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Awards and Recognition | Mathematics and Computer Science

MCS staff play key role in Argonne extreme-scale training program

Researchers in the Mathematics and Computer Science (MCS) division at Argonne National Laboratory played a major role in the 2023 Argonne Training Program on Extreme-Scale Computing (ATPESC).

The program, held July 30 to August 11, offered early career computational scientists two weeks of in-depth instruction on current and next-generation supercomputers.

One of the 70 participants selected for the ATPESC 2023 program was Youngjun Lee, an MCS division postdoctoral appointee. Lee received his Ph.D. in applied mathematics from the University of California, Santa Cruz. At Argonne he designs and develops numerical algorithms and performance portability layers for heterogeneous systems for high-performance multiphysics simulation software.

Among the lecturers at the training program were ten MCS staff, who gave presentations and led hands-on sessions on key skills and tools for designing, implementing and executing applications on high-end computing systems. Following is a list of the MCS presentations.

Track 2CProgramming Models and Languages – MPI

  • Introduction, Unintended Synchronization, Collectives and Nonblocking Collectives, Hands-on – Yanfei Guo and Ken Raffenetti
  • Derived Datatypes, Introduction to RMA, Hands-on – Ken Raffenetti and Hui Zhou
  • RMA continued, Introduction to Hybrid Programming, Hands-on – Hui Zhou and Yanfei Guo
  • Hybrid Programming with Threads and GPUs, What’s New in MPI-4, Hands-on – Yanfei Guo

Track 3 – Software Productivity and Sustainability

  • Scientific Software Design – Anshu Dubey
  • Software Testing and Verification – Anshu Dubey
  • Refactoring Scientific Software – Anshu Dubey
  • Lab Notebooks for Computational Mathematics, Sciences, & Engineering – Jaren O’Neal
  • Managing Computational Experiments – Anshu Dubey

Track 7 – I/O

  • Welcome and Introduction – Phil Carns
  • Principles of HP I/O – Phil Carns
  • MPI/IO – Rob Latham
  • Parallel netCDF – Rob Latham
  • Understanding and Tuning Performance – Shane Snyder

Track 8 – Machine Learning

  • Deep Learning Methods – Tanwi Mallick
  • Scientific Machine Learning for Dynamical Systems – Romit Maulik

Click here for the full ATPESC 2023 agenda.