SpMV on GPUs

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=== Publications ===
 
=== Publications ===
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N. Sedaghati, T. Mu, L. N. Pouchet, S. Parthasarathy, and P. Sadayappan, "Automatic Selection of Sparse Matrix Representation on GPUs", in Proc. ICS 2015 [http://dl.acm.org/citation.cfm?id=2751244 link].
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N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, and P. Sadayappan, "Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs", in Proc. Workshop on Parallel Programming for Analytics Applications (held with PPoPP), 2015 [http://dl.acm.org/citation.cfm?id=2726941 link].
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A. Ashari, N. Sedaghati, J. Eisenlohr, and P. Sadayappan, "A Model-Driven Blocking Strategy for Load Balanced Sparse Matrix-Vector Multiplication on GPUs", Journal of Parallel and Distributed Computing (JPDC) [http://www.sciencedirect.com/science/article/pii/S0743731514002081 link].
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A. Ashari, N. Sedaghati, J. Eisenlohr, S. Parthasarathy and P. Sadayappan, "Fast Sparse Matrix Multiplication on GPUs for Graph Applications", in Proc. SC 2014 [http://dl.acm.org/citation.cfm?id=2683679 link].
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A. Ashari, N. Sedaghati, J. Eisenlohr and P. Sadayappan, "An Efficient Two-Dimensional Blocking Mechanism for Sparse Matrix-Vector Multiplication on GPUs", in Proc. ICS 2014 [http://dl.acm.org/citation.cfm?id=2597678 link].
  
 
== Project Members ==
 
== Project Members ==

Revision as of 21:11, 26 September 2016

This research has focused on optimizing sparse matrix representations (i.e. storage formats) for data-parallel accelerators (i.e. GPUs). In addition, it is shown that no sparse matrix representation is consistently superior, with the best representation being dependent on the matrix sparsity patterns. The research then uses machine learning techniques to automatically select the best sparse representation for a given matrix.

Publications

N. Sedaghati, T. Mu, L. N. Pouchet, S. Parthasarathy, and P. Sadayappan, "Automatic Selection of Sparse Matrix Representation on GPUs", in Proc. ICS 2015 link.

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, and P. Sadayappan, "Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs", in Proc. Workshop on Parallel Programming for Analytics Applications (held with PPoPP), 2015 link.

A. Ashari, N. Sedaghati, J. Eisenlohr, and P. Sadayappan, "A Model-Driven Blocking Strategy for Load Balanced Sparse Matrix-Vector Multiplication on GPUs", Journal of Parallel and Distributed Computing (JPDC) link.

A. Ashari, N. Sedaghati, J. Eisenlohr, S. Parthasarathy and P. Sadayappan, "Fast Sparse Matrix Multiplication on GPUs for Graph Applications", in Proc. SC 2014 link.

A. Ashari, N. Sedaghati, J. Eisenlohr and P. Sadayappan, "An Efficient Two-Dimensional Blocking Mechanism for Sparse Matrix-Vector Multiplication on GPUs", in Proc. ICS 2014 link.

Project Members

  • Arash Ashari
  • Naser Sedaghati
  • John Eisenlohr
  • Louis-Noel Pouchet
  • P. Sadayappan
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