POSTER: Utilizing dataflow-based execution for coupled cluster methods

McCraw, Heike; Danalis, Anthony; Hérault, Thomas; Bosilca, George; Dongarra, Jack; Kowalski, Karol; Windus, Theresa L.

doi:10.1109/cluster.2014.6968738

Cited by 4 publications

(2 citation statements)

References 5 publications

(3 reference statements)

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“…The tasks can then be scheduled to run on available nodes and to migrate to nodes where the data is located having the effect of optimizing the job to the architecture. For example, PARSEC 45 has been used to reduce load imbalances in the coupled cluster component of NWChem 46 and is currently being used for similar purposes in the GAMESS code.…”

Section: T H Imentioning

confidence: 99%

Editorial: Modern Architectures and Their Impact on Electronic Structure Theory

Gordon

Windus

2020

Chem. Rev.

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Section: T H Imentioning

confidence: 99%

Editorial: Modern Architectures and Their Impact on Electronic Structure Theory

Gordon

Windus

2020

Chem. Rev.

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“…ACES III (Lotrich et al, 2008) is another method that has been used effectively to parallelize CC codes. In this work, the CC algorithms are designed in a domain-specific language called the Super Instruction Assembly Language (SIAL) (Deumens et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Accelerating NWChem Coupled Cluster through dataflow-based execution

Jagode

Danalis

Dongarra

2017

The International Journal of High Performance Computing Applica

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Numerical techniques used for describing many-body systems, such as the Coupled Cluster methods (CC) of the quantum chemistry package NWCHEM, are of extreme interest to the computational chemistry community in fields such as catalytic reactions, solar energy, and bio-mass conversion. In spite of their importance, many of these computationally intensive algorithms have traditionally been thought of in a fairly linear fashion, or are parallelized in coarse chunks. In this paper, we present our effort of converting the NWCHEM's CC code into a dataflow-based form that is capable of utilizing the task scheduling system PARSEC (Parallel Runtime Scheduling and Execution Controller): a software package designed to enable high-performance computing at scale. We discuss the modularity of our approach and explain how the PARSEC-enabled dataflow version of the subroutines seamlessly integrate into the NWCHEM codebase. Furthermore, we argue how the CC algorithms can be easily decomposed into finer-grained tasks (compared with the original version of NWCHEM); and how data distribution and load balancing are decoupled and can be tuned independently. We demonstrate performance acceleration by more than a factor of two in the execution of the entire CC component of NWCHEM, concluding that the utilization of dataflow-based execution for CC methods enables more efficient and scalable computation.

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