@inproceedings{19:gsp:pdp,
  title        = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},
  author       = {Maurizio Drocco and Paolo Viviani and Iacopo Colonnelli and Marco Aldinucci and Marco Grangetto},
  year         = 2019,
  booktitle    = {Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
  publisher    = {IEEE},
  address      = {Pavia, Italy},
  pages        = {9--16},
  doi          = {10.1109/EMPDP.2019.8671640},
  url          = {https://iris.unito.it/retrieve/handle/2318/1695315/488105/19_wavefront_PDP.pdf},
  abstract     = {The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.},
  date-modified = {2019-03-22 23:07:10 +0100},
  keywords     = {eigenvalues, wavefront, GPU, CUDA, linear algebra},
  bdsk-url-1   = {https://iris.unito.it/retrieve/handle/2318/1695315/488105/19_wavefront_PDP.pdf},
  bdsk-url-2   = {https://doi.org/10.1109/EMPDP.2019.8671640}
}

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