@conference {,
	title = {Distributed-Memory Multi-GPU Block-Sparse Tensor Contraction for Electronic Structure},
	booktitle = {35th IEEE International Parallel \&  Distributed Processing Symposium (IPDPS 2021)},
	year = {2021},
	month = {2021-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Portland, OR},
	abstract = {Many domains of scientific simulation (chemistry, condensed matter physics, data science) increasingly eschew dense tensors for block-sparse tensors, sometimes with additional structure (recursive hierarchy, rank sparsity, etc.). Distributed-memory parallel computation with block-sparse tensorial data is paramount to minimize the time-tosolution (e.g., to study dynamical problems or for real-time analysis) and to accommodate problems of realistic size that are too large to fit into the host/device memory of a single node equipped with accelerators. Unfortunately, computation with such irregular data structures is a poor match to the dominant imperative, bulk-synchronous parallel programming model. In this paper, we focus on the critical element of block-sparse tensor algebra, namely binary tensor contraction, and report on an efficient and scalable implementation using the task-focused PaRSEC runtime. High performance of the block-sparse tensor contraction on the Summit supercomputer is demonstrated for synthetic data as well as for real data involved in electronic structure simulations of unprecedented size.},
	keywords = {block-sparse matrix multiplication, distributed-memory, Electronic structure, multi-GPU node, parsec, tensor contraction},
	url = {https://hal.inria.fr/hal-02970659/document},
	author = {Thomas Herault and Yves Robert and George Bosilca and Robert Harrison and Cannada Lewis and Edward Valeev and Jack Dongarra}
}
@techreport {1280,
	title = {Tensor Contraction on Distributed Hybrid Architectures using a Task-Based Runtime System},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-18-13},
	year = {2018},
	month = {2018-12},
	publisher = {University 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.},
	author = {George Bosilca and Damien Genet and Robert Harrison and Thomas Herault and Mohammad Mahdi Javanmard and Chong Peng and Edward Valeev}
}
