Meeting Notes 03-19-2022

Minutes of SBI-FAIR March 19, 2022, Meeting

• Present: Kamil Iskra, Vikram Jadhao, Shantenu Jha, Geoffrey Fox, Xiaodong Yu, Piotr Luszczek, Cade Brown, Baixi Sun, Gregor von Laszewski

Updates

Rutgers

A postdoc left unexpectedly and so the surrogate classification work was delayed. The integration of Rutgers software into Vikram’s work is proceeding and will be tested with a Summit allocation.

Indiana

Vikram discussed a surrogate paper accepted by Machine Learning: Science and Technology journal https://doi.org/10.1088/2632-2153/ac5f60. This evolves a modest collection of particles in for example the Lennard-Jones potential obtaining good results with time steps 4000 times that of classic solvers. He also presented at multiple APS sessions. He noted other work using Tensorflow to perform simulations – a collaboration with another Indiana Engineering faculty.

Virginia

Gregor presented on the status of the MLCommons benchmark stressing the difficulties in reconciling GitHub and Jupyter notebooks. Geoffrey noted that these were not quite what you wanted as a scientific electronic notebook as they didn’t support sharing of modified versions and the management of multiple Jupyter notebooks. For example, this project produced 450 notebooks and it is not even easy to search as traditional Google search fails on notebooks.

Gregor also discussed timing tools

Tennessee

Piotr described progress in integrating MLCommons ontologies into the FAIR metadata system. He also noted problems in defining how to run SciML benchmarks with Horovod. Tennessee also submitted a challenge to the Smoky Mountain conference based on Satellite images generalizing the SciML CloudMask benchmark

Argonne National Laboratory

Xiaodong introduced the Argonne study of shared I/O. The need for global shuffling at each epoch is potentially an I/O problem but their approach gave almost a factor of 10 improvement (11.4 seconds reduced to 1. seconds).

Baixi gave a detailed discussion with his usual excellent presentation.

Geoffrey and Gregor noted the practical challenge of I/O in University shared file systems with both the Earthquake code and an examination of a regular MLPerf benchmark where cloud I/O was much faster than the academic shared file system. The latter problem can be addressed by copying to local disks. Execution from those is a little faster than the cloud numbers.