Research in Multithreaded Dataflow and Hybrid Architectures

这项研究的总体目标是调查基于多线程的经济高效的主流计算的体系结构特征。在这种背景下，本研究将集中在两个方向：多线程数据流范型的研究，以及结果对混合数据流-冯·诺伊曼体系结构的适应。数据流模型非常适合多线程体系结构，因为它促进了指令级上下文切换和细粒度并行。清楚地了解数据流环境中支持多线程的问题将使我们能够使它们适应数据流/控制流混合体系结构。混合系统在多处理领域提供了最有趣的机会--它们直接解决了未来超标量处理器将面临的问题，如长存储器延迟、上下文切换开销、多活动指令流、快速高效的任务同步支持。首先，这个项目将调查多线程数据流架构中高速缓冲存储器的使用，以及提高这类机器性能的编译器技术。然后，该项目将研究对多线程的架构支持，包括对线程管理、线程调度、通信和线程间同步的支持。然后将研究数据流和混合体系结构环境中的同步多线程。最后，本项目将探索最重要的建筑特征与对流RISC技术的适用性。

The overall objective of the research is the investigation of architectural features for cost-effective, mainstream computing based on multithreading. In this vein, this research will focus in two directions; investigation of multithreaded dataflow paradigm, and the adaptation of the results to hybrid dataflow-von Neumann architecture. The dataflow model is ideally suited for multithreaded architecture since it facilitates instruction level context switching and fine-grained parallelism. A clear understanding of the issues in supporting multiple threads in dataflow environment will permit us to adapt them to hybrid dataflow/control flow architectures. Hybrid systems present the most interesting opportunities in the area of multiprocessing - they directly address problems that will be faced by future superscalar processors, such as long memory latencies, context switching overheads, multiple active instruction streams, fast and efficient support for task synchronization. Initially, this project will investigate the use of cache memories for multihreaded dataflow architecture, and compiler techniques for improving the performance of such machines. This project will then study architectural support for multithreading including support for thread management, thread scheduling, communication and synchronization among threads. Simultaneous multithreading in the context of dataflow and hybrid architectures will then be investigated. Finally, this project will explore the suitability of the most significant architectural features for use with convectional RISC technology.