lo/docs/data/simd-sum.md

name, slug, sourceRef, category, subCategory, similarHelpers, position, signatures

name	slug	sourceRef	category	subCategory	similarHelpers	position	signatures
Sum	sum	exp/simd/math_avx.go#L14	exp	simd	exp#simd#sum exp#simd#sumby	0	func SumInt8x16[T ~int8](collection []T) T func SumInt8x32[T ~int8](collection []T) T func SumInt8x64[T ~int8](collection []T) T func SumInt16x8[T ~int16](collection []T) T func SumInt16x16[T ~int16](collection []T) T func SumInt16x32[T ~int16](collection []T) T func SumInt32x4[T ~int32](collection []T) T func SumInt32x8[T ~int32](collection []T) T func SumInt32x16[T ~int32](collection []T) T func SumInt64x2[T ~int64](collection []T) T func SumInt64x4[T ~int64](collection []T) T func SumInt64x8[T ~int64](collection []T) T func SumUint8x16[T ~uint8](collection []T) T func SumUint8x32[T ~uint8](collection []T) T func SumUint8x64[T ~uint8](collection []T) T func SumUint16x8[T ~uint16](collection []T) T func SumUint16x16[T ~uint16](collection []T) T func SumUint16x32[T ~uint16](collection []T) T func SumUint32x4[T ~uint32](collection []T) T func SumUint32x8[T ~uint32](collection []T) T func SumUint32x16[T ~uint32](collection []T) T func SumUint64x2[T ~uint64](collection []T) T func SumUint64x4[T ~uint64](collection []T) T func SumUint64x8[T ~uint64](collection []T) T func SumFloat32x4[T ~float32](collection []T) T func SumFloat32x8[T ~float32](collection []T) T func SumFloat32x16[T ~float32](collection []T) T func SumFloat64x2[T ~float64](collection []T) T func SumFloat64x4[T ~float64](collection []T) T func SumFloat64x8[T ~float64](collection []T) T

Sums the values in a collection using SIMD instructions. The suffix (x2, x4, x8, x16, x32, x64) indicates the number of lanes processed simultaneously.

Requirements

• Go 1.26+ with GOEXPERIMENT=simd
• amd64 architecture only

CPU compatibility

SIMD variant	Lanes	Required flags	Typical CPUs
AVX (xN)	2-16	avx	All amd64
AVX2 (xN)	4-32	avx2	Intel Haswell+, AMD Excavator+
AVX-512 (xN)	8-64	avx512f	Intel Skylake-X+, some Xeons

Note

: Choose the variant matching your CPU's capabilities. Higher lane counts provide better performance but require newer CPU support.

// Using AVX2 variant (32 lanes at once) - Intel Haswell+ / AMD Excavator+
sum := simd.SumInt8x32([]int8{1, 2, 3, 4, 5})
// 15

// Using AVX-512 variant (16 lanes at once) - Intel Skylake-X+
sum := simd.SumFloat32x16([]float32{1.1, 2.2, 3.3, 4.4})
// 11

// Using AVX variant (4 lanes at once) - works on all amd64
sum := simd.SumInt32x4([]int32{1000000, 2000000, 3000000})
// 6000000

// Empty collection returns 0
sum := simd.SumUint16x16([]uint16{})
// 0