Scientific simulations run by high-performance computing (HPC) systems produce a large amount of data, which causes an extreme I/O bottleneck and a huge storage burden. Applying compression techniques can mitigate such overheads through reducing the data size. Unlike traditional lossless compressions, error-controlled lossy compressions, such as SZ, ZFP, and DCTZ, designed for scientists who demand not only high compression ratios but also a guarantee of certain degree of precision, is coming into prominence. While rate-distortion efficiency of recent lossy compressors, especially the DCT-based one, is promising due to its high-compression encoding, the overall coding architecture is still conservative, necessitating the quantization that strikes a balance between different encoding possibilities and varying rate-distortions. In this paper, we aim to improve the performance of DCT-based compressor, namely DCTZ, by optimizing the quantization model and encoding mechanism. Specifically, we propose a bit-efficient quantizer based on the DCTZ framework, develop a unique ordering mechanism based on the quantization table, and extend the encoding index. We evaluate the performance of our optimized DCTZ in terms of rate-distortion using real-world HPC datasets. Our experimental evaluations demonstrate that, on average, our proposed approach can improve the compression ratio of the original DCTZ by 1.38x. Moreover, combined with the extended encoding mechanism, the optimized DCTZ shows a competitive performance with state-of-the-art lossy compressors, SZ and ZFP.
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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.
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- Award ID(s):
- 1910197
- NSF-PAR ID:
- 10357314
- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPSW55747.2022.00184
