PARASOL: An Adaptive Framework for Parallel Processing

并行计算机，特别是共享内存的多处理器，现在很普遍。然而，程序员仍然很难在实际应用程序中挖掘他们的潜力来获得良好的性能。只有消除了这一困难，并行计算才会真正成功。研究人员通过静态分析程序来确定它们的结构，嵌入运行时测量来确定它们的动态需求，并在运行时触发针对这些需求的自适应优化。从技术上讲，研究人员和他们的学生计划探索几种创新技术和/或已知评估和优化技术的组合：*他们的编译器将直接将自适应功能插入应用程序，而不是通过第三方，如操作系统。然后，应用程序将监视其系统资源使用情况，并根据其预测的需求和系统的最佳工作负载控制工作。*他们将在运行时使用并行性能模型进行自适应优化。该模型将用于预测潜在优化的执行时间，并诊断计算效率低下。*它们将实现运行时并行化和优化技术，并将它们集成到Polaris编译器和Parasol系统中。*他们将使用运行时并行化来减少应用程序中延迟的影响。

Parallel computers, particularly shared-memory multiprocessors, are nowwidespread. However, programmers still have great difficulty exploitingtheir potential to obtain good performance on real applications. Parallelcomputing will only be truly successful when this difficulty is removed.The investigators attack this problem by statically analyzing programs todetermine their structure, embedding run-time measurements to determinetheir dynamic needs, and triggering adaptive optimization at run-time thatis tuned to those needs.Technically, the investigators and their students plan to explore severalinnovative techniques and/or combinations of known evaluation andoptimization techniques: * Their compiler will insert adaptive capabilities directly into applications, rather than going through a third party such as the operating system. The application will then monitor its system resource usage and control work based on its predicted demands and the optimal workload of the system. * They will use a parallel performance model for adaptive optimization at run-time. The model will be used to both predict execution times for potential optimizations and to diagnose computational inefficiencies. * They will implement run-time parallelization and optimization techniques and integrate them in the POLARIS compiler and the PARASOL system. * They will use run-time parallelization to reduce the impact of latency in the applications.