# README
Basic Linear Algebra Operations
Introduction
The following operations are implemented using vectors as operands:
float32:
add, sub, mul, div
int32
add, sub, mul,
hfloat32/hint32
The "horizontal"(across a single vector) operations below are available for both int32 and float32:
hsum, hmin, hmax
float64
add, sub, mul, div
####int64 amd64: add, sub, mul
arm64: add, sub only. mul falls back to scalar
####hfloat64 hsum, hmin, hmax
####hint64 amd64: hsum requires AVX. hmin and hmax require AVX512, otherwise fall back to scalar ops.
arm64: hsum. hmin and hmax fall back to scala ops due to unavailability of vector ops on neon.
Usage:
Vectorized Add
package mypkg;
import "github.com/viant/vec/blas"
func ExampleInt32_Add() {
v1 := []int32{1, 2, 3, 4, 5, 6, 7, 8}
v2 := []int32{1, 7, 3, 4, 3, 6, 7, 2}
out := blas.Int32s(make([]int32, 8))
out.AddInt32(v1, v2)
}
Vectorized Horizontal Sum
package mypkg;
import (
"fmt"
"github.com/viant/vec/blas"
)
func ExampleInt32_HSum() {
var data = make([]float32, 1000)
//...
sum := blas.HsumFloat32(data)
fmt.Println(sum)
}
Benchmarks
ARM64 (Neon)
goarch: arm64
pkg: github.com/viant/vec/blas
BenchmarkAddFloat32Naive-16 8077633 148.5 ns/op
BenchmarkAddFloat32-16 40494718 29.66 ns/op
BenchmarkSubFloat32Naive-16 8079802 148.5 ns/op
BenchmarkSubFloat32-16 40544472 29.56 ns/op
BenchmarkMulFloat32Naive-16 8080488 148.5 ns/op
BenchmarkMulFloat32-16 37863837 31.64 ns/op
BenchmarkDivFloat32Naive-16 7515062 159.8 ns/op
BenchmarkDivFloat32-16 15938955 75.28 ns/op
BenchmarkHsumFloat32Naive-16 7127556 168.4 ns/op
BenchmarkHsumFloat32-16 24377919 49.83 ns/op
BenchmarkHmaxFloat32Naive-16 11152754 107.6 ns/op
BenchmarkHmaxFloat32-16 18596677 63.38 ns/op
BenchmarkHminFloat32Naive-16 3211771 373.6 ns/op
BenchmarkHminFloat32-16 17684204 66.94 ns/op
BenchmarkHsumInt32Naive-16 10272612 116.8 ns/op
BenchmarkHsumInt32-16 24530227 49.38 ns/op
BenchmarkHmaxInt32Naive-16 5949591 201.7 ns/op
BenchmarkHmaxInt32-16 18702337 63.25 ns/op
BenchmarkHminInt32Naive-16 5949391 201.7 ns/op
BenchmarkHminInt32-16 15323509 77.99 ns/op
BenchmarkAddInt32Naive-16 6029112 199.1 ns/op
BenchmarkAddInt32-16 39999256 30.03 ns/op
BenchmarkSubInt32Naive-16 6029341 199.1 ns/op
BenchmarkSubInt32-16 39963598 29.96 ns/op
BenchmarkMulInt32Naive-16 6028239 199.1 ns/op
BenchmarkMulInt32-16 38536497 31.13 ns/op
AMD64 (AVX2)
** MacBook2.4 GHz 8-Core Intel Core i9**
cpu: Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
BenchmarkAddFloat32Naive-16 9386047 125.3 ns/op
BenchmarkAddFloat32-16 49576354 23.45 ns/op
BenchmarkSubFloat32Naive-16 9222256 126.7 ns/op
BenchmarkSubFloat32-16 76574758 15.81 ns/op
BenchmarkMulFloat32Naive-16 9295201 130.4 ns/op
BenchmarkMulFloat32-16 54382240 18.65 ns/op
BenchmarkDivFloat32Naive-16 6605150 172.0 ns/op
BenchmarkDivFloat32-16 37387005 30.13 ns/op
BenchmarkHsumFloat32Naive-16 6167763 196.0 ns/op
BenchmarkHsumFloat32-16 31841235 36.94 ns/op
BenchmarkHmaxFloat32Naive-16 8876424 127.9 ns/op
BenchmarkHmaxFloat32-16 27600762 42.20 ns/op
BenchmarkHminFloat32Naive-16 8366142 136.5 ns/op
BenchmarkHminFloat32-16 35269717 36.81 ns/op
BenchmarkHsumInt32Naive-16 13112025 94.23 ns/op
BenchmarkHsumInt32-16 41688908 28.40 ns/op
BenchmarkHmaxInt32Naive-16 9809748 117.6 ns/op
BenchmarkHmaxInt32-16 20696240 55.76 ns/op
BenchmarkHminInt32Naive-16 9347374 134.8 ns/op
BenchmarkHminInt32-16 24188605 50.46 ns/op
BenchmarkAddInt32Naive-16 8154877 132.2 ns/op
BenchmarkAddInt32-16 44763596 25.55 ns/op
BenchmarkSubInt32Naive-16 9035091 131.1 ns/op
BenchmarkSubInt32-16 71036890 16.09 ns/op
BenchmarkMulInt32Naive-16 8255730 138.3 ns/op
BenchmarkMulInt32-16 66136818 18.92 ns/op