Analyzing the Performance and Accuracy of Lossy Checkpointing on Sub-Iteration of NWChem

Reza, Tasmia; Calhoun, Jon C.; Keipert, Kristopher; Di, Sheng; Cappello, Franck

doi:10.1109/drbsd-549595.2019.00009

Cited by 3 publications

(2 citation statements)

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“…Existing lossy compressors mainly focus on optimizing from three aspects: compression ratio (i.e., storage reduction ratio), and compression speed (a.k.a., throughput), and reconstructed data quality based on statistical metrics such as PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure). However, only few works [20], [24], [25] have studied the impact of compression error on HPC applications and none of them have systematically studied how compression errors propagate in any HPC program. This is because unlike traditional resilience and fault tolerance community that has many fault injection tools (such as PinFI [26], LLFI [23], an TensorFI [27]) to investigate how software applications are resilient to hardware errors, the HPC community is missing an efficient fault injection tool for lossy compression errors, which can help lossy compressor developers and users to understand the compression error impact on specific HPC programs.…”

Section: Research Motivationmentioning

confidence: 99%

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LCFI: A Fault Injection Tool for Studying Lossy Compression Error Propagation in HPC Programs

Shan

Shamji

Tian

et al. 2020

Preprint

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Error-bounded lossy compression is becoming more and more important to today's extreme-scale HPC applications because of the ever-increasing volume of data generated. Errorbounded lossy compressors have been widely used in in situ visualization, data stream intensity reduction, storage reduction, I/O performance improvement, checkpoint/restart acceleration, memory footprint reduction, etc.Although many works have optimized ratio, quality, and performance for different error-bounded lossy compressors, there is none of the existing works attempting to systematically understand the impact of lossy compression errors on HPC application due to error propagation.In this paper, we propose and develop a lossy compression fault injection tool, called LCFI. To the best of our knowledge, this is the first tool that help both lossy compressor developers and users to systematically and comprehensively understand the impact of lossy compression errors on any given HPC programs.The contributions of this work are threefold.(1) We propose an efficient approach to inject lossy compression faults according to a statistical analysis of compression errors for different stateof-the-art error-bounded lossy compressors. (2) We build a fault injector which is highly applicable, customizable, ease-to-use in generating top-down comprehensive results. We use a simple sample program to demonstrate the use of LCFI. (3) We evaluate LCFI on four representative HPC benchmark programs with different lossy compression errors and derive several important insights based on our observations.

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Section: Research Motivationmentioning

confidence: 99%

“…In other words, the propagation of compression errors in HPC programs has not been well studied and understood. Therefore, current lossy compression methods may lead to unacceptably inaccurate results for scientific discovery [18]- [20] based on the corrupted program output.…”

Section: Introductionmentioning

confidence: 99%