AiDotNet.Tensors 0.9.3

AiDotNet.Tensors

The fastest .NET tensor library. Beats MathNet, NumSharp, TensorPrimitives, and matches TorchSharp CPU on pure managed code with hand-tuned AVX2/FMA SIMD kernels and JIT-compiled machine code.

Features

• Zero Allocations: In-place operations with ArrayPool<T> and Span<T> for hot paths
• Hand-Tuned SIMD: Custom AVX2/FMA kernels with 4x loop unrolling, not just Vector<T> wrappers
• JIT-Compiled Kernels: Runtime x86-64 machine code generation for size-specialized operations
• BLIS-Style GEMM: Tiled matrix multiply with FMA micro-kernel, cache-aware panel packing
• GPU Acceleration: Optional CUDA, HIP/ROCm, and OpenCL support via separate packages
• Multi-Target: Supports .NET 10.0 and .NET Framework 4.7.1
• Generic Math: Works with any numeric type via INumericOperations<T> interface

Installation

# Core package (CPU SIMD acceleration)
dotnet add package AiDotNet.Tensors

# Optional: OpenBLAS for optimized CPU BLAS operations
dotnet add package AiDotNet.Native.OpenBLAS

# Optional: CLBlast for OpenCL GPU acceleration (AMD/Intel/NVIDIA)
dotnet add package AiDotNet.Native.CLBlast

# Optional: CUDA for NVIDIA GPU acceleration (requires NVIDIA GPU)
dotnet add package AiDotNet.Native.CUDA

Quick Start

using AiDotNet.Tensors.LinearAlgebra;

// Create vectors
var v1 = new Vector<double>(new[] { 1.0, 2.0, 3.0, 4.0 });
var v2 = new Vector<double>(new[] { 5.0, 6.0, 7.0, 8.0 });

// SIMD-accelerated operations
var sum = v1 + v2;
var dot = v1.Dot(v2);

// Create matrices
var m1 = new Matrix<double>(3, 3);
var m2 = Matrix<double>.Identity(3);

// Matrix operations
var product = m1 * m2;
var transpose = m1.Transpose();

CPU Benchmarks

All benchmarks run on AMD Ryzen 9 3950X, .NET 10.0, BenchmarkDotNet. No AVX-512.

vs TorchSharp CPU (Tensor Operations, float)

Head-to-head against TorchSharp's libtorch C++ backend on identical data sizes.

AiDotNet wins or matches TorchSharp CPU on the majority of operations using pure managed C# with hand-tuned SIMD, no native C++ dependencies required.

vs MathNet.Numerics (Linear Algebra, double, N=1000)

vs NumSharp (N=1000)

vs System.Numerics.Tensors.TensorPrimitives (N=1000)

In-place operations (zero allocation) compared to raw TensorPrimitives calls.

Small Matrix Multiply (double)

AiDotNet is 1.4x faster at 16x16 and 3.4x faster at 32x32 than MathNet.

SIMD Instruction Support

The library automatically detects and uses the best available SIMD instructions:

Check Available Acceleration

using AiDotNet.Tensors.Engines;

var caps = PlatformDetector.Capabilities;

// SIMD capabilities
Console.WriteLine($"AVX2: {caps.HasAVX2}");
Console.WriteLine($"AVX-512: {caps.HasAVX512F}");

// GPU support
Console.WriteLine($"CUDA: {caps.HasCudaSupport}");
Console.WriteLine($"OpenCL: {caps.HasOpenCLSupport}");

// Native library availability
Console.WriteLine($"OpenBLAS: {caps.HasOpenBlas}");
Console.WriteLine($"CLBlast: {caps.HasClBlast}");

// Or get a full status summary
Console.WriteLine(NativeLibraryDetector.GetStatusSummary());

Optional Acceleration Packages

AiDotNet.Native.OpenBLAS

Provides optimized CPU BLAS operations using OpenBLAS:

dotnet add package AiDotNet.Native.OpenBLAS

Performance: Accelerated BLAS operations for matrix multiply and decompositions.

AiDotNet.Native.CLBlast

Provides GPU acceleration via OpenCL (works on AMD, Intel, and NVIDIA GPUs):

dotnet add package AiDotNet.Native.CLBlast

Performance: 10x+ faster for large matrix operations on GPU.

AiDotNet.Native.CUDA

Provides GPU acceleration via NVIDIA CUDA (NVIDIA GPUs only):

dotnet add package AiDotNet.Native.CUDA

Performance: 30,000+ GFLOPS for matrix operations on modern NVIDIA GPUs.

Requirements:

• NVIDIA GPU (GeForce, Quadro, or Tesla)
• NVIDIA display driver 525.60+ (includes CUDA driver)

Usage with helpful error messages:

using AiDotNet.Tensors.Engines.DirectGpu.CUDA;

// Recommended: throws beginner-friendly exception if CUDA unavailable
using var cuda = CudaBackend.CreateOrThrow();

// Or check availability first
if (CudaBackend.IsCudaAvailable)
{
    using var backend = new CudaBackend();
    // Use CUDA acceleration
}

If CUDA is not available, you'll get detailed troubleshooting steps explaining exactly what's missing and how to fix it.

Requirements

• .NET 10.0 or .NET Framework 4.7.1+
• Windows x64, Linux x64, or macOS x64/arm64

License

Apache 2.0 - See LICENSE for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.