Loop scheduling techniques aim to achieve load-balanced executions of scientific applications. Dynamic loop self-scheduling (DLS) libraries for distributed-memory systems are typically MPI-based and employ a centralized chunk calculation approach (CCA) to assign variably-sized chunks of loop iterations. We present a distributed chunk calculation approach (DCA) that supports various types of DLS techniques. Using both CCA and DCA, twelve DLS techniques are implemented and evaluated in different CPU slowdown scenarios. The results show that the DLS techniques implemented using DCA outperform their corresponding ones implemented with CCA, especially in extreme system slowdown scenarios.

循环调度技术旨在实现科学应用程序的负载平衡执行。分布式内存系统的动态循环自调度（DLS）库通常基于MPI，并采用集中式块计算方法（CCA）分配大小可变的循环迭代块。我们提出了一种支持各种DLS技术的分布式块计算方法（DCA）。同时使用CCA和DCA，可以在不同的CPU速度下降的情况下实施和评估十二种DLS技术。结果表明，使用DCA实施的DLS技术要优于使用CCA实施的DLS技术，尤其是在极端系统减速的情况下。