Meeting Notes 09-26-2022

Minutes of SBI-FAIR September 26, 2022, Meeting

Present: Kamil Iskra, Xiaodong Yu, 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 still making progress
• He noted two nearly completed new surrogates
  • paper on Tsunami simulation surrogates entitled “Forecasting tsunami inundation with convolutional neural networks for a potential Cascadia Subduction Zone rupture”
  • 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
• He summarized the MLCommons status with the move to continuous (rolling) submissions rather than fixed date submissions

Indiana University

• Vikram presented some of his recent work
• He studied sensitivity to input training set showing some dramatic effects from seemingly small changes – removing one value of electrolyte concentration c

Tennessee

Piotr reported

• There was a Data Challenge at Smoky Mountain meeting with a smaller version of the Cloudmask dataset from MLCommons 2022 Challenge 6: SMCEFR: Sentinel-3 Satellite Dataset « SMC Data Challange 2021
• Two Submitted papers: one on Performance Surrogate and the other a SABATH paper at HPEC Conference IEEE HPEC 26th Annual 2022 IEEE High Performance Extreme Computing Virtual Conference 19 - 23 September 2022
  • paper and presentation Deep Gaussian process with multitask and transfer learning for performance optimization
• Questions included reproducibility and overheads from using FAIR metadata
• It was asked if SABATH recorded training time; it does record loss versus epoch number.
• Tennessee will give a detailed presentation on SABATH next time.

Rutgers

Shantenu reported

• Drug and Quantum surrogates
• He noted a new DOE $25M award for climate surrogates revisiting the startling Oxford paper https://iopscience.iop.org/article/10.1088/2632-2153/ac3ffa/meta and https://arxiv.org/pdf/2001.08055v1
• Work with Indiana University was continuing with efforts to get system running on Summit
• There was a discussion of Large Language models LLM and DOE interest in using them on scientific literature. There is a challenge with the current $10-100 million computing training cost possibly reaching a billion dollars.

Argonne

• Xiaodong Yu discussed the ASPLOS paper which was unfortunately rejected
• Baixi presented their results commenting on referee remarks
• One question prompted observation that surrogate MODEL sizes are comparatively small
• Another question was answered by noting that scheduling was a one-time cost
• In some cases their custom training order outperformed the baseline training