High-performance tensor operations with SIMD acceleration for .NET. Provides Vector, Matrix, and Tensor types with hardware-accelerated operations (Sin, Cos, Exp, Log, etc.) that work across .NET Framework 4.7.1 and .NET 10.0. Supports any numeric type via generic INumericOperations interface. Optional GPU acceleration available via separate packages.
High-performance tensor operations with SIMD and GPU acceleration for .NET.
Features
Zero Allocations: Uses ArrayPool<T> and Span<T> for hot path performance
SIMD Acceleration: Automatic vectorization using SSE, AVX, AVX2, AVX-512, and ARM NEON
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();
Performance
The library automatically detects and uses the best available SIMD instructions:
Instruction Set
Vector Width
Supported
AVX-512
512-bit (16 floats)
.NET 8+
AVX2
256-bit (8 floats)
.NET 6+
AVX
256-bit (8 floats)
.NET 6+
SSE4.2
128-bit (4 floats)
.NET 6+
ARM NEON
128-bit (4 floats)
.NET 6+
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: 2x faster matrix operations compared to managed code.
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.
