Accelerating the LOBPCG method on GPUs using a blocked sparse matrix vector product

@inproceedings{Anzt2015AcceleratingTL,
  title={Accelerating the LOBPCG method on GPUs using a blocked sparse matrix vector product},
  author={Hartwig Anzt and Stanimire Tomov and Jack J. Dongarra},
  booktitle={SpringSim},
  year={2015}
}
This paper presents a heterogeneous CPU-GPU implementation for a sparse iterative eigensolver – the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG). For the key routine generating the Krylov search spaces via the product of a sparse matrix and a block of vectors, we propose a GPU kernel based on a modified sliced ELLPACK format. Blocking a set of vectors and processing them simultaneously accelerates the computation of a set of consecutive SpMVs significantly. Comparing the… CONTINUE READING
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