.png){kind=icon}
.png){kind=icon}
Return
Accelerating NWChem Coupled Cluster through dataflow-based execution
.png){kind=icon}
PRE
AI
DOI：10.1177/1094342016672543.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
PaRSEC
tasks
dataflow
DAG
PTG
NWChem
CCSD
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.1K
Citations:
1.3K