Career: Adaptive Parallel Processing for Dynamic Applications

职业：动态应用程序的自适应并行处理

• 批准号: 9734471
• 负责人: Lawrence Rauchwerger
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-01 至 2004-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9734471&HistoricalAwards=false
• 关键词: Career; Adaptive; Parallel; Processing; Dynamic

Existing restructuring compilers have been successful in detecting the parallelism of regular programs, i.e., codes with relatively simple access patterns that are statically (compile time) defined, by using current dependence tests and symbolic methods. Unfortunately, for more than 50% of programs the access pattern are either very complex or are completely undefined before execution. Examples of such "cycle consuming" applications are SPICE, DYNA-3D, GAUSSIAN, DMOL, CHARMM and FIDAP. As modeling techniques become more sophisticated, future large scale simulations will be mostly dynamic in nature, thus increasing the fraction of statically non-analyzable codes and therefore effectively decreasing the coverage of current compiler technology. Sparse, dynamic programs are fundamentally intractable through classic compile-time analysis techniques making parallelization or other optimizations impossible. This work will extend the coverage of current technology to dynamic programs by creating a framework that will complement static compilation with run--time analysis and optimization techniques. Application level programmable hardware for the acceleration of certain simple, repetitive operations will also be studied. Finally, an adaptive strategy in which static and run-time methods will be connected in an adaptive loop with hardware and software performance monitors and statistical databases will tune the application to its data during execution.

现有的重组编译器已成功地检测常规程序的并行性，即通过使用当前依赖性测试和符号方法，具有相对简单的访问模式（编译时间）定义的相对简单访问模式的代码。不幸的是，对于超过50％的程序，访问模式要么很复杂，要么是在执行前完全不确定。 这种“循环消费”应用的示例是Spice，Dyna-3D，Gaussian，DMOL，CHARMM和FIDAP。随着建模技术变得越来越复杂，未来的大规模仿真本质上将大多是动态的，因此增加了静态非分析代码的比例，因此有效地降低了当前编译器技术的覆盖范围。稀疏，动态程序从根本上可以通过经典的编译时间分析技术使并行化或其他优化进行不可能。这项工作将通过创建一个将通过运行时分析和优化技术来补充静态编译的框架，将当前技术的覆盖范围扩展到动态程序。 还将研究用于加速某些简单重复操作的应用程序级别可编程硬件。最后，一种自适应策略，其中静态和运行时方法将通过硬件和软件性能监视器和统计数据库连接到自适应环中，将在执行过程中将应用程序调整为其数据。