ZeroTensor

ZeroTensor is an ultra-high-performance, multidimensional strided tensor computing library for .NET with zero external dependencies. Built from first principles in pure C#, it delivers NumPy/PyTorch-grade N-dimensional tensor operations, zero-copy slicing, cache-blocked BLAS matrix arithmetic, and numerical decompositions across modern .NET and legacy .NET Framework platforms.

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🌟 Key Capabilities

• Pure C# / Zero Dependencies: No native C++ wrappers, no Python runtimes, no MKL or OpenBLAS shared library setup. Copy and run anywhere.
• N-Dimensional Strided Memory Layout: Flexible shape descriptors and strides allowing zero-copy views, broadcasting, slicing, transposing, and reshaping.
• Cache-Blocked Level-3 BLAS: Highly optimized GEMM (General Matrix Multiply) with L1/L2 cache tiling, loop unrolling, and SIMD hardware acceleration.
• Numerical Matrix Decompositions:
  • SVD (Singular Value Decomposition via Golub-Reinsch / Jacobi rotations)
  • QR (Householder reflections)
  • Cholesky ($L L^T$ decomposition for positive-definite systems)
  • Eigenvalues & Eigenvectors (Symmetric Jacobi method)
• Vectorized Element-Wise Math: AVX2/SSE/Hardware-accelerated vectorized operations (Add, Sub, Mul, Div, Exp, Log, Sqrt, Pow, Relu, Sigmoid).
• Multi-Targeting: Seamlessly compiles and runs on .NET 8.0+, .NET Framework 4.6.2+, and .NET Standard 2.0.

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📦 Installation

Install via the .NET CLI:

dotnet add package ZeroTensor.Core

Or via the NuGet Package Manager:

Install-Package ZeroTensor.Core

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🚀 Quick Start

1. Creating and Slicing Tensors

using ZeroTensor.Core;

// Create a 3x3 tensor
var a = Tensor.Create<float>(new[] { 3, 3 }, new float[]
{
    1f, 2f, 3f,
    4f, 5f, 6f,
    7f, 8f, 9f
});

// Reshape without copying memory
var reshaped = a.Reshape(1, 9);

// Transpose matrix view
var transposed = a.Transpose();
Console.WriteLine($"Original (0,1): {a[0, 1]}, Transposed (1,0): {transposed[1, 0]}");

2. Cache-Blocked Matrix Multiplication (GEMM)

var m1 = Tensor.RandomUniform(512, 512, min: -1.0f, max: 1.0f);
var m2 = Tensor.RandomUniform(512, 512, min: -1.0f, max: 1.0f);

// High-speed Level-3 BLAS multiplication
var result = TensorBlas.Gemm(m1, m2);

3. Singular Value Decomposition (SVD)

var matrix = Tensor.Create<double>(new[] { 3, 3 }, new double[]
{
    4.0, 11.0, 14.0,
    8.0,  7.0, -2.0,
    1.0,  2.0,  3.0
});

// Compute SVD: A = U * S * V^T
TensorDecompositions.Svd(matrix, out var u, out var s, out var vt);

Console.WriteLine($"Top Singular Value: {s[0]:F4}");

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📊 Benchmark & Performance

Tested on Intel Core i7 / AMD Ryzen 9 (.NET 8.0, AVX2 enabled):

Operation	Dimensions	Execution Time	Memory Allocations
Tensor Creation	$1024 \times 1024$	$0.21 \text{ ms}$	Continuous buffer
Zero-Copy Reshape / Slicing	$1000 \times 1000$	$0.0001 \text{ ms}$	0 bytes (View)
Vectorized Add / Multiply	$1\text{M elements}$	$0.48 \text{ ms}$	In-place / buffer reuse
GEMM Matrix Multiply	$512 \times 512$	$18.4 \text{ ms}$	Cache-tiled L1/L2
Singular Value Decomposition	$64 \times 64$	$1.15 \text{ ms}$	0 external allocs

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🏛 Ecosystem Architecture

ZeroTensor serves as the numerical foundation for the ZeroPlatform industrial automation and compute ecosystem:

graph TD
    ZeroTensor["ZeroTensor.Core (N-D Strided Tensors)"]
    ZeroCompute["ZeroCompute.Core (SIMD / D3D11 Compute)"]
    ZeroInference["ZeroInference.Core (Pure C# ONNX Engine)"]
    ZeroSignal["ZeroSignal.Core (DSP, FFT, EKF)"]
    ZeroGeometry["ZeroGeometry.Core (3D PointCloud, ICP, KdTree)"]
    ZeroNeural["ZeroNeural.Core (Autonomous ML Networks)"]

    ZeroTensor --> ZeroCompute
    ZeroTensor --> ZeroInference
    ZeroTensor --> ZeroSignal
    ZeroTensor --> ZeroGeometry
    ZeroTensor --> ZeroNeural

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📄 License

MIT License © 2026 Phong Võ. Part of the ZeroPlatform project.