BlockMGARD：在GPU上通过关注区域误差控制加速自适应科学数据缩减

中文摘要 AI 辅助

科学数据规模的增长使得有损压缩成为在可控误差下缩减数据量的必要手段。基于多级分解的变换型压缩器（如MGARD）能实现较高压缩比，但难以适配GPU架构。本文提出BlockMGARD，这是一种支持关注区域（ROI）的自适应GPU有损压缩器，包含四项贡献：（1）利用GPU片上内存和常量查找表的缓存内块分解，用于加速分解；（2）结合缓存内块分解与全局分解的混合层级结构，以平衡速度与压缩比；（3）具备细粒度ROI误差控制的端到端流水线，用于保留特征；（4）在五个真实世界数据集上与最先进方法的评估。与MGARD-X相比，BlockMGARD的压缩和解压缩吞吐量最高提升4.2倍和9.1倍，在ROI感知误差控制下，压缩比最高比均匀容差基线高8.63倍；在四款GPU上，BlockMGARD实现接近理想的线性扩展，I/O成本较MGARD-X最高降低1.58倍。

英文摘要

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-Interest (ROI)-supported GPU lossy compressor, with four contributions: (1) an In-cache Block decomposition leveraging GPU on-chip memory and constant lookup tables to accelerate decomposition; (2) a hybrid hierarchy combining In-cache Block and global decomposition to balance speed and compression ratio; (3) an end-to-end pipeline with fine-grained ROI error control for feature preservation; and (4) an evaluation against state-of-the-art methods on five real-world datasets. Compared to MGARD-X, BlockMGARD achieves up to 4.2x and 9.1x higher compression and decompression throughput, and up to 8.63x higher compression ratio than uniform-tolerance baselines under ROI-aware error control. Across four GPUs, BlockMGARD achieves near-ideal linear scaling and up to 1.58x I/O cost reduction over MGARD-X.

发表机构

• University of Oregon（俄勒冈大学）
• Oak Ridge National Laboratory（橡树岭国家实验室）
• New Jersey Institute of Technology（新泽西理工学院）
• Oregon State University（俄勒冈州立大学）

机构由 AI 辅助整理，请以论文原文为准。