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|Title||Tensor Contraction on Distributed Hybrid Architectures using a Task-Based Runtime System|
|Publication Type||Tech Report|
|Year of Publication||2018|
|Authors||Bosilca, G., D. Genet, R. Harrison, T. Herault, M. Mahdi Javanmard, C. Peng, and E. Valeev|
|Technical Report Series Title||Innovative Computing Laboratory Technical Report|
|Institution||University of Tennessee|
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.