@inproceedings{10.1145/3624062.3624191,
author = {Yildirim, Izzet and Devarajan, Hariharan and Kougkas, Anthony and Sun, Xian-He and Mohror, Kathryn},
title = {IOMax: Maximizing Out-of-Core I/O Analysis Performance on HPC Systems},
year = {2023},
isbn = {9798400707858},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3624062.3624191},
doi = {10.1145/3624062.3624191},
abstract = {I/O analysis is an essential task for improving the performance of scientific applications on high-performance computing (HPC) systems. However, current analysis tools, which often use data drilling techniques (iterative exploration for deeper insights), treat every query independently and do not optimize column data for data-slicing (extracting specific data subsets), resulting in subpar querying performance. In this paper, we designed IOMax, a tool for efficient data drilling analysis on large-scale I/O traces. IOMax utilizes a novel query optimization technique to improve the query performance by 8.6x while reducing the memory footprint required for analysis by 11x. Additionally, it employs data transformation techniques to improve data-slicing performance by up to 11.4x. In conclusion, IOMax optimizes I/O analysis for scientific workflows on the Lassen supercomputer, resulting in up to 7x improvement.},
booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
pages = {1209–1215},
numpages = {7},
keywords = {Out-of-Core Analysis, HPC, Data Drilling, I/O Performance},
location = {Denver, CO, USA},
series = {SC-W '23}
}