High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of the compute workloads, new approaches to optimized resource usage, allocation, and deployment of new AI frameworks are needed. By identifying compute workloads and their utilization characteristics, HPC systems may be able to better match available resources with the application demand. By leveraging datacenter instrumentation, it may be possible to develop AI-based approaches that can identify workloads and provide feedback to researchers and datacenter operators for improving operational efficiency. To enable this research, we released the MIT Supercloud Dataset, which provides detailed monitoring logs from the MIT Supercloud cluster. This dataset includes CPU and GPU usage by jobs, memory usage, and file system logs. In this paper, we present a workload classification challenge based on this dataset. We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches. The goal of this challenge is to foster algorithmic innovations in the analysis of compute workloads that can achieve higher accuracy than existing methods. Data and code will be made publicly available via the Datacenter Challenge website : https://dcc.mit.edu.
@article{arxiv.2204.05839,
title = {The MIT Supercloud Workload Classification Challenge},
author = {Benny J. Tang and Qiqi Chen and Matthew L. Weiss and Nathan Frey and Joseph McDonald and David Bestor and Charles Yee and William Arcand and Chansup Byun and Daniel Edelman and Matthew Hubbell and Michael Jones and Jeremy Kepner and Anna Klein and Adam Michaleas and Peter Michaleas and Lauren Milechin and Julia Mullen and Andrew Prout and Albert Reuther and Antonio Rosa and Andrew Bowne and Lindsey McEvoy and Baolin Li and Devesh Tiwari and Vijay Gadepally and Siddharth Samsi},
journal= {arXiv preprint arXiv:2204.05839},
year = {2022}
}