Title-Coarse-grained Energy Modeling of Rollback/Recovery Mechanisms
  As high-performance computing systems continue to grow in size and complexity,
  energy efficiency and reliability have emerged as first-order concerns. Researchers
  have shown that data movement is a significant contributing factor to power
  consumption on these systems.  Additionally, rollback/recovery protocols like
  checkpoint/restart can generate large volumes of data traffic exacerbating the
  energy and power concerns.  In this work, we show that a coarse-grained model can
  be used effectively to speculate about the energy footprints of rollback/recovery
  protocols.  Using our validated model, we evaluate the energy footprint of
  checkpoint compression, a method that incurs higher computational demand to reduce
  data volumes and data traffic.  Specifically, we show that while checkpoint
  compression leads to more frequent checkpoints (as per the optimal checkpoint
  frequency) and increases per checkpoint energy cost, compression still yields a
  decrease in total application energy consumption due to the overall runtime
  decrease.