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1. ARC: An Automated Approach to Resiliency for Lossy Compressed Data via Error Correcting Codes

   https://doi.org/10.1145/3431379.3460638  

   Fulp, Dakota; Poulos, Alexandra; Underwood, Robert; Calhoun, Jon C. (June 2020, HPDC '21: Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing)

   null (Ed.)

   Progress in high-performance computing (HPC) systems has led to complex applications that stress the I/O subsystem by creating vast amounts of data. Lossy compression reduces data size considerably, but a single error renders lossy compressed data unusable. This sensitivity stems from the high information content per bit in compressed data and is a critical issue as soft errors that cause bit-flips have become increasingly commonplace in HPC systems. While many works have improved lossy compressor performance, few have sought to address this critical weakness. This paper presents ARC: Automated Resiliency for Compression. Given user-defined constraints on storage, throughput, and resiliency, ARC automatically determines the optimal error-correcting code (ECC) configuration before encoding data. We conduct an extensive fault injection study to fully understand the effects of soft errors on lossy compressed data and how to best protect it. We evaluate ARC's scalability, performance, resiliency, and ease of use. We find on a 40 core node that encoding and decoding demonstrate throughput up to 3730 MB/s and 3602 MB/s. ARC also detects and corrects multi-bit errors with a tunable overhead in terms of storage and throughput. Finally, we display the ease of using ARC and how to consider a systems failure rate when determining the constraints. 

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2. Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations

   https://doi.org/10.1109/CLUSTER.2019.8891052  

   Triantafyllides, Pavlo; Reza, Tasmia; Calhoun, Jon C. (September 2019, 2019 IEEE International Conference on Cluster Computing (CLUSTER))

   Lossy compression algorithms are effective tools to reduce the size of high-performance computing data sets. As established lossy compressors such as SZ and ZFP evolve, they seek to improve the compression/decompression bandwidth and the compression ratio. Algorithm improvements may alter the spatial distribution of errors in the compressed data even when using the same error bound and error bound type. If HPC applications are to compute on lossy compressed data, application users require an understanding of how the performance and spatial distribution of error changes. We explore how spatial distributions of error, compression/decompression bandwidth, and compression ratio change for HPC data sets from the applications PlasComCM and Nek5000 between various versions of SZ and ZFP. In addition, we explore how the spatial distribution of error impacts application correctness when restarting from lossy compressed checkpoints. We verify that known approaches to selecting error tolerances for lossy compressed checkpointing are robust to compressor selection and in the face of changes in the distribution of error. 

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3. SZOps: Scalar Operations for Error-bounded Lossy Compressor for Scientific Data

   https://doi.org/10.1109/SCW63240.2024.00042  

   Agarwal, Tripti; Di, Sheng; Huang, Jiajun; Huang, Yafan; Gopalakrishnan, Ganesh; Underwood, Robert; Zhao, Kai; Liang, Xin; Li, Guanpeng; Cappello, Franck (November 2024, IEEE)

   Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific applications while effectively controlling the data distortion based on user-specified error bound. In many real-world use cases, users must perform computational operations on the compressed data. However, none of the existing error-bounded lossy compressors support operations, inevitably resulting in undesired decompression costs. In this paper, we propose a novel error-bounded lossy compressor (called SZOps), which supports not only error-bounding features but efficient computations (including negation, scalar addition, scalar multiplication, mean, variance, etc.) on the compressed data without the complete decompression step, which is the first attempt to the best of our knowledge. We develop several optimization strategies to maximize the overall compression ratio and execution performance. We evaluate SZOps compared to other state-of-the-art lossy compressors based on multiple real-world scientific application datasets. 

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

   https://doi.org/10.1109/DRBSD-549595.2019.00009  

   Reza, Tasmia; Calhoun, Jon; Keipert, Kristopher; Di, Sheng; Cappello, Franck (November 2019, 10.1109/DRBSD-549595.2019.00009)

   Future exascale systems are expected to be characterized by more frequent failures than current petascale systems. This places increased importance on the application to minimize the amount of time wasted due to recompution when recovering from a checkpoint. Typically HPC application checkpoint at iteration boundaries. However, for applications that have a high per-iteration cost, checkpointing inside the iteration limits the amount of re-computation. This paper analyzes the performance and accuracy of using lossy compressed check-pointing in the computational chemistry application NWChem. Our results indicate that lossy compression is an effective tool for reducing the sub-iteration checkpoint size. Moreover, compression error tolerances that yield acceptable deviation in accuracy and iteration count are quantified. 

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5. Bit-Error Aware Quantization for DCT-based Lossy Compression

   Zhang, Jialing; Chen, Jiaxi; Moon, Aekyeung; Zhuo, Xiaoyan; Son, Seung Woo (September 2020, IEEE Conference on High Performance Extreme Computing)

   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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