no code implementations • 23 May 2024 • Matthias Chung, Rick Archibald, Paul Atzberger, Jack Michael Solomon
Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalidating artifacts.
no code implementations • 23 Nov 2020 • Rick Archibald, Edmond Chow, Eduardo D'Azevedo, Jack Dongarra, Markus Eisenbach, Rocco Febbo, Florent Lopez, Daniel Nichols, Stanimire Tomov, Kwai Wong, Junqi Yin
This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e. g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP.
no code implementations • IEEE Geoscience and Remote Sensing Letters 2007 • Rick Archibald, George Fann
Hyperspectral images consist of large number of bands which require sophisticated analysis to extract.