Author

Jieyang Chen

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Preprint Aug 2026

BlockMGARD: Accelerating Adaptive Scientific Data Reduction with Region-of-Interest Error Control on GPUs

The growing scale of scientific data makes lossy compression essential for reducing data volume under controllable error. Transformation-based compressors using multilevel decomposition, such as MGARD, achieve strong compression ratios but map poorly to GPU architectures. We propose BlockMGARD, an adaptive, Region-of-I...

Yan-Liang Li, Qian Gong, Qing Liu et al. · 0 citations

Preprint Sep 2026

Improving Progressive Compression with Adaptive Interpolation and Coefficient Decomposition

Exascale simulations generate data far faster than it can be stored or analyzed, making efficient data reduction essential. Error-controlled lossy compression offers high compression ratios under user-specified error bounds, but the target tolerance must be fixed at compression time. Progressive compression relaxes thi...

Wenbo Li, Xuan Wu, Qian Gong et al. · 0 citations

#machine learning Preprint Sep 2026

CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing

This work replaces the Jensen-Shannon Divergence routing with C_struct, a structural proxy that measures mass at Vertical-Slash compatible positions and reproduces JSD's routing decisions while eliminating both the pooled matmul and subsequent KL divergence overhead.

H. Nguyen, Chien Van Nguyen, Franck Dernoncourt et al. · 0 citations