论文标题
任务级投机性科学应用的资源分配：使用平行轨迹剪接的概念证明
Resource allocation for task-level speculative scientific applications: a proof of concept using Parallel Trajectory Splicing
论文作者
论文摘要
大规模分布式计算机上可用的并行性不断增加对许多科学应用构成了主要的可扩展性挑战。提高可扩展性的一种常见策略是根据可以在运行时系统同时执行的独立任务来表达算法。在此手稿中，我们考虑了允许任务级别推测的这种方法的概括。在这种情况下，每个任务都附加了概率，这与将任务的乘积作为计算的一部分消耗的可能性相对应。我们考虑到每个可能的任务的最佳资源分配问题，以使预期的总体计算吞吐量最大化。通过分析其在平行轨迹剪接上的应用，这是一种用于原子模拟的大规模平行长期动力学方法来证明这种方法的力量。
The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express the algorithm in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of this approach where task-level speculation is allowed. In this context, a probability is attached to each task which corresponds to the likelihood that the product of the task will be consumed as part of the calculation. We consider the problem of optimal resource allocation to each of the possible tasks so as too maximize the expected overall computational throughput. The power of this approach is demonstrated by analyzing its application to Parallel Trajectory Splicing, a massively-parallel long-time-dynamics method for atomistic simulations.
