动态可重构平台操作系统中的资源管理问题研究
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摘要
随着集成电路工艺的快速发展,在单片系统上部署多个处理器核和加速器核的片上多核系统应用越来越广泛。同时,可重构技术的日益成熟使得在单片系统上搭建一个完整的可重构平台成为可能。然而,由于异构可重构计算平台上的计算资源具有多样性和多变性的特点,给操作系统的任务调度和资源管理都带来了极大的困难。
本文的工作围绕动态可重构平台操作系统中的资源管理问题展开,主要包含如下几个方面：
(1)可重构平台的硬件架构及操作系统架构
可重构平台都具有一些共同的特征,例如系统中都包含可重构资源FPGA,可以通过在FPGA上布局IP核加速程序的执行,平台都支持静态重构或者动态重构。本文提取了可重构平台的共同特征,提出了系统硬件架构的设计规范,包括IP核设计规范、异构计算单元之间的通信规范等。以可重构硬件平台为基础,本文构建了可重构平台上的操作系统的层次化模型,提出了统一的任务模型、资源模型和驱动模型,并采用面向服务的思想来设计操作系统中的任务调度算法和资源管理算法。
(2)可重构平台上的操作系统中的任务调度以及资源管理
可重构平台上的任务既包含硬件任务也包含软件任务,可重构平台上的资源除了包含通用处理器、外设、内存等之外,还包含可重构逻辑资源。本文根据可重构平台的任务特征以及资源特征,将对硬件任务和可重构资源的管理纳入到操作系统的范畴,通过在系统中添加硬件函数库以及定制软件函数库辅助完成任务调度和资源管理。
针对动态全局重构的特点,本文设计并实现了基于可变窗口的任务调度算法和资源管理算法,其中,任务调度算法与资源管理算法是分离的,任务调度算法与资源管理算法通过共享系统的某些全局信息进行通信。
针对动态部分重构的特点,本文设计了基于独立窗口的任务调度和资源管理算法,其中,任务调度算法和资源管理算法是紧密耦合在一起的。该方法降低了任务调度占用的处理器时间,并且能够保证每次重构请求的有效性。与基于可变窗口的任务调度算法和资源管理算法相比,该算法在不带来额外开销的情况下,能够更有效的利用可重构资源。
(3)可重构平台上的任务自动并行化
异构多核可重构平台上集成的计算资源日益增多,给有效并行利用计算资源带来了严峻的挑战,加剧了“编程墙”问题带来的影响。本文首先将任务级的Tomasulo算法进行了扩展,然后采用硬件的方式实现了该算法,并对该硬件模块功能的正确性以及调度性能进行了测试。硬件的Tomasulo算法作为一个硬件调度器与操作系统中的调度算法一起构成了调度系统；其中,操作系统中的调度算法负责解决任务级的控制相关,并将任务序列发送给硬件调度器；而硬件调度器检测任务之间的RAW相关并消除任务间的WAW相关和WAR相关,从而自动完成任务的并行化。
With the rapid development of integrated circuit technology, system on chip with multiple general purpose processors and accelerators is widely used. Meanwhile, the maturity of reconfigurable technology makes it possible to build a complete reconfigurable system on a single chip. However, due to the diverse and variable computing resources on heterogeneous reconfigurable platforms, it is posing significant challenges for task scheduling and resource management in the operating system design and implementation.
This dissertation focuses on the key techniques of resources management of operating systems for dynamic reconfigurable platforms. The study mainly includes the following aspects:
(1) Reconfigurable hardware platform and operating system architecture
Reconfigurable platforms have some common characteristics, such as integrating reconfigurable FPGA resources, accelerating the execution of programs via IP cores on FPGA, and supporting static or dynamic reconfiguration. Based on the characteristics of reconfigurable platforms, this dissertation proposes the design specifications of the hardware platform, including IP core design specifications, standardized communication between heterogeneous computing units. Through specifications that satisfy the hardware platform, this paper illustrates the hierarchical model of the operating system for reconfigurable platforms, and establishes a unified task model, resource model and driver model. On this basis, we propose task scheduling algorithms and resource management algorithms using service-oriented concepts.
(2) Task scheduling and resource management of operating systems for reconfigurable platforms.
The tasks of reconfigurable platforms include both hardware tasks and software tasks. Except for general purpose processors, peripherals, memory, the resources of reconfigurable platforms also include reconfigurable logic resources. Based on the characteristics of tasks and resources of reconfigurable platforms, we use operating systems to manage the hardware tasks and the reconfigurable resources. Hardware function library and custom software libraries are integrated in the system to facilitate task scheduling and resource management.
Regarding dynamic global reconfiguration, we design and implement a variable-window based task scheduling algorithm and resource management algorithm in which task scheduling and resource management are separated. Task scheduling algorithm and resource management algorithm communicate by sharing global information of the system.
Regarding dynamic partial reconfiguration, we design a separate-window based task scheduling algorithm and resource management algorithm in which task scheduling algorithm and resource management algorithm are tightly coupled. The algorithms could significantly shorten the processor time for scheduling and resource management and will guarantee that all reconfiguration requests will surely get a response. Compared with variable-window based task scheduling algorithm and resource management algorithm, separate-window based algorithms could achieve better resource utilization without additional overhead.
(3) Task-level automatic parallelization for reconfigurable platforms
The growing computing resources integrated within heterogeneous multicore platform are posing a great challenge to effectively utilize the parallel computing resources. The effect of "Programming wall" problem is becoming more and more serious. In order to deal with these problems, firstly, we extend the task level Tomasulo algorithm to a more general situation, and then we implement the algorithm via hardware circuits. Both the correctness and efficiency of the hardware module are tested. The hardware Tomasulo cooperates with the software scheduler of operating system in following ways:The software scheduler is responsible for resolving task-level control hazards, then the task sequence is sent to the hardware scheduler, which detects the task-level RAW hazard and eliminates task-level WAW hazards and WAR hazards. As a result, tasks can run in parallel automatically.
