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SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks. (arXiv:2309.04037v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
The fast growth of computational power and scales of modern super-computing
systems have raised great challenges for the management of exascale scientific
data. To maintain the usability of scientific data, error-bound lossy
compression is proposed and developed as an essential technique for the size
reduction of scientific data with constrained data distortion. Among the
diverse datasets generated by various scientific simulations, certain datasets
cannot be effectively compressed by existing error-bounded lossy compressors
with traditional techniques. The recent success of Artificial …
arxiv challenges compression computational computing computing systems data error exascale growth management modern networks neural networks power systems usability