Argonne National Laboratory

Reducing Waste in Large Scale Systems through Introspective Analysis

TitleReducing Waste in Large Scale Systems through Introspective Analysis
Publication TypeConference Paper
Year of Publication2015
AuthorsBautista-Gomez, L, Gainaru, A, Perarnau, S, Tiwari, D, Gupta, S, Engelmann, C, Cappello, F, Snir, M
Conference NameIPDPS 2016
Conference LocationChicago, IL
Other NumbersANL/MCS-P5438-1115
AbstractResilience is an important challenge for extreme-scale supercomputers. Today, failures in supercomputers are assumed to be uniformly distributed in time. However, recent studies show that failures in high-performance computing systems are partially correlated in time, generating periods of higher failure density. Our study of the failure logs of multiple supercomputers show that periods of higher failure density occur with up to three times more than the average. We design a monitoring system that listens to hardware events and forwards important events to the runtime to detect those regime changes. We implement a runtime capable of receiving notifications and adapt dynamically. In addition, we build an analytical model to predict the gains that such dynamic approach could achieve. We demonstrate that in some systems, our approach can reduce the wasted time by over 30%.