QuanTAlib/lib/core/simd/SimdExtensions.md

SimdExtensions Class

SimdExtensions provides high-performance, SIMD-accelerated extension methods for ReadOnlySpan<double>. It leverages .NET's Vector<T> to achieve 4-8x speedups on supported hardware (AVX2, AVX-512) while automatically falling back to scalar implementations on older hardware.

Key Features

• Hardware Acceleration: Uses CPU vector registers to process multiple elements in parallel.
• Automatic Fallback: Gracefully handles non-SIMD hardware or small arrays.
• Zero-Allocation: Operates directly on spans without creating new arrays.
• Aggressive Inlining: Methods are marked for inlining to minimize call overhead.

Available Methods

Method	Description
SumSIMD()	Calculates the sum of elements.
MinSIMD()	Finds the minimum value.
MaxSIMD()	Finds the maximum value.
MinMaxSIMD()	Finds both min and max in a single pass (more efficient than separate calls).
AverageSIMD()	Calculates the arithmetic mean.
VarianceSIMD()	Calculates the sample variance.
StdDevSIMD()	Calculates the sample standard deviation.

Performance

On modern CPUs (e.g., Intel Core i7/i9, AMD Ryzen), these methods typically outperform standard LINQ or scalar loops by a factor of 4 to 8 for large arrays.

Usage

using QuanTAlib;

double[] data = { 1.0, 2.0, 3.0, 4.0, 5.0, ... };
ReadOnlySpan<double> span = data;

// Calculate sum
double sum = span.SumSIMD();

// Calculate min and max in one pass
var (min, max) = span.MinMaxSIMD();

// Calculate standard deviation
double stdDev = span.StdDevSIMD();