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

职业：并行、自适应和混合应用程序的动态运行时优化

• 批准号: 0846002
• 负责人: Edgar Gabriel
• 金额: $ 40.96万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-15 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0846002&HistoricalAwards=false
• 关键词: CAREER; Dynamic; Run; Time; Optimization

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.

职业：平行，自适应和混合应用程序的动态运行时间调整当今的高性能计算系统的复杂性要求最终用户和应用程序开发人员为每个平台调整代码。 处理器和节点体系结构，网络互连以及软件堆栈都暴露了大量影响应用程序性能的参数。这些参数通常相关，这进一步使任何应用程序性能的可预测性变得复杂。截至今天，最受欢迎的调整方法适用于代码最耗时的操作，即对某些问题大小进行评估的不同版本的同一操作的性能，并为后续执行选择了最佳性能版本应用程序。但是，对于自适应应用，这种方法是不切实际的。这些应用程序在运行时有所不同，例如通过根据某些误差标准在本地完善计算网格。因此，问题尺寸通常是未知的，因此无法针对相关的问题大小调整昂贵的操作。本项目的重点是利用分布式内存并行编程模型（例如Hybrid）的平行，适应性应用程序的运行时间调整使用OpenMP和MPI共享内存/分布式存储器并行化策略。该项目的重点是引入新颖的运行时选择算法，该算法结合了从以前的执行中收集的知识，来自阶乘设计理论的算法，这些算法非常大参数空间和机器学习中的高级算法。 该项目还针对推荐系统的开发，该系统提出了一种从优化运行中收集的人类可读的体验形式，以便在其他应用程序中重新使用它们。该提案解决了高性能计算中最紧迫和最根本的问题之一。一侧的代码可移植性和可维护性，另一侧的性能通常与目标相矛盾。该项目开发了开发性能便携式并行代码所需的基本知识，因此避免了为不同平台维护多个版本的相同代码的必要性。