Characterization and Detection of Artifacts for Error-Controlled Lossy Compressors

Today's scientific high-performance computing (HPC) applications are often running on large-scale environments, producing extremely large volumes of data that need to be compressed effectively for efficient storage or data transfer. Error-bounded lossy compression is arguably the most efficient way to this end, because it can get very high compression ratios while controlling the data distortion strictly based on user requirements for compression errors. However, error-bounded lossy compressors may have serious artifact issues in situations with relatively large error bound or high compression ratios, which is highly undesirable to users. In this paper, we compre-hensively characterize the artifacts for multiple state-of-the-art error-bounded lossy compressors (including SZ-1.4, SZ-2.1, SZ-3.0, FPZIP, ZFP, MGARD) and provide an in-depth analysis for the root cause of these artifacts. We summarize the artifact issue into three types and also develop an efficient artifact detection algorithm for each type of artifact. We finally evaluate our artifact detection methods using four scientific datasets, which demonstrates that the proposed methods are able to detect artifact issues under linear time complexity.