Ultrafast Error-Bounded Lossy Compression for Scientific Datasets

Today\u27s scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in the scientific community because it not only can significantly reduce the data volumes but also can strictly control the data distortion based on the user-specified error bound. Existing lossy compressors, however, cannot offer ultrafast compression speed, which is highly demanded by numerous applications or use cases (such as in-memory compression and online instrument data compression). In this paper, we propose a novel ultrafast error-bounded lossy compressor that can obtain fairly high compression performance on both CPUs and GPUs and with reasonably high compression ratios. The key contributions are threefold. (1) We propose a generic error-bounded lossy compression framework - -called SZx - -that achieves ultrafast performance through its novel design comprising only lightweight operations such as bitwise and addition/subtraction operations, while still keeping a high compression ratio. (2) We implement SZx on both CPUs and GPUs and optimize the performance according to their architectures. (3) We perform a comprehensive evaluation with six real-world production-level scientific datasets on both CPUs and GPUs. Experiments show that SZx is 2∼16x faster than the second-fastest existing error-bounded lossy compressor (either SZ or ZFP) on CPUs and GPUs, with respect to both compression and decompression

Data compression opportunities in EOSDIS

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Lossless Compression for Semantic Textures

A semantic texture overlays semantic labels over an image to indicate the type of texture represented by each region of the image. Traditional lossy compression works well for color textures, but not for semantic textures. This disclosure describes lossless compression techniques to compress semantic textures, thereby reducing the memory occupied by semantic textures. The techniques leverage the observation that semantic textures, unlike color textures, are highly structured with large blocks of common values. The techniques enable high-speed access to detailed rendering and resolution in real-time computer graphics. They also enable additional textures or texture resolution, enhancing the detail and realism of rendering