mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-02 03:37:42 +00:00
Miha Kralj
74b49d2bb4
- Introduced TBar struct for efficient OHLCV data representation. - Implemented TBarSeries class for high-performance collection of TBar instances using Structure of Arrays (SoA) layout. - Added TSeries class for time-series data management with zero-copy access. - Created TValue struct for time-value pairs with implicit conversions. - Defined IFeed interface for consistent data feed implementations. - Developed CsvFeed class for loading historical OHLCV data from CSV files. - Implemented GBM class for generating synthetic financial data using Geometric Brownian Motion. - Added Quantower project files for Averages indicator with necessary dependencies and configurations. - Included extensive usage examples and notebooks for TBar, TBarSeries, TSeries, TValue, and feed implementations.
44 lines
1.6 KiB
Markdown
44 lines
1.6 KiB
Markdown
# SimdExtensions Class
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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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| `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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## 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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