Graph Analytics in the Exascale Era

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#combinatorial algorithms #exascale applications #graph algorithms #ExaGraph #multi-GPI systems # parallel systems

Dr. Mahantesh Halappanavar, a chief computer scientist at PNNL, will present a brief overview of the latest work on multi-GPU systems for prototypical graph problems and demonstrate substantial gains in performance.

Combinatorial algorithms in general, and graph algorithms in particular, play a critical enabling role in numerous scientific applications. The irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems.

To address the challenges, ExaGraph, the co-design center on combinatorial algorithms, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a set of exascale applications, targeting accelerator-enabled pre-exascale and exascale systems.

Dr. Mahantesh Halappanavar, a chief computer scientist at PNNL, presents a brief overview of the latest work on multi-GPU systems for two prototypical graph problems — graph clustering and influence maximization — and demonstrates substantial gains in performance.

We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

This presentation was given on January 20, 2022. It was co-hosted by the Boise Computer Society, the Spokane Computer Society, and the Pikes Peak Computer Society.

Dr. Mahantesh Halappanavar, a chief computer scientist at PNNL, will present a brief overview of the latest work on multi-GPU systems for prototypical graph problems and demonstrate substantial gains in performance.

Combinatorial algorithms in general, and graph algorithms in particular, play a critical...

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