Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation
Pu Jiao, Sheng Di, Jiannan Tian, Mingze Xia, Xuan Wu, Yang Zhang, Xin Liang, Franck Cappello

TL;DR
This paper introduces a quantization-aware interpolation method to reduce artifacts in pre-quantization based scientific data compressors, significantly improving data quality while preserving high throughput in parallel computing environments.
Contribution
It presents a novel artifact mitigation algorithm that characterizes compression errors, improves decompressed data quality, and is efficiently parallelized for high-performance computing.
Findings
Effective artifact reduction in decompressed data
Maintains high compression throughput
Validated on real-world datasets
Abstract
Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance computing systems. Pre-quantization, a critical technique to remove sequential dependency and enable high parallelism, is widely used to design and develop high-throughput error-controlled data compressors. Despite the extremely high throughput of pre-quantization based compressors, they generally suffer from low data quality with medium or large user-specified error bounds. In this paper, we investigate the artifacts generated by pre-quantization based compressors and propose a novel algorithm to mitigate them. Our contributions are fourfold: (1) We carefully characterize the artifacts in pre-quantization based compressors to understand the correlation between the quantization index and compression error; (2) We propose a novel…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Advanced Data Storage Technologies · Parallel Computing and Optimization Techniques
