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Benchmarking Machine Learning Workloads on Emerging Hardware
Tom St John · Murali Emani

Wed Mar 04 07:00 AM -- 03:30 PM (PST) @ Level 3 Room 6
Event URL: https://memani1.github.io/challenge20/ »

With evolving system architectures, hardware and software stacks, diverse machine learning (ML) workloads, and data, it is important to understand how these components interact with each other. Well-defined benchmarking procedures help evaluate and reason the performance gains with ML workload-to-system mappings. We welcome all novel submissions in benchmarking machine learning workloads from all disciplines, such as image and speech recognition, language processing, drug discovery, simulations, and scientific applications. Key problems that we seek to address are: (i) which representative ML benchmarks cater to workloads seen in industry, national labs, and interdisciplinary sciences; (ii) how to characterize the ML workloads based on their interaction with hardware; (iii) which novel aspects of hardware, such as heterogeneity in compute, memory, and networking, will drive their adoption; (iv) performance modeling and projections to next-generation hardware. Along with selected publications, the workshop program will also have experts in these research areas presenting their recent work and potential directions to pursue.

Call for Papers can be found here:

Paper Submission Deadline: January 15, 2020
Author Notification: January 27, 2020
Camera-Ready Papers Due: February 21, 2020

Author Information

Tom St John (Tesla)
Murali Emani (Argonne National Laboratory)

Murali Emani is an Assistant Computer Scientist in the Data Science group with the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. His research interests are at the intersection of systems and machine learning including Parallel programming models, Hardware accelerators for ML/DL, High Performance Computing, Scalable Machine Learning, Runtime Systems, Performance optimization, Emerging HPC architectures, Online Adaptation. Prior, he was a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory, US. Murali obtained his PhD and worked as a Research Associate at the Institute for Computing Systems Architecture at the School of Informatics, University of Edinburgh, UK. His research resulted in multiple publications at top conferences such as PACT, PLDI and granted patents. Murali served as technical program committee member for conferences including ICPP'19, CCGRID'19, PACT '18, CCGRID '18, ICPP '18. He chaired the first Birds-of-feather session on Machine Learning benchmarking on HPC systems at Supercomputing 2019.

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