DotCompute Documentation

Welcome to the DotCompute documentation. DotCompute is a high-performance, Native AOT-compatible universal compute framework for .NET 9+ with production-ready CPU and CUDA acceleration.

What is DotCompute?

DotCompute provides GPU-accelerated compute capabilities for .NET applications with:

  • Production-Ready Performance: Measured 3.7x-141x speedup over CPU on real workloads
  • Multiple Backends: CPU (SIMD), CUDA, Metal, OpenCL support
  • Native AOT Compatible: Sub-10ms startup times, zero runtime code generation
  • Simple API: Write kernels once in C# with [Kernel] attributes
  • Comprehensive Testing: 215/234 tests passing (91.9% pass rate)

Status

Component Status Coverage Description
Core Runtime ✅ Production Ready 91.9% Orchestration, debugging, optimization
CPU Backend ✅ Production Ready ~85% SIMD vectorization (AVX512/AVX2/NEON)
CUDA Backend ✅ Production Ready ~85% NVIDIA GPU support (CC 5.0+)
Metal Backend ✅ Production Ready 85% Apple Silicon optimized
OpenCL Backend 🚧 Foundation Complete ~70% Cross-platform GPU support
Memory Management ✅ Production Ready ~85% Unified memory with 90% pooling
Algorithms 🚧 Active Development ~75% Linear algebra, FFT, numerical methods
LINQ Extensions 🚧 In Development ~60% GPU-accelerated LINQ queries

System Requirements

  • .NET 9.0 or later
  • For GPU Acceleration:
    • NVIDIA GPU with CUDA 12.0+ (Compute Capability 5.0+)
    • Apple Silicon M1/M2/M3 with Metal 2.4+
    • OpenCL 1.2+ compatible device

Installation

# Core runtime
dotnet add package DotCompute.Core --version 0.2.0-alpha

# CPU backend
dotnet add package DotCompute.Backends.CPU --version 0.2.0-alpha

# CUDA backend
dotnet add package DotCompute.Backends.CUDA --version 0.2.0-alpha

# Metal backend (macOS only)
dotnet add package DotCompute.Backends.Metal --version 0.2.0-alpha

Quick Example

using DotCompute.Abstractions;

// Define kernel
[Kernel]
public static void VectorAdd(ReadOnlySpan<float> a, ReadOnlySpan<float> b, Span<float> result)
{
    int idx = Kernel.ThreadId.X;
    if (idx < result.Length)
        result[idx] = a[idx] + b[idx];
}

// Configure services
var services = new ServiceCollection();
services.AddDotComputeRuntime();
var provider = services.BuildServiceProvider();

// Execute on GPU
var orchestrator = provider.GetRequiredService<IComputeOrchestrator>();
var result = await orchestrator.ExecuteKernelAsync<float[], float[]>(
    nameof(VectorAdd),
    new { a = dataA, b = dataB, length = 1_000_000 }
);

Documentation

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