NDLI: Multi-GPU implementation of the uniformization method for solving Markov models

Content Provider	IEEE Xplore Digital Library
Author	Karwacki, M. Bylina, B. Bylina, J.
Copyright Year	2012
Description	Author affiliation: Institute of Mathematics, Marie Curie-Sklodowska University, Pl. M. Curie-Skłodowskiej 5, 20-031 Lublin, Poland (Karwacki, M.; Bylina, B.; Bylina, J.)
Abstract	Markovian models can generate very large sparse matrices, which are difficult to store and solve. A useful method for finding transient probabilities in Markovian models is the uniformization. The aim of this paper is to show that the performance of the uniformization can be improved using multi-GPU architecture. We propose partitioning scheme for HYB sparse matrix storage format and some optimization techniques adjusted so as to minimize communication between GPUs during iterative sparse matrix-vector multiplication, which is the most time consuming step. The results of experiments show that on multi-GPU we can solve larger matrices than on single device and accelerate computations in comparison to a multithreaded CPU. Computational test have been carried out in double precision for a wireless network models. Using multi-GPU we were able to solve model which is described by a matrix of the size $3.6×10^{7}.$
Starting Page	533
Ending Page	537
File Size	148937
Page Count	5
File Format	PDF
ISBN	9781467307086
e-ISBN	9788360810484
e-ISBN	9788360810514
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-09
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Polish Info Processing Socit
Subject Keyword	Uniformization method Multicore processing Computational modeling Graphics processing units Vectors Sparse matrices GPU Heterogeneous computations Parallel computing Wireless network models Markovian models Multi-GPU Markov processes Mathematical model
Content Type	Text
Resource Type	Article

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