Towards Practical Machine Learning Frameworks for Performance Diagnostics in Supercomputers
Author:
Affiliation:
1. Boston University, Boston, MA, USA
2. Sandia National Laboratories, Albuquerque, NM, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3588982.3603609
Reference38 articles.
1. The Lightweight Distributed Metric Service: A Scalable Infrastructure for Continuous Monitoring of Large Scale Computing Systems and Applications
2. Toward Rapid Understanding of Production HPC Applications and Systems
3. E2EWatch: End-to-end Anomaly Diagnosis Framework for Production HPC Systems.
4. Burak Aksar , Efe Sencan , Benjamin Schwaller , Omar Aaziz , Vitus J Leung , Jim Brandt , Brian Kulis , and Ayse K Coskun . 2022 . ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC Systems. In 2022 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 369--380 . Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J Leung, Jim Brandt, Brian Kulis, and Ayse K Coskun. 2022. ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC Systems. In 2022 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 369--380.
5. Proctor: A Semi-Supervised Performance Anomaly Diagnosis Framework for Production HPC Systems
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