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

Talks:

13:30 UTC

ExaTron.jl: a scalable GPU-MPI-based batch solver for small NLPs

07/29/2021, 1:30 PM2:00 PM UTC
Blue

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.

Platinum sponsors

Julia Computing

Gold sponsors

Relational AI

Silver sponsors

Invenia LabsConningPumas AIQuEra Computing Inc.King Abdullah University of Science and TechnologyDataChef.coJeffrey Sarnoff

Media partners

Packt Publication

Fiscal Sponsor

NumFOCUS