The scalability of high-performance, parallel iterative applications is directly affected by how well they use the available computing resources. These applications are subject to load imbalance due to the nature and dynamics of their computations. It is common that high performance systems employ periodic load balancing to tackle this issue. Dynamic load balancing algorithms redistribute the application’s workload using heuristics to circumvent the NP-hard complexity of the problem However, scheduling heuristics must be fast to avoid hindering application performance when distributing the workload on large and distributed environments. In this work, we present a technique for low overhead, high quality scheduling decisions for parallel iterative applications. The technique relies on combined application workload information paired with distributed scheduling algorithms. An initial distributed step among scheduling agents group application tasks in packs of similar load to minimize messages among them. This information is used by our scheduling algorithm, PackStealLB, for its distributed-memory work stealing heuristic. Experimental results showed that PackStealLB is able to improve the performance of a molecular dynamics benchmark by up to 41%, outperforming other scheduling algorithms in most scenarios over almost one thousand cores.

高性能，并行迭代应用程序的可伸缩性直接受到它们使用可用计算资源的能力的直接影响。这些应用程序由于其计算的性质和动态性而受到负载不平衡的影响。高性能系统通常采用周期性的负载平衡来解决此问题。动态负载平衡算法使用启发式方法重新分配应用程序的工作量，以解决问题的NP-hard复杂性。但是，调度启发式方法必须快速，以避免在大型和分布式环境中分配工作量时妨碍应用程序性能。在这项工作中，我们提出了一种用于并行迭代应用程序的低开销，高质量调度决策的技术。该技术依赖于组合的应用程序工作负载信息和分布式调度算法。调度代理程序之间的初始分布式步骤将应用程序任务分组为负载相似的包，以最大程度地减少它们之间的消息。我们的调度算法PackStealLB使用此信息进行分布式内存工作窃取启发式。实验结果表明，PackStealLB能够将分子动力学基准测试的性能提高多达41％，在大多数情况下超过近千个核的性能优于其他调度算法。为其分布式内存工作窃取启发。实验结果表明，PackStealLB能够将分子动力学基准测试的性能提高多达41％，在大多数情况下超过近千个核的性能优于其他调度算法。为其分布式内存工作窃取启发。实验结果表明，PackStealLB能够将分子动力学基准测试的性能提高多达41％，在大多数情况下超过近千个核的性能优于其他调度算法。