Advances in Lossy Data Compression Techniques

Dear Colleagues,

Today's modern applications generate large volumes of data, making data reduction a crucial technique in various domains. Lossy compression offers the capability to significantly reduce data size, saving memory and storage space, alleviating I/O burden, reducing communication time, and improving energy efficiency in parallel and distributed environments such as high-performance computing (HPC), cloud computing, edge computing, and the Internet of Things (IoT). For example, in HPC systems, where computational capabilities are extensive, scientific applications can generate vast amounts of data for post-analysis, posing challenges due to limited storage space and bottlenecks in runtime memory and communication.

Addressing the challenges in lossy compression requires expertise from computer science, mathematics, and application domains. It involves studying the problem holistically, developing solutions, and creating robust software tools for production applications. The community needs to understand the intricate relationship between application design, data analysis and reduction methods, programming models, system software, hardware, and other elements of large-scale computing infrastructure. Considerations of applicability, fidelity, performance portability, and energy efficiency are essential. The continuous exploration and development of new lossy compression techniques are also necessary to meet the needs of emerging applications and diverse use cases.

Within this context, there are three significant research topics that the community is addressing: (1) the possibility of achieving several orders of magnitude of lossy compression for extreme-scale sciences, (2) understanding the trade-off between performance and accuracy in lossy compression, and (3) developing effective solutions for reducing data size while preserving the information within large datasets.

The goal of this Special Issue is to provide a dedicated platform for researchers from all related communities to present their research findings, exchange ideas, identify new research directions, and foster collaborations within the lossy compression community.

Dr. Dingwen Tao
Dr. Xin Liang
Dr. Kai Zhao
Guest Editors

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• lossy compression
• big data
• data reduction