TY - GEN
T1 - Parallel Computing Implementation of Marine Heat Waves Detection
AU - Pangestu, Ade Rachmat J.
AU - Kurniawan, Riski
AU - Swardiana, I. Wayan Aditya
AU - Abdurrouf,
AU - Latifah, Arnida L.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Marine heat waves (MHW) are anomaly increases in sea surface temperature that can negatively impact marine life, such as the mass death of marine creatures. Therefore, MHW detection is required to determine areas whose high potential risk for MHW. Extreme event detection, such as MHW, is a computing challenge as it is costly and needs huge memory. Basically, MHW detection is calculated individually for each location, which is inefficient for large domain. This study aims to accelerate MHW detection by implementing a parallel computation technique. The parallel computation of MHW detection combines the MHW Python library proposed by Oliver et al., 201S and the joblib library based on pipelining in Python. We propose the parallelization in a single and nested loop and evaluate the performance of the parallelization in single and multi-nodes. We found that the proposed parallelization can efficiently accelerate the computation time in a single node up to 10 times. Moreover, the parallelization in the nested loop performs more efficiently than the single loop.
AB - Marine heat waves (MHW) are anomaly increases in sea surface temperature that can negatively impact marine life, such as the mass death of marine creatures. Therefore, MHW detection is required to determine areas whose high potential risk for MHW. Extreme event detection, such as MHW, is a computing challenge as it is costly and needs huge memory. Basically, MHW detection is calculated individually for each location, which is inefficient for large domain. This study aims to accelerate MHW detection by implementing a parallel computation technique. The parallel computation of MHW detection combines the MHW Python library proposed by Oliver et al., 201S and the joblib library based on pipelining in Python. We propose the parallelization in a single and nested loop and evaluate the performance of the parallelization in single and multi-nodes. We found that the proposed parallelization can efficiently accelerate the computation time in a single node up to 10 times. Moreover, the parallelization in the nested loop performs more efficiently than the single loop.
KW - computing
KW - extreme temperature
KW - joblib
KW - loop
KW - MHW
KW - parallel
UR - https://www.scopus.com/pages/publications/85175972889
U2 - 10.1109/IC3INA60834.2023.10285767
DO - 10.1109/IC3INA60834.2023.10285767
M3 - Conference contribution
AN - SCOPUS:85175972889
T3 - Proceedings - 2023 10th International Conference on Computer, Control, Informatics and its Applications: Exploring the Power of Data: Leveraging Information to Drive Digital Innovation, IC3INA 2023
SP - 436
EP - 439
BT - Proceedings - 2023 10th International Conference on Computer, Control, Informatics and its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2023
Y2 - 4 October 2023 through 5 October 2023
ER -