本文的工作围绕动态可重构平台操作系统中的资源管理问题展开,主要包含如下几个方面：
(1)可重构平台的硬件架构及操作系统架构
可重构平台都具有一些共同的特征,例如系统中都包含可重构资源FPGA,可以通过在FPGA上布局IP核加速程序的执行,平台都支持静态重构或者动态重构。本文提取了可重构平台的共同特征,提出了系统硬件架构的设计规范,包括IP核设计规范、异构计算单元之间的通信规范等。以可重构硬件平台为基础,本文构建了可重构平台上的操作系统的层次化模型,提出了统一的任务模型、资源模型和驱动模型,并采用面向服务的思想来设计操作系统中的任务调度算法和资源管理算法。
(2)可重构平台上的操作系统中的任务调度以及资源管理
可重构平台上的任务既包含硬件任务也包含软件任务,可重构平台上的资源除了包含通用处理器、外设、内存等之外,还包含可重构逻辑资源。本文根据可重构平台的任务特征以及资源特征,将对硬件任务和可重构资源的管理纳入到操作系统的范畴,通过在系统中添加硬件函数库以及定制软件函数库辅助完成任务调度和资源管理。
针对动态全局重构的特点,本文设计并实现了基于可变窗口的任务调度算法和资源管理算法,其中,任务调度算法与资源管理算法是分离的,任务调度算法与资源管理算法通过共享系统的某些全局信息进行通信。
针对动态部分重构的特点,本文设计了基于独立窗口的任务调度和资源管理算法,其中,任务调度算法和资源管理算法是紧密耦合在一起的。该方法降低了任务调度占用的处理器时间,并且能够保证每次重构请求的有效性。与基于可变窗口的任务调度算法和资源管理算法相比,该算法在不带来额外开销的情况下,能够更有效的利用可重构资源。
(3)可重构平台上的任务自动并行化
异构多核可重构平台上集成的计算资源日益增多,给有效并行利用计算资源带来了严峻的挑战,加剧了“编程墙”问题带来的影响。本文首先将任务级的Tomasulo算法进行了扩展,然后采用硬件的方式实现了该算法,并对该硬件模块功能的正确性以及调度性能进行了测试。硬件的Tomasulo算法作为一个硬件调度器与操作系统中的调度算法一起构成了调度系统；其中,操作系统中的调度算法负责解决任务级的控制相关,并将任务序列发送给硬件调度器；而硬件调度器检测任务之间的RAW相关并消除任务间的WAW相关和WAR相关,从而自动完成任务的并行化。
With the rapid development of integrated circuit technology, system on chip with multiple general purpose processors and accelerators is widely used. Meanwhile, the maturity of reconfigurable technology makes it possible to build a complete reconfigurable system on a single chip. However, due to the diverse and variable computing resources on heterogeneous reconfigurable platforms, it is posing significant challenges for task scheduling and resource management in the operating system design and implementation.
This dissertation focuses on the key techniques of resources management of operating systems for dynamic reconfigurable platforms. The study mainly includes the following aspects:
(1) Reconfigurable hardware platform and operating system architecture
Reconfigurable platforms have some common characteristics, such as integrating reconfigurable FPGA resources, accelerating the execution of programs via IP cores on FPGA, and supporting static or dynamic reconfiguration. Based on the characteristics of reconfigurable platforms, this dissertation proposes the design specifications of the hardware platform, including IP core design specifications, standardized communication between heterogeneous computing units. Through specifications that satisfy the hardware platform, this paper illustrates the hierarchical model of the operating system for reconfigurable platforms, and establishes a unified task model, resource model and driver model. On this basis, we propose task scheduling algorithms and resource management algorithms using service-oriented concepts.
(2) Task scheduling and resource management of operating systems for reconfigurable platforms.
The tasks of reconfigurable platforms include both hardware tasks and software tasks. Except for general purpose processors, peripherals, memory, the resources of reconfigurable platforms also include reconfigurable logic resources. Based on the characteristics of tasks and resources of reconfigurable platforms, we use operating systems to manage the hardware tasks and the reconfigurable resources. Hardware function library and custom software libraries are integrated in the system to facilitate task scheduling and resource management.
Regarding dynamic global reconfiguration, we design and implement a variable-window based task scheduling algorithm and resource management algorithm in which task scheduling and resource management are separated. Task scheduling algorithm and resource management algorithm communicate by sharing global information of the system.
Regarding dynamic partial reconfiguration, we design a separate-window based task scheduling algorithm and resource management algorithm in which task scheduling algorithm and resource management algorithm are tightly coupled. The algorithms could significantly shorten the processor time for scheduling and resource management and will guarantee that all reconfiguration requests will surely get a response. Compared with variable-window based task scheduling algorithm and resource management algorithm, separate-window based algorithms could achieve better resource utilization without additional overhead.
(3) Task-level automatic parallelization for reconfigurable platforms
The growing computing resources integrated within heterogeneous multicore platform are posing a great challenge to effectively utilize the parallel computing resources. The effect of "Programming wall" problem is becoming more and more serious. In order to deal with these problems, firstly, we extend the task level Tomasulo algorithm to a more general situation, and then we implement the algorithm via hardware circuits. Both the correctness and efficiency of the hardware module are tested. The hardware Tomasulo cooperates with the software scheduler of operating system in following ways:The software scheduler is responsible for resolving task-level control hazards, then the task sequence is sent to the hardware scheduler, which detects the task-level RAW hazard and eliminates task-level WAW hazards and WAR hazards. As a result, tasks can run in parallel automatically.
引文
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