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Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular pros and cons. In this paper we provide a comprehensive survey of emerging error-bounded lossy compression techniques. The key contribution is fourfold. (1) We summarize a novel taxonomy of lossy compression into 6 classic models. (2) We provide a comprehensive survey of 10 commonly used compression components/modules. (3) We summarized pros and cons of 47 state-of-the-art lossy compressors and present how state-of-the-art compressors are designed based on different compression techniques. (4) We discuss how customized compressors are designed for specific scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data. 

—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.