Architecture Specific Resource Management Via Intelligent Compilation and Strategy Learning

本研究的目标是通过应用程序的智能调度来实现高效的分布式计算。该方法包括三个部分：编译器开发、系统负载测量和最优调度策略的自动学习。编译器被修改为从应用程序中提取控制和数据依赖关系，发出性能监视代码，并允许将其划分为具有可预测资源需求的进程。正在开发一种神经网络模型，以基于就绪列表长度、I/O流量和网络拥塞来表征系统负载。最后，自动学习系统将使用预测和测量的应用程序需求调整调度策略以平衡系统负载。

Wah This research targets efficient distributed computing through intelligent scheduling of application programs. The approach has three components: compiler development, measurement of system loads, and automated learning of optimal scheduling policies. Compilers are modified to extract control and data dependencies from applications programs, emit performance monitoring code, and allow partitioning into processes with predictable resource requirements. A neural network model is being developed to characterize system loads based on ready list lengths, I/O traffic, and network congestion. Finally, an automated learning system will tune scheduling policies to balance system loads using the predicted and measured application program requirements.