The spectacular growth in the number of cores in current supercomputers poses design challenges for the development of performance analysis and tuning tools. To be effective, such analysis and tuning tools must be scalable and be able to manage the dynamic behaviour of parallel applications. In this work, we present ELASTIC, an environment for dynamic tuning of large-scale parallel applications. To be scalable, the architecture of ELASTIC takes the form of a hierarchical tuning network of nodes that perform a distributed analysis and tuning process. Moreover, the tuning network topology can be configured to adapt itself to the size of the parallel application. To guide the dynamic tuning process, ELASTIC supports a plugin architecture. These plugins, called ELASTIC packages, allow the integration of different tuning strategies into ELASTIC. We also present experimental tests conducted using ELASTIC, showing its effectiveness to improve the performance of large-scale parallel applications.

中文翻译：

---

ELASTIC：大型动态调整环境

当前超级计算机中内核数量的惊人增长对性能分析和调整工具的开发提出了设计挑战。为了有效，此类分析和调整工具必须具有可伸缩性，并且能够管理并行应用程序的动态行为。在这项工作中，我们介绍了ELASTIC，这是一种动态调整大型并行应用程序的环境。为了实现可伸缩性，ELASTIC的体系结构采用节点的分层调整网络的形式，这些节点执行分布式分析和调整过程。此外，可以将调整网络拓扑配置为适合于并行应用程序的大小。为了指导动态调整过程，ELASTIC支持插件体系结构。这些称为ELASTIC软件包的插件允许将不同的调整策略集成到ELASTIC中。