TY - GEN
T1 - Cloud Automation to Run Large-Scale Quantum Chemical Simulations
AU - Alrayhi, N.
AU - Salah, K.
AU - Al-Kork, N.
AU - Bentiba, A.
AU - Trabelsi, Z.
AU - Azad, M. A.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/8
Y1 - 2019/1/8
N2 - Scientists and researchers often need to run midscale to large-scale scientific computations on powerful computing machines or platforms. Even with today's available powerful computing platforms, many of these computations still take enormous runtime. With the advent of cloud computing technology, scientists are now able to reduce significantly the computational time by outsourcing computation functions to the cloud systems. We show in this paper how AWS (Amazon Web Services) cloud computing platform can be automated in executing large-scale computationally expensive scientific experiments. Specifically, we show how quantum chemistry simulations can be executed in parallel and in a cluster-based fashion using the publicly available and popular Amazon cloud platform. With Amazon cloud, we were able to reduce the computation time by almost five orders of magnitude. In addition, the paper offers many important useful guidelines, scripts, and commands for scientists and researchers on how to automate and execute parallel and cluster-based scientific jobs on any cloud platform.
AB - Scientists and researchers often need to run midscale to large-scale scientific computations on powerful computing machines or platforms. Even with today's available powerful computing platforms, many of these computations still take enormous runtime. With the advent of cloud computing technology, scientists are now able to reduce significantly the computational time by outsourcing computation functions to the cloud systems. We show in this paper how AWS (Amazon Web Services) cloud computing platform can be automated in executing large-scale computationally expensive scientific experiments. Specifically, we show how quantum chemistry simulations can be executed in parallel and in a cluster-based fashion using the publicly available and popular Amazon cloud platform. With Amazon cloud, we were able to reduce the computation time by almost five orders of magnitude. In addition, the paper offers many important useful guidelines, scripts, and commands for scientists and researchers on how to automate and execute parallel and cluster-based scientific jobs on any cloud platform.
KW - Cloud Computing
KW - Cluster Computing
KW - Parallel Processing
KW - Scientific Computation
UR - http://www.scopus.com/inward/record.url?scp=85062386025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062386025&partnerID=8YFLogxK
U2 - 10.1109/INNOVATIONS.2018.8606027
DO - 10.1109/INNOVATIONS.2018.8606027
M3 - Conference contribution
AN - SCOPUS:85062386025
T3 - Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
SP - 75
EP - 80
BT - Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Innovations in Information Technology, IIT 2018
Y2 - 18 November 2018 through 19 November 2018
ER -