Reliability-Aware Speedup Models for Parallel Applications with Coordinated Checkpointing/Restart

Speedup models are powerful analytical tools for evaluating and predicting the performance of parallel applications. Unfortunately, the well-known speedup models like Amdahl's law and Gustafson's law do not take reliability into consideration and therefore cannot accurately account for application performance in the presence of failures. In this study, we enhance Amdahl's law and Gustafson's law by considering the impact of failures and the effect of coordinated checkpointing/restart. Unlike existing analytical studies relying on Exponential failure distribution alone, in this work we consider both Exponential and Weibull failure distributions in the construction of our reliability-aware speedup models. The derived reliability-aware models are validated through trace-based simulations under a variety of parameter settings. Our trace-based simulations demonstrate these models can effectively quantify failure impact on application speedup. Moreover, we present two case studies to illustrate the use of these reliability-aware speedup models.

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