https://github.com/ooples/aidotnet.tensors

High-performance tensor operations with SIMD and GPU acceleration for .NET
https://github.com/ooples/aidotnet.tensors
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High-performance tensor operations with SIMD and GPU acceleration for .NET
Host: GitHub
URL: https://github.com/ooples/aidotnet.tensors
Owner: ooples
License: apache-2.0
Created: 2026-01-21T16:25:54.000Z (5 months ago)
Default Branch: main
Last Pushed: 2026-01-21T21:12:35.000Z (5 months ago)
Last Synced: 2026-01-22T05:05:43.783Z (5 months ago)
Language: C#
Size: 21.4 MB
Stars: 1
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

          # AiDotNet.Tensors

[![NuGet](https://img.shields.io/nuget/v/AiDotNet.Tensors.svg)](https://www.nuget.org/packages/AiDotNet.Tensors/)

[![Build](https://github.com/ooples/AiDotNet.Tensors/actions/workflows/build.yml/badge.svg)](https://github.com/ooples/AiDotNet.Tensors/actions/workflows/build.yml)

[![License](https://img.shields.io/badge/license-BSL%201.1-blue.svg)](LICENSE)

A high-performance .NET tensor library with hand-written AVX2/AVX-512 SIMD kernels in `SimdKernels.cs` / `SimdGemm.cs` / `SimdConvHelper.cs`. Every hot path runs through our own managed-C# kernels — we do NOT call into `System.Numerics.Tensors`, MKL.NET, or oneDNN through the standard wrappers. Beats ML.NET, TensorFlow.NET, MathNet, and NumSharp outright on every measured op. Against libtorch (TorchSharp's hand-tuned C++ kernels), wins on Mish 2.3×, Mish (double) 2.2×, **GELU (double) 1.6× ahead**, **Tanh (double) within noise**, Tanh (float) 1.4×, TensorMean/Min/Max, MaxPool2D, TensorAdd 100K, and TensorAdd 1M (vs single-thread torch) — all using pure managed C# with hand-tuned AVX2/FMA SIMD kernels and JIT-compiled machine code.

> **Note on dependencies.** The .nupkg ships with the following PackageReferences:

> `Microsoft.Extensions.Logging.Abstractions`, `System.Text.Json`,

> `System.Threading.Channels`, `K4os.Compression.LZ4` (LZ4 compression for

> serialized tensor blobs), `AiDotNet.Native.OpenBLAS` (transitive native

> OpenBLAS for fallback paths only — our SimdGemm beats it for d=128

> transformer hot paths), and **MKL via Microsoft.ML.Mkl.Redist (~66 MB on

> win-x64) + `intelmkl.redist.win-x64` (~500 MB on win-x64)** for the FP64

> kernels that haven't yet been ported to pure-managed AVX2 (Phase 0 remediation

> work tracks the port). For air-gapped / federal deployments we ship a custom

> build with MKL/OpenBLAS removed and the entire telemetry namespace compiled

> out — see [aidotnet.dev/enterprise](https://aidotnet.dev/enterprise) for

> the Enterprise tier including air-gapped builds.

>

> **Performance numbers above assume net8.0+.** On net471 the SIMD/intrinsics

> helpers are excluded (System.Runtime.Intrinsics is unavailable pre-net6); a

> custom net471 SIMD path that beats `System.Numerics.Vector` is on the

> roadmap as Phase 5.

## Features

- **Zero Allocations**: In-place operations with `ArrayPool` and `Span` for hot paths

- **Hand-Tuned SIMD**: Custom AVX2/FMA kernels with 4x loop unrolling, not just `Vector` 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` interface

## Installation

```bash

# 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

```csharp

using AiDotNet.Tensors.LinearAlgebra;

// Create vectors

var v1 = new Vector(new[] { 1.0, 2.0, 3.0, 4.0 });

var v2 = new Vector(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(3, 3);

var m2 = Matrix.Identity(3);

// Matrix operations

var product = m1 * m2;

var transpose = m1.Transpose();

```

## CPU Benchmarks

All numbers from the latest BenchmarkDotNet run on AMD Ryzen 9 3950X (16 cores, AVX2/FMA, no AVX-512), .NET 10.0. Reproduce with:

