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Jack Dongarra presents: Adaptive Linear Solvers and Eigensolvers

Jack Dongarra from UT Knoxville gave this talk at ATPESC 2019. “Success in large-scale scientific computations often depends on algorithm design. Even the fastest machine may prove to be inadequate if insufficient attention is paid to the way in which the computation is organized. We have used several problems from computational physics to illustrate the importance of good algorithms, and we offer some very general principles for designing algorithms.”

Video: Overview of Machine Learning Methods

“Machine learning enables systems to learn automatically, based on patterns in data, and make better searches, decisions, or predictions. Machine learning has become increasingly important to scientific discovery. Indeed, the U.S. Department of Energy has stated that “machine learning has the potential to transform Office of Science research best practices in an age where extreme complexity and data overwhelm human cognitive and perception ability by enabling system autonomy to self-manage, heal and find patterns and provide tools for the discovery of new scientific insights.”