TY - GEN
T1 - Performance optimisation of the parallel CFD code MGLET across different HPC platforms
AU - Sakai, Yoshiyuki
AU - Mendez, Sandra
AU - Strandenes, Håkon
AU - Ohlerich, Martin
AU - Pasichnyk, Igor
AU - Allalen, Momme
AU - Manhart, Michael
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/6/12
Y1 - 2019/6/12
N2 - This paper presents the optimisation techniques implemented to run across four HPC platforms the finite-volume computational fluid dynamics (CFD) code MGLET (Multi Grid Large Eddy Turbulence). We analysed and applied refactoring to the parallel communication routines, and reduced the memory footprint significantly, resulting in a substantial improvement of the parallel-scaling capability and in an increase of the maximum number of degrees of freedom for applications. Data structures and files layout were redesigned for implementing parallel I/O in HDF5. The new parallel I/O strategy results in a considerable increase in the average data transfer rate compared with the former serial implementation. An I/O pattern analysis and detailed I/O profiling of the new implementation were then conducted and further performance improvement was achieved by increasing the size of I/O requests and reducing the number of I/O processes. We compare the improved parallel-scaling capability of MGLET on different architectures using representative CFD application test cases.
AB - This paper presents the optimisation techniques implemented to run across four HPC platforms the finite-volume computational fluid dynamics (CFD) code MGLET (Multi Grid Large Eddy Turbulence). We analysed and applied refactoring to the parallel communication routines, and reduced the memory footprint significantly, resulting in a substantial improvement of the parallel-scaling capability and in an increase of the maximum number of degrees of freedom for applications. Data structures and files layout were redesigned for implementing parallel I/O in HDF5. The new parallel I/O strategy results in a considerable increase in the average data transfer rate compared with the former serial implementation. An I/O pattern analysis and detailed I/O profiling of the new implementation were then conducted and further performance improvement was achieved by increasing the size of I/O requests and reducing the number of I/O processes. We compare the improved parallel-scaling capability of MGLET on different architectures using representative CFD application test cases.
KW - CFD
KW - High performance computing
KW - Parallel I/O
KW - Parallel optimization
UR - http://www.scopus.com/inward/record.url?scp=85068736963&partnerID=8YFLogxK
U2 - 10.1145/3324989.3325716
DO - 10.1145/3324989.3325716
M3 - Conference contribution
AN - SCOPUS:85068736963
T3 - Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2019
BT - Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2019
PB - Association for Computing Machinery, Inc
T2 - 6th Platform for Advanced Scientific Computing Conference, PASC 2019
Y2 - 12 June 2019 through 14 June 2019
ER -