Biblio

The design and implementation of exascale system is nowadays an important challenge. Such a system is expected to combine HPC with Big Data methods and technologies to allow the execution of scientific workloads which are not tractable at this present time. In this paper we focus on an event and anomaly detection framework which is crucial in giving a global overview of a exascale system (which in turn is necessary for the successful implementation and exploitation of the system). We propose an architecture for such a framework and show how it can be used to handle failures during job execution.

Resilience in High Performance Computing (HPC) is a constraining factor for bringing applications to the upcoming exascale systems. Resilience techniques must be able to scale to handle the increasing number of expected errors in an energy efficient manner. Since the purpose of running applications on HPC systems is to perform large scale computations as quick as possible, resilience methods should not add a large delay to the time to completion of the application. In this paper we introduce a novel technique to detect and recover from transient errors in HPC applications. One of the features of our technique is that the energy budget allocated to resilience can be adjusted depending on the operator's resilience needs. For example, on synthetic data, the technique can detect about 50% of transient errors while only using 20% of the dynamic energy required for running the application. For a 60% energy budget, an application that uses 10k cores and takes 128 hours to run, will only require 10% longer to complete.