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.

Accelerating spectral graph analysis through wavefronts of linear algebra operations

Maurizio Drocco;Paolo Viviani;COLONNELLI, IACOPO;Marco Aldinucci;Marco Grangetto
2019

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.
27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)
Pavia, Italy
13-15 February 2019
Proc. of the 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)
IEEE
9
16
978-1-7281-1644-0
https://ieeexplore.ieee.org/document/8671640
eigenvalues, wavefront, GPU, CUDA, linear algebra
Maurizio Drocco, Paolo Viviani, Iacopo Colonnelli, Marco Aldinucci, Marco Grangetto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1695315
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