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.
NMath
● 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)