TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Efficient Solving of Markov Decision Processes on GPUs Using Parallelized Sparse Matrices

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 Conference on Design and Architectures for Signal and Image Processing, DASIP 2018
KustantajaIEEE COMPUTER SOCIETY PRESS
Sivut13-18
Sivumäärä6
ISBN (elektroninen)9781538682371
DOI - pysyväislinkit
TilaJulkaistu - joulukuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaConference on Design and Architectures for Signal and Image Processing - Porto, Portugali
Kesto: 10 lokakuuta 201812 lokakuuta 2018

Julkaisusarja

NimiConference on Design and Architectures for Signal and Image Processing, DASIP
ISSN (painettu)2164-9766

Conference

ConferenceConference on Design and Architectures for Signal and Image Processing
MaaPortugali
KaupunkiPorto
Ajanjakso10/10/1812/10/18

Tiivistelmä

Markov Decision Processes (MDPs) provide important capabilities for facilitating the dynamic adaptation of hardware and software configurations to the environments in which they operate. However, the use of MDPs in embedded signal processing systems is limited because of the large computational demands for solving this class of system models. This paper presents Sparse Parallel Value Iteration (SPVI), a new algorithm for solving large MDPs on resource-constrained embedded systems that are equipped with mobile GPUs. SPVI leverages recent advances in parallel solving of MDPs and adds sparse linear algebra techniques to significantly outperform the state-of-the-art. The method and its application are described in detail, and demonstrated with case studies that are implemented on an NVIDIA Tegra K1 System On Chip (SoC). The experimental results show execution time improvements in the range of 65 % -78% for several applications. SPVI also lifts restrictions required by other MDP solver approaches, making it more widely compatible with large classes of optimization problems.