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Solving time-invariant differential matrix Riccati equations using GPGPU computing
- Source :
- RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia, instname
- Publication Year :
- 2014
- Publisher :
- Springer Science and Business Media LLC, 2014.
-
Abstract
- Differential matrix Riccati equations (DMREs) enable to model many physical systems appearing in different branches of science, in some cases, involving very large problem sizes. In this paper, we propose an adaptive algorithm for time-invariant DMREs that uses a piecewise-linearized approach based on the Pade approximation of the matrix exponential. The algorithm designed is based upon intensive use of matrix products and linear system solutions so we can seize the large computational capability that modern graphics processing units (GPUs) have on these types of operations using CUBLAS and CULATOOLS libraries (general purpose GPU), which are efficient implementations of BLAS and LAPACK libraries, respectively, for NVIDIA $$\copyright $$ © GPUs. A thorough analysis showed that some parts of the algorithm proposed can be carried out in parallel, thus allowing to leverage the two GPUs available in many current compute nodes. Besides, our algorithm can be used by any interested researcher through a friendly MATLAB $$\copyright $$ © interface.
- Subjects :
- Computer science
Piecewise-linearized method
Parallel computing
Theoretical Computer Science
LTI system theory
Matrix (mathematics)
Padé approximants
CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL
Padé approximant
Leverage (statistics)
Ordinary differential equation (ODE)
Graphics
MATLAB
computer.programming_language
Adaptive algorithm
GPGPU
Linear system
Differential matrix Riccati equation (DMRE)
Hardware and Architecture
Matrix exponential
General-purpose computing on graphics processing units
LENGUAJES Y SISTEMAS INFORMATICOS
computer
Software
Information Systems
Subjects
Details
- ISSN :
- 15730484 and 09208542
- Volume :
- 70
- Database :
- OpenAIRE
- Journal :
- The Journal of Supercomputing
- Accession number :
- edsair.doi.dedup.....bdb56563b5cbc49ec8ccc2ea522a4439
- Full Text :
- https://doi.org/10.1007/s11227-014-1111-3