Meeting Notes 10-31-2022

Minutes of SBI-FAIR October 31, 2022, Meeting

Present: Kamil Iskra, Xiaodong Yu, Peter Beckman, Deborah Penchoff, Shantenu Jha, Geoffrey Fox, Piotr Luszczek, Baixi Sun. Vikram Jadhao, Gregor von Laszewski

Updates

Virginia

Geoffrey discussed

• The transfer of the DOE grant is completed
• The Tsunami surrogate (see last meeting) is finished while the diffusion-based surrogate is still being finalized
  • Rough draft of the diffusion model for cell simulations GENERALIZATION AND TRANSFER LEARNING IN A DEEP DIFFUSION SURROGATE FOR MECHANISTIC REAL-WORLD SIMULATIONS. Interesting is the study of dataset sizes 5000-400,000 and the importance of dealing with the large numeric range in computed values
• We discussed Margaret Lentz’s request for a project presentation
  • Draft after SC22 with final presentation November 28 1-2 pm finalized with Margaret
  • Some integrating slides and then 4-6 from each team covering past work; remaining work in the grant; what to do after the grant
  • Pete reminded us not to forget FAIR!
  • Geoffrey will make a plan

Argonne

• Their VLDB2023 paper: “SOLAR: A Highly Optimized Data Loading Framework Training CNN-based Scientific Surrogates,” was discussed
• This paper looks at the training of 3 surrogates and addresses the overhead of the I/O disk access that dominates the performance
• They compare with PyTorch Data Loader and the NoPFS paper [2101.08734] Clairvoyant Prefetching for Distributed Machine Learning I/O from Torsten Hoefler at the last SC meeting. This does optimized prefetching
• The shuffle is optimized to minimize redistribution and this leads to an improvement factor of 3.5 over NoPFS and 24 over default PyTorch \

Tennessee

Piotr reported that Cade Brown has left and they are hiring a replacement.

Rutgers

Shantenu reported

• That their team had identified 6 categories with AI enhancing HPC and they were studying performance
• He returned to topic of Large Language models LLM that can be effective in chemistry,

Indiana University

Vikram reported that

• They were continuing study of accuracy and robustness as last time as well as
• Dataset size
• Ensemble issues
• Definition of speedup