CAST: Contraction Algorithm for Symmetric Tensors
Tensor contractions represent the most compute-intensive core kernels in ab initio computational quantum chemistry and nuclear physics. Symmetries in these tensor contractions makes them difficult to load balance and scale to large distributed systems. In this paper, we develop an efficient and scalable algorithm to contract symmetric tensors. We introduce a novel approach that avoids data redistribution in contracting symmetric tensors while also avoiding redundant storage and maintaining load balance. We present experimental results on two parallel supercomputers for several symmetric contractions that appear in the CCSD quantum chemistry method. We also present a novel approach to tensor redistribution that can take advantage of parallel hyperplanes when the initial distribution has replicated dimensions, and use collective broadcast when the final distribution has replicated dimensions, making the algorithm very efficient.
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- Conference: 43rd International Conference on Parallel Processing (ICPP 2014), September 9-12, 2014, Minneapolis, Minnesota, 261-272
- IEEE, Piscataway, NJ, United States(US).
- Research Org:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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- United States
- tensor contractions; symmetry; distributed memory