Youngdae Kim
- B.S. in Mathematics and Computer Science, Pohang University of Science and Technology, 2007
- M.S. in Computer Science, Pohang University of Science and Technology, 2009
- Ph.D. in Computer Science, University of Wisconsin-Madison, 2017
- Postdoctoral Appointee, Argonne National Laboratory, 2018-Current
Session
07-29
13:30
30min
ExaTron.jl: a scalable GPU-MPI-based batch solver for small NLPs
Youngdae Kim
We introduce ExaTron.jl which is a scalable GPU-MPI-based batch solver for many small nonlinear programming problems. We present ExaTron.jl's architecture, its kernel design principles, and implementation details with experimental results comparing different design choices. We demonstrate a linear scaling of parallel computational performance of ExaTron.jl on Summit at Oak Ridge National Laboratory.
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