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Title: Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures

Abstract

Patient-specific hemodynamic simulations have the potential to greatly improve both the diagnosis and treatment of a variety of vascular diseases. Portability will enable wider adoption of computational fluid dynamics (CFD) applications in the biomedical research community and targeting to platforms ideally suited to different vascular regions. In this work, we present a case study in performance portability that assesses (1) the ease of porting an MPI application optimized for one specific architecture to new platforms using variants of hybrid MPI+X programming models; (2) performance portability seen when simulating blood flow in three different vascular regions on diverse heterogeneous architectures; (3) model-based performance prediction for future architectures; and (4) performance scaling of the hybrid MPI+X programming on parallel heterogeneous systems. We discuss the lessons learned in porting HARVEY, a massively parallel CFD application, from traditional multicore CPUs to diverse heterogeneous architectures ranging from NVIDIA/AMD GPUs to Intel MICs and Altera FPGAs.

Authors:
ORCiD logo [1];  [1];  [2]; ORCiD logo [1]
  1. ORNL
  2. Duke university Duhram, NC
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1550724
Alternate Identifier(s):
OSTI ID: 1547683
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Parallel and Distributed Computing
Additional Journal Information:
Journal Volume: 129; Journal Issue: 0
Country of Publication:
United States
Language:
English

Citation Formats

Lee, Seyong, Gounley, John, Randles, Amanda, and Vetter, Jeffrey S. Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures. United States: N. p., 2019. Web. doi:10.1016/j.jpdc.2019.02.005.
Lee, Seyong, Gounley, John, Randles, Amanda, & Vetter, Jeffrey S. Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures. United States. doi:10.1016/j.jpdc.2019.02.005.
Lee, Seyong, Gounley, John, Randles, Amanda, and Vetter, Jeffrey S. Fri . "Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures". United States. doi:10.1016/j.jpdc.2019.02.005.
@article{osti_1550724,
title = {Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures},
author = {Lee, Seyong and Gounley, John and Randles, Amanda and Vetter, Jeffrey S.},
abstractNote = {Patient-specific hemodynamic simulations have the potential to greatly improve both the diagnosis and treatment of a variety of vascular diseases. Portability will enable wider adoption of computational fluid dynamics (CFD) applications in the biomedical research community and targeting to platforms ideally suited to different vascular regions. In this work, we present a case study in performance portability that assesses (1) the ease of porting an MPI application optimized for one specific architecture to new platforms using variants of hybrid MPI+X programming models; (2) performance portability seen when simulating blood flow in three different vascular regions on diverse heterogeneous architectures; (3) model-based performance prediction for future architectures; and (4) performance scaling of the hybrid MPI+X programming on parallel heterogeneous systems. We discuss the lessons learned in porting HARVEY, a massively parallel CFD application, from traditional multicore CPUs to diverse heterogeneous architectures ranging from NVIDIA/AMD GPUs to Intel MICs and Altera FPGAs.},
doi = {10.1016/j.jpdc.2019.02.005},
journal = {Journal of Parallel and Distributed Computing},
number = 0,
volume = 129,
place = {United States},
year = {2019},
month = {3}
}

Journal Article:
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This content will become publicly available on March 1, 2020
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