TY - GEN
T1 - Task-parallel analysis of molecular dynamics trajectories
AU - Paraskevakos, Ioannis
AU - Chantzialexiou, George
AU - Luckow, Andre
AU - Cheatham, Thomas E.
AU - Khoshlessan, Mahzad
AU - Beckstein, Oliver
AU - Fox, Geoffrey C.
AU - Jha, Shantenu
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/8/13
Y1 - 2018/8/13
N2 - Different parallel frameworks for implementing data analysis applications have been proposed by the HPC and Big Data communities. In this paper, we investigate three task-parallel frameworks: Spark, Dask and RADICAL-Pilot with respect to their ability to support data analytics on HPC resources and compare them to MPI. We investigate the data analysis requirements of Molecular Dynamics (MD) simulations which are significant consumers of supercomputing cycles, producing immense amounts of data. A typical large-scale MD simulation of a physical system of O(100k) atoms over secs can produce from O(10) GB to O(1000) GBs of data. We propose and evaluate different approaches for parallelization of a representative set of MD trajectory analysis algorithms, in particular the computation of path similarity and leaflet identification. We evaluate Spark, Dask and RADICAL-Pilot with respect to their abstractions and runtime engine capabilities to support these algorithms. We provide a conceptual basis for comparing and understanding different frameworks that enable users to select the optimal system for each application. We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
AB - Different parallel frameworks for implementing data analysis applications have been proposed by the HPC and Big Data communities. In this paper, we investigate three task-parallel frameworks: Spark, Dask and RADICAL-Pilot with respect to their ability to support data analytics on HPC resources and compare them to MPI. We investigate the data analysis requirements of Molecular Dynamics (MD) simulations which are significant consumers of supercomputing cycles, producing immense amounts of data. A typical large-scale MD simulation of a physical system of O(100k) atoms over secs can produce from O(10) GB to O(1000) GBs of data. We propose and evaluate different approaches for parallelization of a representative set of MD trajectory analysis algorithms, in particular the computation of path similarity and leaflet identification. We evaluate Spark, Dask and RADICAL-Pilot with respect to their abstractions and runtime engine capabilities to support these algorithms. We provide a conceptual basis for comparing and understanding different frameworks that enable users to select the optimal system for each application. We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
KW - Data analytics
KW - MD analysis
KW - MD simulations analysis
KW - Task-parallel
UR - http://www.scopus.com/inward/record.url?scp=85054786481&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054786481&partnerID=8YFLogxK
U2 - 10.1145/3225058.3225128
DO - 10.1145/3225058.3225128
M3 - Conference contribution
AN - SCOPUS:85054786481
SN - 9781450365109
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 47th International Conference on Parallel Processing, ICPP 2018
PB - Association for Computing Machinery
T2 - 47th International Conference on Parallel Processing, ICPP 2018
Y2 - 14 August 2018 through 16 August 2018
ER -