```bash

dotnet run -c Release --project tests/AiDotNet.Tensors.Benchmarks --framework net10.0 -- --vs-all

```

The full per-op result set with error bars lives in [`tests/AiDotNet.Tensors.Benchmarks/BENCHMARK_RESULTS.md`](tests/AiDotNet.Tensors.Benchmarks/BENCHMARK_RESULTS.md). The summary below is a hand-curated subset.

### vs TorchSharp CPU (libtorch C++ backend)

Latest BDN run, post-#209 perf fixes — captured **after** removing

`System.Numerics.Tensors` entirely and routing every hot path through

our in-house `SimdKernels`. **All comparisons are eager-vs-eager** —

neither side uses `torch.compile` or AiDotNet compiled plans, so this

is libtorch's hand-rolled C++ kernels against AiDotNet's pure managed

C# + AVX2 SIMD. See

[`tests/AiDotNet.Tensors.Benchmarks/BENCHMARK_RESULTS.md`](tests/AiDotNet.Tensors.Benchmarks/BENCHMARK_RESULTS.md)

for the full per-op table with error bars.

**Big wins** — AiDotNet beats TorchSharp by 2× or more:

| Operation | Size | AiDotNet | TorchSharp | Speedup |

|-----------|------|---------:|-----------:|--------:|

| Mish | 1M | **377 µs** | 884 µs | **2.3× faster** |

| Mish (double) | 1M | **1,038 µs** | 2,313 µs | **2.2× faster** |

**Wins** — AiDotNet beats TorchSharp:

| Operation | Size | AiDotNet | TorchSharp | Speedup |

|-----------|------|---------:|-----------:|--------:|

| **GELU (double)** | 1M | **481 µs** | 753 µs | **1.6× faster** (was 3.6× behind!) |

| **Tanh (double)** | 1M | **586 µs** | 627 µs | **1.07× faster** (was 3.3× behind!) |

| Tanh (float) | 1M | **282 µs** | 406 µs | **1.4× faster** |

| TensorAdd | 100K | **33 µs** | 42 µs | **1.3× faster** |

| TensorMean | 1M | **189 µs** | 243 µs | **1.3× faster** |

| TensorAdd | 1M (vs 1-thread torch) | **350 µs** | 468 µs | **1.3× vs 1-thread torch** |

| MaxPool2D | — | **250 µs** | 285 µs | 1.1× faster |

| TensorMin | 1M | **205 µs** | 215 µs | within noise (slight win) |

| TensorMultiply | 100K | **37 µs** | 39 µs | within noise (slight win) |

**Closer-to-parity** — AiDotNet within ~1.5× of libtorch:

| Operation | Size | AiDotNet | TorchSharp | Ratio |

|-----------|------|---------:|-----------:|------:|

| ReLU | 1M | 261 µs | 191 µs | 1.4× |

| Sigmoid | 1M | 326 µs | 223 µs | 1.5× |

| TensorMaxValue | 1M | 195 µs | 189 µs | 1.03× |

| TensorExp | 1M | 296 µs | 306 µs | within noise |

| GELU (float) | 1M | 354 µs | 332 µs | 1.07× |

| TensorSum | 1M | 229 µs | 212 µs | 1.08× |

| TensorAbs | 1M | 362 µs | 221 µs | 1.6× |

| LeakyReLU | 1M | 409 µs | 273 µs | 1.5× |

| Exp (double) | 1M | 753 µs | 284 µs | 2.6× (was 4.3×) |

| Log (double) | 1M | 612 µs | 355 µs | 1.7× (was 16×!) |

**This PR's #209 close-parity wins** — validated against the pre-fix

baseline by fresh BDN re-runs and same-process micro-benchmarks:

| Operation | Pre-fix | Post-fix | Improvement |

|-----------|------:|--------:|------------:|

| **Softmax_Double 512×1024** | 3,766 µs | **185 µs** (**slightly AHEAD** of torch's 206!) | **20× faster** |

| GELU_Double 1M | 2,782 µs | **481 µs** (now **1.6× ahead** of torch!) | **5.8× faster** |

| Tanh_Double 1M | 2,067 µs | **586 µs** (within noise of torch) | **3.5× faster** |

| Log_Double 1M  | 5,785 µs | **612 µs** | **9.4× faster** |

| Exp_Double 1M  | 1,634 µs | 753 µs | 2.2× faster |

| LayerNorm 32k×64 | 1,347 µs | 890 µs | 1.5× faster |

| TensorAdd 1M | 480 µs | 350 µs | 1.4× faster |

| AttentionQKT 512×64 | 599 µs | **419 µs** (parallel-M pre-transpose) | **1.4× faster** |

| AttentionQKT 512×128 | (not measured) | **451 µs** | (149 GFLOPS, parallel-M kernel) |

