This is the first part of the discussion of MLforHPC. It includes MLAutotuning (Using ML to configure or autotune ML or HPC simulations and MLaroundHPC (Learning outputs from inputs).

Fox received a Ph.D. in Theoretical Physics from Cambridge University where he was Senior Wrangler. He is now a distinguished professor of Engineering, Computing, and Physics at Indiana University where he is the director of the Digital Science Center. He previously held positions at Caltech, Syracuse University, and Florida State University after being a postdoc at the Institute for Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge. He has supervised the Ph.D. of 72 students and published around 1300 papers (over 500 with at least ten citations) in physics and computing with an hindex of 77 and over 35000 citations. He is a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface between computing and applications. Current work is in Biology, Pathology, Sensor Clouds and Ice-sheet Science, Image processing, Deep Learning, and Particle Physics. His architecture work is built around High-performance computing enhanced Software Defined Big Data Systems on Clouds and Clusters. The analytics focuses on scalable parallel machine learning. He is an expert on streaming data and robot-cloud interactions. He is involved in several projects to enhance the capabilities of Minority Serving Institutions. He has experience in online education and its use in MOOCs for areas like Data and Computational Science.

Presented at ACM HPDC 2019 - The 28th International Symposium on High-Performance Parallel and Distributed Computing, Phoenix, Arizona, USA - June 27, 2019.

Supported by National Science Foundation through Awards: 443054 CIF21 DIBBs: Middleware and High Performance Analytics Libraries for Scalable Data Science and, 1720625 Network for Computational Nanotechnology - Engineered nanoBIO Node

Researchers should cite this work as follows:

• Fox, Geoffrey C. (2019), "Perspectives on High-Performance Computing in a Big Data World: Part C - MLaroundHPDC/HPC and MLAutotuning," https://nanohub.org/resources/31392.

  BibTex | EndNote

1. artificial intelligence (AI)
2. big data
3. cloud computing
4. high performance computing (HPC)
5. machine learning
6. NanoBio Node at Indiana