NMath User's Guide

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39.1 Supported Features (.NET, C#, CSharp, VB, Visual Basic, F#)

Only selected NMath classes are able to route their computations to the graphics processor. The directly supported features for GPU acceleration of linear algebra (dense systems) include:

Singular value decomposition (SVD)

QR decomposition

Eigenvalue routines

Solve Ax = B

GPU acceleration for signal processing includes:

1D Fast Fourier Transforms (Complex data input)1

2D Fast Fourier Transforms (Complex data input)

Of course, many higher-level NMath and NMath Stats classes make use of these functions internally, and so also benefit from GPU acceleration indirectly.


Least squares, including weighted least squares

Filtering, such as moving window filters and Savitsky-Golay

Nonlinear programming (NLP)

Ordinary differential equations (ODE)

NMath Stats

Two-Way ANOVA, with or without repeated measures

Factor Analysis

Linear regression and logistic regression

Principal component analysis (PCA)

Partial least squares (PLS)

Nonnegative matrix factorization (NMF)

  1. Real signals can currently be handled by filling the imaginary parts with zeros.