Meeting Notes 04-25-2022

Minutes of SBI-FAIR April 25, 2022, Meeting

Present: Kamil Iskra, Deborah Penchoff, Vikram Jadhao, Shantenu Jha, Geoffrey Fox, Xiaodong Yu, Piotr Luszczek, Cade Brown, Baixi Sun, Jack Dongarra

Updates

Virginia

• Discussed continued work on diffusion surrogate with Glazier and Javier Toledo (Edmonton)
• Discussed Fusion surrogate benchmark from Lawrence Livermore

Tennessee

• Cade Brown presented an update
• Discussed Sentinel 3 benchmark based on UK Cloudmask from MLCommons
• Then discussed FAIR Benchmark platform SLIP which is has been extended to become SABATH
• Described report structure
  • Model format - how universal is this
• Has done UK cloudmask and looked at TEvol (2 MLCommons benchmarks)
• Deal with Jupyter notebooks with nbconvert
• Add callbacks to model.fit
• How to do FAIR
• Use Json
• Relation to SciML-Bench GitHub - stfc-sciml/sciml-bench: SciML Benchmarking Suite for AI for Science and MLCube from MLCommons

Rutgers

• The IPDPS paper was accepted. This isn’t the final version, but the only publicly available version currently is [2104.04797] Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
• Discussed Adversarial autoencoders and use of Alphafold which is expected to do better
• Summit difficult due to IBM containers
• Noted continued study of “2 billion” paper (renamed “Building high accuracy emulators for scientific simulations with deep neural architecture search” https://arxiv.org/pdf/2001.08055.pdf)
• Survey paper
• Noted Proxima by Ian Foster Proxima | Proceedings of the ACM International Conference on Supercomputing

**Indiana **

• Working on scaling recurrent neural net surrogate https://doi.org/10.1088/2632-2153/ac5f60 to more particles
• Ph.D. student JCS Jcs Kadupitiya will defend thesis.

Argonne

• Baixi presentation
• Described distributed training shuffling problem as a graph
• Cost of training has large data loading time
• Studied increasing standard deviation/mean by redistribution over nodes
• Address Imbalance data loading by moving computetasks to other nodes
• Note large compute variance over GPUs even if batch size fixed, which seems surprising – why are some GPUs slow?