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COFFEE: Cross-Layer Optimization for Fast and Efficient Executions of Sinkhorn-Knopp Algorithm on HPC Systems., , , , and . IEEE Trans. Parallel Distributed Syst., 34 (7): 2167-2179 (July 2023)Total Variation Reduction for Lossless Compression of HPC Applications., , , , and . SoCC, page 129-134. IEEE, (2021)SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data., , , , , , and . IEEE Trans. Multi Scale Comput. Syst., 4 (4): 900-913 (2018)zMesh: Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement., , , , , and . IPDPS, page 402-411. IEEE, (2021)Identifying Latent Reduced Models to Precondition Lossy Compression., , , , , , , , , and . IPDPS, page 293-302. IEEE, (2019)A content-aware writing mechanism for reducing energy on non-volatile memory based embedded storage systems., , , , and . Des. Autom. Embed. Syst., 17 (3-4): 711-737 (2013)A Data-driven Approach to Harvesting Latent Reduced Models to Precondition Lossy Compression for Scientific Data., , , , , , and . IEEE Trans. Big Data, 9 (3): 949-963 (June 2023)ZFP-X: Efficient Embedded Coding for Accelerating Lossy Floating Point Compression., , , , and . IPDPS, page 1041-1050. IEEE, (2023)Compression Ratio Modeling and Estimation across Error Bounds for Lossy Compression., , , , , and . IEEE Trans. Parallel Distributed Syst., 31 (7): 1621-1635 (2020)zMesh: Theories and Methods to Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement., , , , , and . IEEE Trans. Parallel Distributed Syst., 33 (12): 3702-3717 (2022)