MPI环境中的自提交并行作业队列实现
An MPI Implementation of a Self-Submitting Parallel Job Queue
AI总结：
提出一种在MPI环境中使用高级自提交并行作业队列的简单方法，通过C++库MPQueue实现，并应用于计算组合学和偏微分方程分岔图求解，验证了其高效性和可扩展性。
摘要：
我们提出了一种简单且易于应用的方法，用于在MPI环境中使用高级自提交并行作业队列。使用C++，我们实现了一个函数库MPQueue，既用于测试我们的概念，也用于实际应用。特别是，我们将我们的想法应用于解决计算组合学问题以及寻找偏微分方程解的分岔图。我们的方法是通用的，可以在许多情况下无需大量编程工作即可应用。关键思想是工作者本身可以轻松地向当前运行的作业队列提交新作业。我们的应用涉及复杂的数据结构，因此我们采用序列化来允许数据在节点之间轻松传递。使用我们的库，即使不是MPI专家，也可以并行解决大型问题。我们通过几个示例程序演示了我们的方法和库的特性，并给出了当前偏微分方程研究的一些结果。我们通过开销和扩展性实验表明，我们的技术是高效且有效的。
英文摘要：
We present a simple and easy to apply methodology for using high-level self-submitting parallel job queues in an MPI environment. Using C++, we implemented a library of functions, MPQueue, both for testing our concepts and for use in real applications. In particular, we have applied our ideas toward solving computational combinatorics problems and for finding bifurcation diagrams of solutions of partial differential equations (PDE). Our method is general and can be applied in many situations without a lot of programming effort. The key idea is that workers themselves can easily submit new jobs to the currently running job queue. Our applications involve complicated data structures, so we employ serialization to allow data to be effortlessly passed between nodes. Using our library, one can solve large problems in parallel without being an expert in MPI. We demonstrate our methodology and the features of the library with several example programs, and give some results from our current PDE research. We show that our techniques are efficient and effective via overhead and scaling experiments.
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