NDLI: Computational Aspects of GPU-accelerated Sparse Matrix-Vector Multiplication for Solving Markov Models

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Computational Aspects of GPU-accelerated Sparse Matrix-Vector Multiplication for Solving Markov Models

Content Provider	Semantic Scholar
Author	Bylina, Beata Bylina, Jarosław Karwacki, Marek
Copyright Year	2011
Abstract	In this article we investigate some computational aspects of GPU-accelerated matrix-vector multiplication where matrix is sparse. Particularly, we deal with sparse matrices appearing in modelling with Markovian queuing models. The model we use for research is a Markovian queuing model of a wireless device. This model describes the device’s behavior during possible channel occupation by other devices. We study the efficiency of multiplication of a sparse matrix by a dense vector with the use of an appropriate, ready-to-use GPU-accelerated mathematical library, namely CUSP. For the CUSP library we discuss data structures and their impact on the CUDA platform for the fine-grained parallel architecture of the GPU. Our aim is to find the best format for storing a sparse matrix for GPU-computation (especially one associated with the Markovian model of a wireless device). We compare the time, the performance and the speed-up for the card NVIDIA Tesla C2050 (with ECC ON). For unstructured matrices (as our Markovian matrices), we observe speed-ups (in respect to CPU-only computations) of over 8 times.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://taai.iitis.pl/taai/article/download/338/taai-vol.23-no.2-pp.127
Language	English
Access Restriction	Open
Subject Keyword	3 Tesla Magnetic Resonance Imaging CPU (central processing unit of computer system) CUDA Central processing unit Computation (action) Data structure Evidence of Contractor Compliance Document Graphics processing unit Malignant Fibrous Histiocytoma Markov chain Markov model Mathematics Matrix multiplication Nvidia Tesla Parallel computing Queueing theory Sparse matrix Tesla - unit
Content Type	Text
Resource Type	Article

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