mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-28 01:37:43 +00:00
Miha Kralj
65 lines
2.6 KiB
Markdown
65 lines
2.6 KiB
Markdown
# SimdExtensions Class
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| Property | Value |
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| ---------------- | -------------------------------- |
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| **Category** | Core |
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| **Inputs** | Source (close) |
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| **Parameters** | None |
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| **Outputs** | Single series (SimdExtensions) |
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| **Output range** | Varies (see docs) |
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| **Warmup** | 1 bar |
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- `SimdExtensions` provides high-performance, SIMD-accelerated extension methods for `ReadOnlySpan<double>`.
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- No configurable parameters; computation is stateless per bar.
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- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
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`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.
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## Key Features
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* **Hardware Acceleration**: Uses CPU vector registers to process multiple elements in parallel.
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* **Automatic Fallback**: Gracefully handles non-SIMD hardware or small arrays.
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* **Zero-Allocation**: Operates directly on spans without creating new arrays.
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* **Aggressive Inlining**: Methods are marked for inlining to minimize call overhead.
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## Available Methods
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| Method | Description |
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| ------ | ------ |
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| `ContainsNonFinite()` | Checks if span contains any non-finite values (NaN or Infinity). |
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| `SumSIMD()` | Calculates the sum of elements. |
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| `MinSIMD()` | Finds the minimum value. |
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| `MaxSIMD()` | Finds the maximum value. |
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| `MinMaxSIMD()` | Finds both min and max in a single pass (more efficient than separate calls). |
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| `AverageSIMD()` | Calculates the arithmetic mean. |
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| `VarianceSIMD()` | Calculates the sample variance. |
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| `StdDevSIMD()` | Calculates the sample standard deviation. |
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| `DotProduct()` | Calculates the dot product of two spans. |
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## Performance
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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.
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## Usage
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```csharp
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using QuanTAlib;
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double[] data = { 1.0, 2.0, 3.0, 4.0, 5.0, ... };
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ReadOnlySpan<double> span = data;
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// Calculate sum
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double sum = span.SumSIMD();
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// Calculate min and max in one pass
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var (min, max) = span.MinMaxSIMD();
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// Calculate standard deviation
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double stdDev = span.StdDevSIMD();
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// Check for valid data
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bool hasInvalid = span.ContainsNonFinite();
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// Calculate dot product
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double dot = span.DotProduct(otherSpan);
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``` |