TY - GEN
T1 - Data decomposition for code parallelization in practice
T2 - 15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013
AU - Meade, Anne
AU - Deeptimahanti, Deva Kumar
AU - Johnston, Michael
AU - Buckley, Jim
AU - Collins, J. J.
PY - 2014
Y1 - 2014
N2 - Parallelizing serial software systems in order to run in a High Performance Computing (HPC) environment presents many challenges to developers. In particular, the extant literature suggests the task of decomposing large-scale data applications is particularly complex and time-consuming. In order to take stock of the state of practice of data decomposition in HPC, we conducted a two-phased study. Firstly, using focus group methodology we conducted an exploratory study at a software laboratory with an established track record in HPC. Based on the findings of this first phase, we designed a survey to assess the state of practice among experts in this field around the world. Our study shows that approximately 75% of parallelized applications use some form of data decomposition. Furthermore, data decomposition was found to be the most challenging phase in the parallelization process, consuming approximately 40% of the total time. A key finding of our study is that experts do not use any of the available tools and formal representations, and in fact, are not aware of them. We discuss why existing tools have not been adopted in industry and based on our findings, provide a number of recommendations for future tool support.
AB - Parallelizing serial software systems in order to run in a High Performance Computing (HPC) environment presents many challenges to developers. In particular, the extant literature suggests the task of decomposing large-scale data applications is particularly complex and time-consuming. In order to take stock of the state of practice of data decomposition in HPC, we conducted a two-phased study. Firstly, using focus group methodology we conducted an exploratory study at a software laboratory with an established track record in HPC. Based on the findings of this first phase, we designed a survey to assess the state of practice among experts in this field around the world. Our study shows that approximately 75% of parallelized applications use some form of data decomposition. Furthermore, data decomposition was found to be the most challenging phase in the parallelization process, consuming approximately 40% of the total time. A key finding of our study is that experts do not use any of the available tools and formal representations, and in fact, are not aware of them. We discuss why existing tools have not been adopted in industry and based on our findings, provide a number of recommendations for future tool support.
KW - Empirical Study
KW - High Performance Computing
KW - Industry survey
KW - Tool support
UR - http://www.scopus.com/inward/record.url?scp=84903975462&partnerID=8YFLogxK
U2 - 10.1109/HPCC.and.EUC.2013.110
DO - 10.1109/HPCC.and.EUC.2013.110
M3 - Conference contribution
AN - SCOPUS:84903975462
SN - 9780769550886
T3 - Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013
SP - 754
EP - 761
BT - Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013
PB - IEEE Computer Society
Y2 - 13 November 2013 through 15 November 2013
ER -