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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. 

Gene Expression Matrices (GEMs) are a fundamental data type in the genomics domain. As the size and scope of genomics experiments increase, researchers are struggling to process large GEMs through downstream workflows with currently accepted practices. In this paper, we propose a methodology to reduce the size of GEMs using multiple approaches. Our method partitions data into discrete fields based on data type and employs state-of-the-art lossless and lossy compression algorithms to reduce the input data size. This work explores a variety of lossless and lossy compression methods to determine which methods work the best for each component of a GEM. We evaluate the accuracy of the compressed GEMs by running them through the Knowledge Independent Network Construction (KINC) workflow and comparing the quality of the resulting gene co-expression network with a lossless control to verify result fidelity. Results show that utilizing a combination of lossy and lossless compression results in compression ratios up to 9.77× on a Yeast GEM, while still preserving the biological integrity of the data. Usage of the compression methodology on the Cancer Cell Line Encyclopedia(CCLE) GEM resulted in compression ratios up to 9.26×. By using this methodology, researchers in the Genomics domain may be able to process previously inaccessible GEMs while realizing significant reduction in computational costs. 

Abstract Growing constraints on memory utilization, power consumption, and I/O throughput have increasingly become limiting factors to the advancement of high performance computing (HPC) and edge computing applications. IEEE‐754 floating‐point types have been the de facto standard for floating‐point number systems for decades, but the drawbacks of this numerical representation leave much to be desired. Alternative representations are gaining traction, both in HPC and machine learning environments. Posits have recently been proposed as a drop‐in replacement for the IEEE‐754 floating‐point representation. We survey the state‐of‐the‐art and state‐of‐the‐practice in the development and use of posits in edge computing and HPC. The current literature supports posits as a promising alternative to traditional floating‐point systems, both as a stand‐alone replacement and in a mixed‐precision environment. Development and standardization of the posit type is ongoing, and much research remains to explore the application of posits in different domains, how to best implement them in hardware, and where they fit with other numerical representations.