Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure - Archive ouverte HAL Access content directly
Conference Papers Year :

Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure

(1) , (1, 2, 3) , (1) , (1, 4) , (1, 5) , (1, 6) , (1, 7)
1
2
3
4
5
6
7

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.). Distributedmemory parallel computation with block-sparse tensorial data is paramount to minimize the time-to-solution (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.
Fichier principal
Vignette du fichier
ipdps21.pdf (732.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03508930 , version 1 (03-01-2022)

Identifiers

Cite

Thomas Herault, Yves Robert, George Bosilca, Robert J Harrison, Cannada A Lewis, et al.. Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure. IPDPS 2021 - IEEE International Parallel and Distributed Processing Symposium, May 2021, Portland, OR, United States. pp.1-10, ⟨10.1109/IPDPS49936.2021.00062⟩. ⟨hal-03508930⟩
17 View
65 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More