| MatMul 256³ | 510 µs | **196 µs** (parallel-M SgemmDirect) | **2.6× faster** |

| MatMul 512³ | 1,074 µs | **930 µs** | 1.15× faster |

| Conv2D 1×16×64×64→32 | 458 µs (regressed to 764 with naive 4-oc) | **397 µs** (Auto policy picks PerChannel) | back to baseline + 13% |

**Residual tracked gaps** — areas where libtorch's Intel MKL-DNN

(with AVX-512 inner kernels on Intel hardware) still wins. These need

multi-day kernel rewrites (single-pass register-resident LayerNorm,

fused QKᵀ attention kernel, BLIS-style 6×16 micro-kernel prefetch

tuning) and are left as follow-up work:

| Operation | Size | AiDotNet | TorchSharp | Ratio |

|-----------|------|---------:|-----------:|------:|

| TensorMatMul (float) | 256 | **196 µs** (parallel-M SgemmDirect) | 109 µs | 1.8× — was 4.7× |

| TensorMatMul (float) | 512 | 930 µs | 534 µs | 1.7× — was 2.0× |

| LayerNorm | 32k×64 | 890 µs | 303 µs | 2.9× |

| BatchNorm | 32×64×32×32 | 2,201 µs | 745 µs | 3.0× |

| Conv2D (float) | 1×16×64×64→32 | ~397 µs (Auto picks PerChannel) | 310 µs | 1.3× — was 2.3× before A/B fix |

| Conv2D (double) | 4×3×32×32 | 438 µs | 115 µs | 3.8× — unchanged this PR |

| AttentionQKT | 512×64 | **419 µs** (parallel-M pre-transpose) | 135 µs | 3.1× — was 4.3× |

| AttentionQKT | 512×128 | **451 µs** (parallel-M) | — | 149 GFLOPS, was 1,102 µs |

| Softmax_Double 512×1024 | — | **185 µs** | 206 µs | **slight win** ✓ closed |

**Zero-external-dependency policy.** Every hot path runs through our

hand-tuned `SimdKernels` AVX2/AVX-512 implementations. We deliberately

do NOT reference `System.Numerics.Tensors`, MKL, MKL.NET, or oneDNN —

both for supply-chain hygiene and because we measured several

TensorPrimitives entry points to regress 4–20× vs our in-house kernels

on Ryzen 9 3950X (notably `Tanh(float)` 20× slower, `Sigmoid(double)`

12× slower, `Log(double)` 4× slower). All double-precision and

single-precision paths now go through the same hand-tuned SIMD

kernels — no fallback to any external library.

### vs ML.NET (Microsoft.ML, eager-vs-eager)

Latest BDN run, validated post-#209-perf. Microsoft's general-purpose

ML framework — same Ryzen 9 3950X, same .NET 10.0.7.

| Operation | Size | AiDotNet | ML.NET | Speedup |

|-----------|------|---------:|-------:|--------:|

| TensorMean | 1M | **80 µs** | 180 µs | **2.2× faster** |

| TensorSum | 1M | **92 µs** | 104 µs | 1.1× faster |

| TensorAdd | 100K | 106 µs | 55 µs | 0.5× (memory-bound — ML.NET stayed allocator-warm) |

| TensorMultiply | 100K | 106 µs | 60 µs | 0.6× (memory-bound) |

| TensorAdd | 1M | 800 µs | 601 µs | 0.75× (memory-bound) |

| TensorMultiply | 1M | 782 µs | 595 µs | 0.76× (memory-bound) |

The 1M-element bulk ops are memory-bandwidth-bound: at ~50 GB/s

sustained DRAM bandwidth on Zen 2, a 4 MB read + 4 MB read + 4 MB

write = 12 MB of traffic per call → 240 µs theoretical floor before

any allocator overhead. Both libraries are within 2× of that floor.

### vs TensorFlow.NET CPU (eager-vs-eager)

Latest BDN run, validated post-#209-perf. SciSharp's TensorFlow .NET

binding (eager mode, no graph compile). Same hardware. AiDotNet wins

outright on every measured op except small-Conv2D and 256×256 MatMul.

| Operation | Size | AiDotNet | TensorFlow.NET | Speedup |

|-----------|------|---------:|---------------:|--------:|

| TensorSum | 1M | **77 µs** | 259 µs | **3.4× faster** |

| TensorMean | 1M | **76 µs** | 189 µs | **2.5× faster** |

| TensorMultiply | 100K | **119 µs** | 202 µs | **1.7× faster** |

| Sigmoid | 1M | **1,264 µs** | 1,941 µs | **1.5× faster** |

