Accelerating Spectral Graph Analysis Through Wavefronts of Linear Algebra Operations

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 wavefron...

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Bibliographic Details
Main Authors: Drocco, Maurizio, Viviani, Paolo, Colonnelli, Iacopo, Aldinucci, Marco, Grangetto, Marco
Format: Conference Proceeding
Language:English
Subjects:
GPU
Graphics processing units
hybrid
Kernel
Libraries
linear algebra
Matrix decomposition
Symmetric matrices
wavefront
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Summary: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.
ISSN:2377-5750
DOI:10.1109/EMPDP.2019.8671640