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Title: Scalability of Hybrid SpMV with Hypergraph Partitioning and Vertex Delegation for Communication Avoidance
Communication overhead has been identified as the primary factor in overall performance degradation for sparse and irregular problems such as SpMV. Many works have shown significant communication reductions, but only for matrices with specific characteristics and by dramatically reworking the computations. This study develops and evaluates a communication avoiding distributed heterogeneous implementation for strong scaling of SpMV on the Sierra supercomputer architecture. To address the far bigger matrices characteristic of real problems, we utilize a hypergraph partitioning package HYPE to determine workload distribution and reduce inter-node communication. Additionally we investigated the performance impact of performing hypergraph partitioning on scale free graphs which had undergone a vertex delegation pre-processing step. We achieved up to 97% reduction in average message size per process at scale when using the HYPE partitioner. Despite this we show how optimizing SpMV on existing GPU architectures does provide increased computational performance, yet does not address the dominant communication overhead factor at scale despite attempts to avoid communication where possible.
Authors:
;
Award ID(s):
1822939
Publication Date:
NSF-PAR ID:
10298914
Journal Name:
International Conference on High Performance Computing & Simulation (HPCS 2020)
Sponsoring Org:
National Science Foundation
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