<span class="var-sub_title">Doomsday: Predicting Which Node Will Fail When on Supercomputers</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Doomsday: Predicting Which Node Will Fail When on Supercomputers


Authors: Anwesha Das (North Carolina State University), Frank Mueller (North Carolina State University), Paul Hargrove (Lawrence Berkeley National Laboratory), Eric Roman (Lawrence Berkeley National Laboratory), Scott Baden (Lawrence Berkeley National Laboratory)

Abstract: Predicting which node will fail and how soon remains a challenge for HPC resilience, yet may pave the way to exploiting proactive remedies before jobs fail. Not only for increasing scalability up to exascale systems, but even for contemporary supercomputer architectures does it require substantial efforts to distill anomalous events from noisy raw logs. To this end, we propose a novel phrase extraction mechanism called TBP (time-based phrases) to pin-point node failures, which is unprecedented. Our study, based on real system data and statistical machine learning, demonstrates the feasibility to predict which specific node will fail in Cray systems. TBP achieves no less than 83% recall rates with lead times as high as 2 minutes. This opens up the door for enhancing prediction lead times for supercomputing systems in general, thereby facilitating efficient usage of both computing capacity and power in large scale production systems.


Presentation: file


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