Parallel monotone spline interpolation and approximation on GPUs
- Publication Type:
- Designing Scientific Applications on GPUs, 2013, pp. 295 - 310
- Issue Date:
Files in This Item:
|Parallel monotone spline interpolation and approximation on GPUs.pdf||Published version||465.69 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2014 by Taylor & Francis Group, LLC. Monotonicity preserving interpolation and approximation have received substantial attention in the last thirty years because of their numerous applications in computer aided-design, statistics, and machine learning [9, 10, 19]. Constrained splines are particularly popular because of their exibility in modeling diffierent geometrical shapes, sound theoretical properties, and availability of numerically stable algorithms [9,10,26]. In this work we examine parallelization and adaptation for GPUs of a few algorithms of monotone spline interpolation and data smoothing, which arose in the context of estimating probability distributions.
Please use this identifier to cite or link to this item: