Career: Adaptive Parallel Processing for Dynamic Applications

现有的重构编译器已经成功地检测了常规程序的并行性，即，通过使用当前依赖测试和符号方法，静态（编译时）定义的具有相对简单访问模式的代码。不幸的是，超过50%的程序的访问模式要么非常复杂，要么在执行之前完全没有定义。 这种“循环消耗”应用的例子是SPICE、DYNA-3D、GAUSSIAN、DMOL、CHARMM和FIDAP。随着建模技术变得越来越复杂，未来的大规模仿真将主要是动态的，从而增加了静态不可分析代码的比例，从而有效地降低了当前编译器技术的覆盖率。通过经典的编译时分析技术，稀疏的动态程序从根本上是难以处理的，这使得并行化或其他优化是不可能的。这项工作将通过创建一个框架，将运行时分析和优化技术补充静态编译，从而将当前技术的覆盖范围扩展到动态程序。 应用级可编程硬件加速某些简单的，重复的操作也将进行研究。最后，一种自适应策略，其中静态和运行时方法将与硬件和软件性能监视器和统计数据库连接在一个自适应循环中，将在执行期间根据其数据调整应用程序。

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.