Efficient PageRank and SpMV Computation on AMD GPUs

Wu, Tianji; Wang, Bo; Shan, Yi; Feng, Yan; Wang, Yu; Xu, Ning

doi:10.1109/icpp.2010.17

Cited by 41 publications

(16 citation statements)

References 7 publications

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“…The work considers the Stochastic Gradient Descent algorithm, discussing a parallel solution, its performance gain, and variations in accuracy. A study on efficient execution of PageRank algorithm on AMD GPUs is presented by Wu et al [13]. They analyze the characteristic of the sparse matrices used in PageRank, and introduce a fast sparse matrix-vector multiplication (SpMV) implementation using a modified Compressed Sparse Row (CSR) format.…”

Section: B General Purpose Computing On Gpus (Gpgpus)mentioning

confidence: 99%

Efficient Image Re-Ranking Computation on GPUs

Pedronette

Torres

Borin

et al. 2012

2012 IEEE 10th International Symposium on Parallel and Distributed Processing With Applications

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The huge growth of image collections and multimedia resources available is remarkable. One of the most common approaches to support image searches relies on the use of Content-Based Image Retrieval (CBIR) systems. CBIR systems aim at retrieving the most similar images in a collection, given a query image. Since the effectiveness of those systems is very dependent on the accuracy of ranking approaches, reranking algorithms have been proposed to exploit contextual information and improve the effectiveness of CBIR systems. Image re-ranking algorithms typically consider the relationship among every image in a given dataset when computing the new ranking. This approach demands a huge amount of computational power, which may render it prohibitive on very large data sets. In order to mitigate this problem, we propose using the computational power of Graphics Processing Units (GPU) to speedup the computation of image re-ranking algorithms. GPUs are fast emerging and relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. In this paper, we propose a parallel implementation of an image re-ranking algorithm designed to fit the computational model of GPUs. Experimental results demonstrate that relevant performance gains can be obtained by our approach.

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Section: B General Purpose Computing On Gpus (Gpgpus)mentioning

confidence: 99%

Efficient Image Re-Ranking Computation on GPUs

Pedronette

Torres

Borin

et al. 2012

2012 IEEE 10th International Symposium on Parallel and Distributed Processing With Applications

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“…Using the CSR format, Wu et al [5] experimented with different thread mappings to compute SpMV on AMD GPUs. Their results show that a mixture of one thread and multiple threads per row achieves the highest number of floating point operations per second for selected matrices.…”

Section: Literature Reviewmentioning

confidence: 99%

A memory transaction model for Sparse Matrix-Vector multiplications on GPUs

Keklikian

Langlois

Savaria

2014

2014 IEEE 12th International New Circuits and Systems Conference (NEWCAS)

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The Sparse Matrix-Vector multiplication (SpMV) is an algorithm used in many fields. Since the introduction of CUDA and general purpose programming on GPUs, several efforts to optimize it have been reported. SpMV optimization is complex due to irregular memory accesses depending on the nonzero element distribution of the matrix. In this paper, we propose a model that predicts the number of memory transactions of SpMV for a matrix stored in the CSR format. With the number of memory transactions known in advance, the performance and the execution time can be estimated. The model can be used to select the best suited CUDA implementation for sparse matrices for a given application domain. Predicted results from the model are within 7.5% for the matrices of more than 1000 rows that we have tested on the NVIDIA Tesla K20c and GeForce GTX 670.

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“…This is more logical, as the data needed to solve the system of equation is generated and already resides in the GPU memory, and no transfer cost is to be needed. of SpMV kernels on graphics hardware has been the subject of many recent researches [23][24][25][26][27][28][29][30][31][32][33]. It has been shown that the naïve implementation of SpMV kernel is quite ineffective on such platforms [23].…”

Section: Solution Of Implicit Pressure Equationmentioning

confidence: 99%

A portable OpenCL-based unstructured edge-based finite element Navier–Stokes solver on graphics hardware

2013

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The rise of GPUs in modern high-performance systems increases the interest in porting portion of codes to such hardware. The current paper aims to explore the performance of a portable state-of-the-art FE solver on GPU accelerators. Performance evaluation is done by comparing with an existing highly-optimized OpenMP version of the solver.Code portability is ensured by writing the program using the OpenCL 1.1 specifications, while performance portability is sought through an optimization step performed at the beginning of the calculations to find out the optimal parameter set for the solver.The results show that the new implementation can be several times faster than the OpenMP version.

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Efficient PageRank and SpMV Computation on AMD GPUs

Cited by 41 publications

References 7 publications

Efficient Image Re-Ranking Computation on GPUs

Efficient Image Re-Ranking Computation on GPUs

A memory transaction model for Sparse Matrix-Vector multiplications on GPUs

A portable OpenCL-based unstructured edge-based finite element Navier–Stokes solver on graphics hardware

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