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I. Introduction into GPU programming.
II. Exception safe dynamic memory handling in Cuda project.
III. Calculation of partial sums in parallel.
IV. Manipulation of piecewise polynomial functions in parallel.
1. History of changes (PiecewisePoly).
2. Calculus behind the PiecewisePoly project.
3. Code structure (PiecewisePoly project).
4. Python scripting for PiecewisePoly project.
V. Manipulation of localized piecewise polynomial functions in parallel.
Downloads. Index. Contents.

Manipulation of piecewise polynomial functions in parallel.


here is a python library for polynomial manipulation presented in the section ( Piecewise polynomials ). In this section we optimize for speed using Cuda.

The source code may be downloaded from the Download section. It includes make files and MS Visual Studio project files. The make files build a release extension for Python with full functionality. The MSVS project files build a debug executable file with some testing functionality.

We restrict our attention to needs of wavelet analysis, see the section ( Wavelet analysis section ). In particular, the presented here library performs well for piecewise polynomials of small degree and large number of pieces and it does not perform too well for large degree and small number of pieces.

There is a __shared__ memory-optimized code inside $T$ -function of ots::poly::impl namespace that refuses to work if the degree is greater than or equal to 16. This limitation may be removed by making localized corrections within that function.

We characterize a piecewise polynomial $p\left( x\right) $ , $x\in \QTR{cal}{R}$ with the following set of parameters:

MATH (Piecewise polynomial representation)
MATH MATH MATH See the section ( Wavelet analysis section ) for the notation $x_{a}^{d}$ .




1. History of changes (PiecewisePoly).
2. Calculus behind the PiecewisePoly project.
3. Code structure (PiecewisePoly project).
4. Python scripting for PiecewisePoly project.

Downloads. Index. Contents.


















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