This event is a part of the "Best Practices for HPC Software Developers" webinar series, produced by the IDEAS Productivity Project. The HPC Best Practices webinars address issues faced by developers of computational science and engineering (CSE) software on high-performance computers (HPC) and occur approximately monthly.
| Resource Information | Details |
|---|---|
| Webinar Title | Lessons Learned Developing Performance Portable QMCPACK |
| Date and Time | 2023-05-10 01:00 pm WTZ |
| Presenter | Paul Kent (Oak Ridge National Laboratory) |
| Registration, Information, and Archives | https://ideas-productivity.org/resources/series/hpc-best-practices-webinars/#webinar074 |
Webinars are free and open to the public, but advance registration is required through the Event website. Archives (recording, slides, Q&A) will be posted at the same link soon after the event.
Abstract
During DOE’s Exascale Computing Project the open source QMCPACK code has been redesigned and reimplemented to run portably and performantly on multiple vendors GPUs as well as CPUs. The QMCPACK code implements Quantum Monte Carlo algorithms to predict the properties of materials with benchmark accuracy. The new implementation has now fully replaced the prior non-portable GPU solution. This webinar will outline some of the design considerations and new algorithms implemented both to run efficiently and to reduce burdens on the developers and maintainers. A key factor has been the adoption of modern development practices, including an extensive test suite. This has accelerated development, improved code quality, and also enabled isolation of problems in the wider HPC software stack, including in compilers and numerical libraries. The webinar will summarize these strategies and other recommendations for HPC application developers and facilities.
Presenter Bio
Paul Kent is distinguished staff at Oak Ridge National Laboratory, PI of the QMCPACK applications development project within DOE’s Exascale Computing Project (ECP), and director of the Center for Predictive Simulation of Functional Materials, a DOE BES Computational Materials Sciences Center. He is a Fellow of the APS and previous ACM Gordon Bell prize winner.
