  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.
