SIMD-enhanced array operations for F# that leverage CPU vector instructions for significant performance improvements.
SIMDArray is an F# library that provides SIMD-accelerated array operations, leveraging CPU vector instructions to process multiple data points simultaneously. It solves the performance bottleneck of sequential array processing in F# by offering drop-in replacements for standard array functions that are 3-10x faster for operations like sum, map, and max. The library integrates with .NET's SIMD support to deliver hardware-accelerated computations while maintaining F#'s type safety.
F# developers working on performance-sensitive numerical computing, data processing, or scientific applications where array operations are a bottleneck. It's particularly valuable for those processing large arrays in financial modeling, simulation, or data analysis pipelines.
Developers choose SIMDArray because it provides significant performance gains over standard F# array operations and managed alternatives like Math.NET Numerics, without requiring low-level code or leaving the F# ecosystem. Its unique selling point is the combination of SIMD acceleration, parallel execution options, and a familiar F# API that requires minimal code changes.
SIMD enhanced Array operations
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Leverages .NET's Vector<T> and Intrinsics to map operations to CPU vector instructions (SSE, AVX, NEON), delivering 3-10x speedups for common ops like sum and max, as shown in benchmarks.
Includes SIMDParallel module for multi-core scaling and low-overhead stride-based loops via Parallel.ForStride extensions, enabling efficient custom parallel implementations with reduced overhead.
Offers functions like distinctUnordered and filterLessThan in the Performance module, which relax ordering or add predicate constraints for faster execution and lower memory usage.
Works on x86/x64 and ARM architectures with .NET handling platform-specific vectorization, ensuring broad usability without manual adjustments for different CPUs.
Functions like map require separate lambdas for vector and leftover elements, adding boilerplate and cognitive load compared to standard F# array APIs, which can lead to errors if not handled carefully.
SIMD operations are not associative, causing floating-point results to differ from sequential computations—this can break applications that rely on exact reproducibility or bitwise identical outputs.
Speedups are not guaranteed and vary significantly based on CPU architecture, vector width, and array size; the README notes that small arrays may even be slower due to SIMD setup overhead.
Focuses solely on array operations without support for other F# collections or advanced mathematical functions, making it less suitable for comprehensive numerical computing compared to libraries like Math.NET.