278
5000 2527 589.9 4.283
6000 3032 706.1 4.294
7000 3535 822.5 4.297
8000 4039 939.2 4.300
9000 4543 1056 4.302
Large Vectors
10000 5046 1172 4.305
20000 10107 2394 4.221
30000 15139 3599 4.206
40000 20178 4957 4.070
50000 25218 6190 4.073
60000 30253 7277 4.157
70000 35285 8707 4.052
80000 40346 9924 4.065
90000 45378 11189 4.055

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Simd support via Go assembly for parallel element-wise computations.

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