CAREER: Dynamic Run-Time Optimization of Parallel, Adaptive and Hybrid Applications

职业生涯：并行、自适应和混合应用程序的动态运行时调优当今的复杂性？S高性能计算系统要求最终用户和应用程序开发人员付出巨大努力，为每个平台调优他们的代码。处理器和节点体系结构、网络互连和软件堆栈都暴露了大量影响应用程序性能的参数。此外，这些参数往往是相互关联的，这进一步增加了任何应用程序性能的可预测性。目前最流行的调优方法对最耗时的代码操作应用静态调优，即针对特定问题大小评估同一操作的不同版本的性能，并为应用程序的后续执行选择性能最佳的版本。然而，这种方法对于自适应应用是不实用的。这些应用程序在运行时改变问题大小，例如通过基于某些误差标准局部细化计算网格。因此，问题大小通常是事先未知的，因此不能针对相关问题大小调整代价高昂的操作。本项目关注使用分布式内存并行编程模型(如MPI)或使用OpenMP和MPI的混合共享内存/分布式内存并行化策略的并行、自适应应用程序的运行时调优。该项目的重点是引入新的运行时选择算法，这些算法结合了从以前的执行中收集的知识，来自析因设计理论的非常大参数空间的算法，以及来自机器学习的高级算法。该项目还旨在开发一个推荐系统，该系统提供了从优化运行中收集的经验的人类可读形式，以便在其他应用程序中重复使用。这一建议解决了高性能计算中最紧迫和最基本的问题之一。一方面是代码的可移植性和可维护性，另一方面是性能似乎经常是相互矛盾的目标。该项目开发了开发高性能可移植并行代码所需的基本知识，从而避免了为不同平台维护同一代码的多个版本的必要性。

CAREER: Dynamic Run-Time Tuning of Parallel, Adaptive and Hybrid ApplicationsThe complexity of today?s High Performance Computing systems mandate significant efforts by end users and application developers to tune their code for each platform. Processor and node architecture, network interconnect and the software stack all expose a significant number of parameters which influence the performance of an application. These parameters are furthermore often correlated, which further complicates the predictability of the performance of any application. The most popular tuning approach as of today applies a static tuning for the most time consuming operations of the code, i.e. the performance of different versions of the same operation is evaluated for certain problem sizes and the best performing version is chosen for the subsequent executions of the application. However, this approach is not practical for adaptive applications. These applications vary the problem sizes at run-time, e.g. by locally refining the computational mesh based on certain error criteria. Thus, the problem sizes are typically unknown in advance and therefore expensive operations cannot be tuned for the relevant problem sizes.This project focuses on run-time tuning of parallel, adaptive applications utilizing either a distributed memory parallel programming model such as MPI or a hybrid shared memory/distributed memory parallelization strategy using OpenMP and MPI. The focus of the project is on introducing novel run-time selection algorithms which incorporate knowledge gathered from previous executions, algorithms from factorial design theory for very large parameter spaces and advanced algorithms from machine learning. The project also targets the development of a recommendation system, which presents a human readable form of experiences gathered from an optimization run in order to reuse them in other applications. This proposal tackles one of the most pressing and fundamental problems in High Performance Computing. Code portability and maintainability on one side and performance on the other side often seem to be contradicting goals. The project develops the fundamental knowledge required to develop performance portable parallel code and thus avoid the necessity to maintain multiple versions of the same code for different platforms.