Scheduling multithreaded computations by work stealing
Journal of the ACM (JACM)
Efficient load balancing for wide-area divide-and-conquer applications
PPoPP '01 Proceedings of the eighth ACM SIGPLAN symposium on Principles and practices of parallel programming
ATLAS: an infrastructure for global computing
EW 7 Proceedings of the 7th workshop on ACM SIGOPS European workshop: Systems support for worldwide applications
Advanced eager scheduling for Java-based adaptively parallel computing
JGI '02 Proceedings of the 2002 joint ACM-ISCOPE conference on Java Grande
Executing functional programs on a virtual tree of processors
FPCA '81 Proceedings of the 1981 conference on Functional programming languages and computer architecture
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Satin: A high-level and efficient grid programming model
ACM Transactions on Programming Languages and Systems (TOPLAS)
UTS: an unbalanced tree search benchmark
LCPC'06 Proceedings of the 19th international conference on Languages and compilers for parallel computing
Granularity-Aware Work-Stealing for Computationally-Uniform Grids
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Work stealing for multi-core HPC clusters
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
A geometric algorithm for winding number computation with complexity analysis
Journal of Complexity
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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Work Stealing has proved to be an effective method for load balancing regular divide-and-conquer (D&C) applications on heterogeneous distributed systems, but there have been relatively few attempts to adapt it to address irregular D&C applications. For such applications, it is essential to have a mechanism that can estimate dynamic system load during the execution of the applications. In this paper, we evaluate a number of work-stealing algorithms on a set of generic Unbalanced Tree Search (UTS) benchmarks. We present a novel Feudal Stealing work-stealing algorithm and show, using simulations, that it delivers consistently better speedups than other work-stealing algorithms for irregular D&C applications on high-latency heterogeneous distributed systems. Compared to the best known work-stealing algorithm for high-latency distributed systems, we achieve improvements of between 9% and 48% for irregular D&C applications.