A Study of Parallel Distributed Processing Kernel Ware by a Principle of the Fine-grain Multi-thread Processing

我们为并行和分布式处理器开发了精细-粒度多线程处理器，并为并行和分布式处理器开发了Fuce体系结构。Fuce处理器执行多个线程独占。Fuce的核心概念是基于连续的多线程执行。在这个项目中，我们进行了以下研究：·基于连续的计算模型被开发为线程级并行计算的基础。C类编程语言、Fuce-C及其编译器系统已经开发出来，以支持基于连续性的线程级并行处理编程。Fuce处理器是在FPGA上设计和实现的。燃料处理器的性能是在FPGA和软件模拟器上使用燃料模拟器进行评估的。Cefos操作系统是根据连续性的精细多线程设计的，并评估了其使用若干多处理基准的可能性。通过这些研究，我们展示了Fuce体系结构的有效性。我们实施了一个模糊的过程 ... More 在FPGA上使用VHDL和使用Java的软件模拟器的ssor。我们评估了若干基准，例如，由sieve算法、FFT、快速排序、合并排序和N-皇后问题组成的主要数字，以及FPGA模拟器和软件模拟器。根据这个评估，我们展示了精细-谷物多线程对并行计算的各种类型、数据并行处理、管道并行处理及其混合非常有效。特别是，基于连续的多线程技术是非常有用的高性能流处理，因为它是构建操作系统内核的一个重要处理方案。作为评估的结果，我们将基于连续性的精细-谷物多线程描述为解决并行处理问题的一种非常有效的方法，如操作系统内核构建所需的基本并行处理技术和并行处理技术。我们还评估了福斯处理器的硬件成本，并显示了福斯处理器的硬件成本与商品处理器的效果比较。我们开发了基于连续性的精细颗粒多线程编程的操作系统内核机制。而且我们展示了操作系统内核可以使用基于连续性的精细谷物多线程技术来构建简单的操作系统内核。我们对硬件和软件之间的协同操作的新架构和基本机制进行了研究;内核软件用于并行处理和分布式处理，我们展示了燃料内核软件是可操作的并行处理器的开发。我们在这项研究中的目标是研究开发实用的并行和分布式机器的基本技术，我们现在可以将这项研究目标结合起来，使之成为现实。我们将继续工作，使燃料机器更实用。我们在FPGS模拟器和软件模拟器上开发Fuce-C语言系统和Cefos操作系统，以便在商品集群机器上运行。然后，我们将评估Fuce体系结构，以便为并行和分布式处理提供更可扩展和实用的基准。Less（低）

We developed the fine-grain multi-threading processor ; the Fuce architecture for parallel-and-distributed processing. The Fuce processor executes multiple threads exclusively. The core concept of Fuce is continuation-based multithread execution.In this project, we conducted the following research :・The continuation-based computing model was developed as a basis for thread level parallel computation. C-like programming language, Fuce-C, and its compiler system was developed to support to program the continuation-based thread level parallel processing.・The Fuce processor was designed and implemented on FPGA. The performance of the Fuce processor was evaluated using Fuce emulator on FPGA and software simulator.・The operating system, Cefos, was designed on the basis of continuation-based fine-grain multithreading, and evaluated its possibility using several multi-processing benchmarks.Through these researches, we showed the effectiveness of Fuce architecture. We implemented the FUCE proce … More ssor as an emulator machine on FPGA using VHDL and software simulator using Java. We evaluated several benchmarks, e.g, Prime-number by sieve algorithm, FFT, Quick sort, Merge sort and N-queen problem, with the FPGA emulator and software simulator. By this evaluation, we showed that the fine-grain multithreading is very effective for various kinds of parallel computations ; data parallel processing, pipeline parallel processing and their mixture. Especially, continuation-based multithreading technique is quite useful for high performance stream processing that is an important processing scheme for constructing OS kernel. As a result of the evaluation, we conclude the continuation-based fine-grain multithreading is a very effective method for solving the parallel processing problems such as OS kernel construction which requires basic concurrent and parallel processing techniques. We also evaluated the hardware cost of the FUCE processor and showed that the Fuce processor is hardware-cost effective compared to the commodity processors. We developed the OS kernel mechanisms based on the continuation-based fine-grain multithreading programs. And we showed the OS kernel could be constructed simple using the continuation-based fine-grain multithreading technique. We conducted a research on new architecture and basic mechanism of co-operation between hardware and software ; the kernel-ware, for parallel and distributed processing, and we showed that the Fuce kernel-ware is feasible developing the practical parallel processor. Our purpose in this research was to study the basic technology for developing a practical parallel-and-distributed machine, and we can now conclude that this research purpose was achieved. We will work further to make the Fuce machine more practical. We develop the Fuce-C language system and Cefos operating system on FPGS emulator and software simulator that runs on the commodity cluster machine. Then, we will evaluate the Fuce architecture using more scalable and practical benchmarks for parallel-and-distributed processing. Less