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DTSTART:19700308T020000
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DTSTAMP:20210402T160050Z
LOCATION:Track 2
DTSTART;TZID=America/New_York:20201117T140000
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UID:submissions.supercomputing.org_SC20_sess159_pap505@linklings.com
SUMMARY:Distributed-Memory DMRG via Sparse and Dense Parallel Tensor Contr
 actions
DESCRIPTION:Paper\n\nDistributed-Memory DMRG via Sparse and Dense Parallel
  Tensor Contractions\n\nLevy, Solomonik, Clark\n\nThe density matrix renor
 malization group (DMRG) algorithm is a powerful tool for solving eigenvalu
 e problems to model quantum systems. DMRG relies on tensor contractions an
 d dense linear algebra to compute properties of condensed matter physics s
 ystems. However, its efficient parallel implementation is challenging due 
 to limited concurrency, large memory footprint and tensor sparsity. We mit
 igate these problems by implementing two new parallel approaches that hand
 le block sparsity arising in DMRG, via Cyclops, a distributed memory tenso
 r contraction library. We benchmark their performance on two physical syst
 ems using the Blue Waters and Stampede2 supercomputers. Our DMRG performan
 ce is improved by up to 5.9x in runtime and 99x in processing rate over IT
 ensor, at roughly comparable computational resource use. This enables high
 er accuracy calculations via larger tensors for quantum state approximatio
 n. We demonstrate that despite having limited concurrency, DMRG is weakly 
 scalable with the use of efficient parallel tensor contraction mechanisms.
 \n\nTag: Algorithms, Applications, Sparse Computation\n\nRegistration Cate
 gory: Tech Program Reg Pass
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