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https://github.com/samber/lo.git
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Samuel Berthe
035f1b358a
* feat(exp,simd): adding SumAxB helpers * feat(exp,simd): adding MeanAxB and ClampAxB helpers * feat(exp,simd): adding MinAxB and MaxAxB helpers * refactor(exp,simd): group perf helper category + architecture * feat(exp,simd): adding ContainsAxB helpers * perf(exp,simd): cast to unsafe slice once * feat(exp,simd): call the right SIMD helper based on local architecture * chore: internal dependency linking * Update exp/simd/math.go Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * style: fix linter * style: fix linter * chore: enable simd in makefile * chore(ci): add simd package to test runs * chore(ci): add simd package to test runs only for go 1.26 * fix(simd): fix overflow * fix(simd): fix overflow and apply the same behavior than lo.Mean * doc(exp,simd): adding initial doc * refactor(simd): move intersect_avx2 and intersect_sse code into intersect_avx512 * fix(simd): call SSE fallback instead of lo.Sum for default helpers * feat(simd): cache simd features on package init to avoid repeated checks * perf(exp,simd): precompute length + improve code quality * perf(exp,simd): faster iteration for min/max value * test(exp,simd): adding benchmarks * test(exp,simd): adding benchmarks results * test(exp,simd): adding benchmarks results * doc(exp,simd): adding warning for overflows in SIMD operations * feat(exp,simd): adding more dispatch helpers * feat(exp,simd): adding SumBy variants * feat(exp,simd): adding MeanBy variants * fix(exp,simd): faster clamp * 💄 * doc(exp,simd): adding SumBy + MeanBy * fix(exp,simd): faster SIMD operations * chore(ci): enable the benchmarks temporary * chore(ci): display cpu architecture before running tests * chore(ci): github actions are hidding some useful stuffs * chore(ci): no SIMD VM available at Github during the weekend ??? * test(exp,simd): larger epsilon * oops * perf(exp,simd): faster iterations * doc(exp,simd): report last version of benchmarks * 💄 --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
5.6 KiB
5.6 KiB
name, slug, sourceRef, category, subCategory, similarHelpers, position, signatures
| name | slug | sourceRef | category | subCategory | similarHelpers | position | signatures | ||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SumBy | sumby | exp/simd/math.go#L841 | exp | simd |
|
20 |
|
SumBy transforms a collection using an iteratee function and sums the result using SIMD instructions. The automatic dispatch functions (e.g., SumByInt8) will select the best SIMD variant based on CPU capabilities. The specific variants (e.g., SumByInt8x32) use a fixed SIMD instruction set regardless of CPU capabilities.
Requirements
- Go 1.26+ with
GOEXPERIMENT=simd - amd64 architecture only
CPU compatibility
| SIMD variant | Lanes | Required flags | Typical CPUs |
|---|---|---|---|
| SSE (xN) | 2-16 | sse2 |
All amd64 |
| AVX2 (xN) | 4-32 | avx2 |
Intel Haswell+, AMD Excavator+ |
| AVX-512 (xN) | 8-64 | avx512f |
Intel Skylake-X+, some Xeons |
Note
: The automatic dispatch functions (e.g.,
SumByInt8) will use the best available SIMD variant for the current CPU. Use specific variants (e.g.,SumByInt8x32) only if you know your target CPU supports that instruction set.
type Person struct {
Name string
Age int8
}
people := []Person{
{Name: "Alice", Age: 25},
{Name: "Bob", Age: 30},
{Name: "Charlie", Age: 35},
}
// Automatic dispatch - uses best available SIMD
sum := simd.SumByInt8(people, func(p Person) int8 {
return p.Age
})
// 90
type Product struct {
Name string
Price float32
Stock int32
}
products := []Product{
{Name: "Widget", Price: 10.50, Stock: 5},
{Name: "Gadget", Price: 20.00, Stock: 3},
{Name: "Tool", Price: 15.75, Stock: 2},
}
// Sum stock value using specific AVX2 variant
sum := simd.SumByFloat32x8(products, func(p Product) float32 {
return p.Price * float32(p.Stock)
})
// 152.5
type Metric struct {
Value uint16
}
metrics := []Metric{
{Value: 100},
{Value: 200},
{Value: 300},
{Value: 400},
}
// Using SSE variant - works on all amd64
sum := simd.SumByUint16x8(metrics, func(m Metric) uint16 {
return m.Value
})
// 1000
// Empty collection returns 0
type Item struct {
Count int64
}
sum := simd.SumByInt64([]Item{}, func(i Item) int64 {
return i.Count
})
// 0