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Title: Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems
—Exascale computing enables unprecedented, detailed and coupled scientific simulations which generate data on the order of tens of petabytes. Due to large data volumes, lossy compressors become indispensable as they enable better compression ratios and runtime performance than lossless compressors. Moreover, as (high-performance computing) HPC systems grow larger, they draw power on the scale of tens of megawatts. Data motion is expensive in time and energy. Therefore, optimizing compressor and data I/O power usage is an important step in reducing energy consumption to meet sustainable computing goals and stay within limited power budgets. In this paper, we explore efficient power consumption gains for the SZ and ZFP lossy compressors and data writing on a cloud HPC system while varying the CPU frequency, scientific data sets, and system architecture. Using this power consumption data, we construct a power model for lossy compression and present a tuning methodology that reduces energy overhead of lossy compressors and data writing on HPC systems by 14.3% on average. We apply our model and find 6.5 kJs, or 13%, of savings on average for 512GB I/O. Therefore, utilizing our model results in more energy efficient lossy data compression and I/O.  more » « less
Award ID(s):
1910197
NSF-PAR ID:
10357314
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Page Range / eLocation ID:
1118 to 1126
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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