Tensor Contraction on Distributed Hybrid Architectures using a Task-Based Runtime System

TitleTensor Contraction on Distributed Hybrid Architectures using a Task-Based Runtime System
Publication TypeTech Report
Year of Publication2018
AuthorsBosilca, G., D. Genet, R. Harrison, T. Herault, M. Mahdi Javanmard, C. Peng, and E. Valeev
Technical Report Series TitleInnovative Computing Laboratory Technical Report
NumberICL-UT-18-13
Date Published12-2018
InstitutionUniversity of Tennessee
Abstract

The needs for predictive simulation of electronic structure in chemistry and materials science calls for fast/reduced-scaling formulations of quantum n-body methods that replace the traditional dense tensors with element-, block-, rank-, and block-rank-sparse (data-sparse) tensors. The resulting, highly irregular data structures are a poor match to imperative, bulk-synchronous parallel programming style due to the dynamic nature of the problem and to the lack of clear domain decomposition to guarantee a fair load-balance. TESSE runtime and the associated programming model aim to support performance-portable composition of applications involving irregular and dynamically changing data. In this paper we report an implementation of irregular dense tensor contraction in a paradigmatic electronic structure application based on the TESSE extension of PaRSEC, a distributed hybrid task runtime system, and analyze the resulting performance on a distributed memory cluster of multi-GPU nodes. Unprecedented strong scaling and promising efficiency indicate a viable future for task-based programming of complete production-quality reduced scaling models of electronic structure.

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