Energy profile of rollback-recovery strategies in high performance computing

Abstract

Extreme-scale computing is set to provide the infrastructure for the advances and breakthroughs that will solve some of the hardest problems in science and engineering. However, resilience and energy concerns loom as two of the major challenges for machines at that scale. The number of components that will be assembled in the supercomputers plays a fundamental role in these challenges. First, a large number of parts will substantially increase the failure rate of the system compared to the failure frequency of current machines. Second, those components have to fit within the power envelope of the installation and keep the energy consumption within operational margins. Extreme-scale machines will have to incorporate fault tolerance mechanisms and honor the energy and power restrictions. Therefore, it is essential to understand how fault tolerance and energy consumption interplay. This paper presents a comparative evaluation and analysis of energy consumption in three different rollback-recovery protocols: checkpoint/restart, message logging and parallel recovery. Our experimental evaluation shows parallel recovery has the minimum execution time and energy consumption. Additionally, we present an analytical model that projects parallel recovery can reduce energy consumption more than 37% compared to checkpoint/restart at extreme scale.

A Fault-Tolerance Protocol for Parallel Applications with Communication Imbalance

“…That leads to a huge waste of time and energy [8], [9]. An enhancement to checkpoint/restart is message logging [15], a technique that stores checkpoints and, in principle, stores all the messages in an execution.…”

“…The performance overhead of those strategies can be kept low [11], [12], they feature a very efficient energy profile [8], [9], and they make possible to parallelize recovery [10]. To leverage all those features, it is imperative to address the major drawback of message logging, namely its increase in memory footprint.…”

“…The model uses theoretical formulations presented elsewhere to estimate execution time [10] and energy consumption [8]. The basic formula in the model decomposes the execution time into four parts: T = T Solve + T Checkpoint + T Recover + T Restart , and finds appropriate analytical expressions to approximate each part.…”

“…A failure in the system will only roll back the failed nodes, and have the rest of the system replay the stored messages to recover the restarted nodes. Message logging has been demonstrated to save energy [8], [9], reduce recovery time [10], and have a small overhead [11], [12]. The main drawback of message logging is its potentially large memory footprint due to the message log.…”

Toward Exascale Resilience: 2014 update

“…Some teams [76] developed models for expected run time and energy consumption for global recovery, message logging, and parallel recovery protocols. These models show in an exascale scenario that parallel recovery outperforms coordinated checkpointing protocols since parallel recovery reduces the rework time.…”