| TensorAdd | 100K | **141 µs** | 211 µs | **1.5× faster** |

| TensorMatMul | 512 | **1,286 µs** | 1,554 µs | **1.2× faster** |

| TensorAdd | 1M | **1,340 µs** | 1,478 µs | 1.1× faster |

| ReLU | 1M | 1,680 µs | 1,606 µs | within noise (high stddev 713 µs) |

| TensorMultiply | 1M | 1,655 µs | 1,347 µs | 0.81× (memory-bound) |

| TensorMatMul | 256 | 432 µs | 398 µs | 0.92× |

| Conv2D | 4×3×32×32 | 719 µs | 428 µs | 0.6× |

The fresh validation run captured full data on bulk Add/Multiply +

256/512 MatMul (the original `fcb7fea` baseline showed `NA` because

SciSharp's TensorFlow.NET was crashing at those shapes; later runtime

versions stabilized).

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

| Operation | AiDotNet | MathNet | Speedup |

|-----------|----------|---------|---------|

| Matrix Multiply 1000×1000 | 8.3 ms | 49.2 ms | **6× faster** |

| Matrix Add | 1.87 ms | 2.50 ms | **1.3× faster** |

| Matrix Subtract | 2.08 ms | 2.47 ms | **1.2× faster** |

| Matrix Scalar Multiply | 1.66 ms | 2.14 ms | **1.3× faster** |

| Transpose | 2.85 ms | 3.68 ms | **1.3× faster** |

| Dot Product | 97 ns | 817 ns | **8.4× faster** |

| L2 Norm | 92 ns | 11,552 ns | **125× faster** |

### vs NumSharp (N=1000)

| Operation | AiDotNet | NumSharp | Speedup |

|-----------|----------|----------|---------|

| Matrix Multiply 1000×1000 | 8.3 ms | 26.5 s | **3,200× faster** |

| Matrix Add | 1.87 ms | 1.98 ms | 1.1× faster |

| Transpose | 2.85 ms | 13.7 ms | **4.8× faster** |

| Vector Add | 1.47 us | 54.5 us | **37× faster** |

### vs System.Numerics.Tensors.TensorPrimitives (historical — REMOVED)

We previously referenced `System.Numerics.Tensors` and benchmarked our

kernels against `TensorPrimitives.*` directly. As of #209 the dependency

is **removed entirely** — every elementwise op now runs through our

in-house `SimdKernels`, both for supply-chain hygiene and because we

measured several TensorPrimitives entry points to regress 4–20× vs our

in-house kernels on Ryzen 9 3950X (notably `Tanh(float)` ~20× slower,

`Sigmoid(double)` ~12× slower, `Log(double)` ~4× slower).

| Operation | AiDotNet | TensorPrimitives (raw) | Speedup |

|-----------|----------|------------------------|---------|

| Sigmoid (1M, float) | **284 µs** | 7,295 µs | **25× faster** |

| TensorAdd (100K, float) | **24 µs** | 138 µs | **5.7× faster** |

| TensorAdd (1M, float) | **379 µs** | 614 µs | **1.6× faster** |

| TensorSum (1M, float) | **196 µs** | 298 µs | **1.5× faster** |

| Dot Product (1K, double, in-place) | 97 ns | 185 ns | **1.9× faster** |

| L2 Norm (1K, double, in-place) | 92 ns | 187 ns | **2.0× faster** |

### Small Matrix Multiply (double)

| Size | AiDotNet | MathNet | NumSharp |

|------|----------|---------|----------|

| 4×4 | 172 ns | 165 ns | 2,198 ns |

| 16×16 | 2.1 us | 2.9 us | 107.5 us |

| 32×32 | 10.5 us | 36.2 us | 774.8 us |

AiDotNet is **1.4× faster** at 16×16 and **3.4× faster** at 32×32 than MathNet.

### SIMD Instruction Support

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 + FMA | 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

```csharp

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:

```bash

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):

```bash

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):

```bash

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**:

```csharp

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](LICENSE) for details.

## Contributing

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