通过数据流分析实现故障后的节能本地回滚
Energy-efficient localised rollback after failures via data flow analysis
摘要：
Exascale系统每小时都会出现故障。HPC程序员主要依赖应用程序级检查点和全局回滚来恢复。近年来，减少回滚进程数量的技术已经通过消息日志记录实现。然而，基于日志的方法有弱点，例如依赖于MPI实现中的复杂修改，以及在一般情况下可能需要完全重启的事实。为了解决所有基于日志的机制的局限性，我们返回到仅检查点的机制，但提倡数据流驱动的恢复（DFR），一个根本不同的方法，依赖于分析的数据流的迭代代码，和众所周知的概念的数据流图。我们证明了DFR的MPI模板代码的有效性，以优化回滚，并减少10- 12%的整体能源消耗的空闲节点在本地化回滚。我们还提供了大规模的估计DFR的节能相比，全球回滚，其中模板代码增加为n平方的过程计数n。
英文摘要：
Exascale systems will suffer failures hourly. HPC programmers rely mostly on application-level checkpoint and a global rollback to recover. In recent years, techniques reducing the number of rolling back processes have been implemented via message logging. However, the log-based approaches have weaknesses, such as being dependent on complex modifications within an MPI implementation, and the fact that a full restart may be required in the general case. To address the limitations of all log-based mechanisms, we return to checkpoint-only mechanisms, but advocate data-flow-driven recovery (DFR), a fundamentally different approach relying on analysis of the data flow of iterative codes, and the well-known concept of data-flow graphs. We demonstrate the effectiveness of DFR for an MPI stencil code to optimise rollback and reduce the overall energy consumption by 10-12 % on idling nodes during localised rollback. We also provide large-scale estimates for the energy savings of DFR compared to global rollback, which for stencil codes increase as n square for a process count n.
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