A brief summary on formalizing parallel tensor distributions redistributions and algorithm derivations.

Technical Report · Tue Sep 01 04:00:00 EDT 2015

DOI:https://doi.org/10.2172/1222973· OSTI ID:1222973

Schatz, Martin D. ^[1]; Kolda, Tamara G. ^[1]; van de Geijn, Robert ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Large-scale datasets in computational chemistry typically require distributed-memory parallel methods to perform a special operation known as tensor contraction. Tensors are multidimensional arrays, and a tensor contraction is akin to matrix multiplication with special types of permutations. Creating an efficient algorithm and optimized im- plementation in this domain is complex, tedious, and error-prone. To address this, we develop a notation to express data distributions so that we can apply use automated methods to find optimized implementations for tensor contractions. We consider the spin-adapted coupled cluster singles and doubles method from computational chemistry and use our methodology to produce an efficient implementation. Experiments per- formed on the IBM Blue Gene/Q and Cray XC30 demonstrate impact both improved performance and reduced memory consumption.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1222973
• Report Number(s): SAND--2015-8453; 607241
• Country of Publication: United States
• Language: English