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.

Abstract-The aim of this paper is to investigate the impact of thread affinity on computing performance for matrix factorization on shared memory multicore systems with hierarchical memory. We consider two parallel block matrix factorizations (LU and WZ) and employ thread affinity to improve their performance. We study decomposition without pivoting and we compare differences between various affinity strategies for diagonally dominant matrices. Our results show that the choice of thread affinity has the measurable impact on the performance of the matrice factorizations.

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