A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Introduction to Algorithms
Automatic performance tuning of sparse matrix kernels
Automatic performance tuning of sparse matrix kernels
UbiCrawler: a scalable fully distributed web crawler
Software—Practice & Experience
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Concurrent number cruncher: a GPU implementation of a general sparse linear solver
International Journal of Parallel, Emergent and Distributed Systems
An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
Proceedings of the 36th annual international symposium on Computer architecture
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
An adaptive performance modeling tool for GPU architectures
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Model-driven autotuning of sparse matrix-vector multiply on GPUs
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Parallel PageRank computation using GPUs
Proceedings of the Third Symposium on Information and Communication Technology
Iterative statistical kernels on contemporary GPUs
International Journal of Computational Science and Engineering
SMAT: an input adaptive auto-tuner for sparse matrix-vector multiplication
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
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Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real web graph data, we show how our representation scheme, coupled with a novel tiling algorithm, can yield significant benefits over the current state of the art GPU efforts on a number of core data mining algorithms such as PageRank, HITS and Random Walk with Restart.