基于最大空闲矩形的可重构资源管理方法

可重构硬件如FPGA的规模和集成度的提高使其承载的硬件任务越来越多,FPGA的动态部分重构能力使任务可在系统运行过程中动态地添加或者删除而不影响其他任务的运行,对可重构硬件的资源管理非常重要。该文提出一种基于任务上边界计算最大空闲矩形的算法,使用这些最大空闲矩形能够有效地管理可重构资源,便于更好地利用具有动态部分重构能力的可重构硬件。     

动态部分可重构系统空闲资源全集管理研究

可重构系统兼具了传统处理器的灵活性和接近于ASIC的计算速度,FPGA的动态部分重构能够实现计算和重构操作的同时进行,使系统能够动态地改变任务的运行。在动态部分可重构系统中,高效的空闲资源管理策略对系统整体性起着非常重要的作用。提出了一种基于单向栈的算法来寻找最大空闲矩形(MFR)。利用可重构计算单元的不同叮值进出单向栈来找到所有最大空闲矩形。通过实验表明,算法通过使用单向找与算法优化,有效地提高了查找空闲资源全集的性能。     

可重构系统中硬件任务布局布线算法研究

可重构计算系统中,二维可重构硬件任务的布局布线问题是影响系统资源利用率的重要因素。在异质化的可重构器件和任务模型基础上,对可重构硬件任务进行了适当分类,并提出一种能够对多类型可重构硬件任务同时布局布线的算法DRS-TCW。实验表明,该算法能够有效提高可重构器件的资源利用率和任务布线连通率。     

基于遗传算法的可重构系统软硬件划分

在考虑动态部分重构及重构延时等特征的基础上,采用遗传算法及其与爬山算法的融合实现可重构系统软硬件任务的划分,并采用动态优先级调度算法进行划分结果的评价。实验表明,在可重构系统的资源约束等条件下,算法能够有效地实现应用任务图到可重构系统的时空映射。     

基于改进ESLA算法的可重构资源管理

针对动态可重构系统的空闲资源管理问题,改进基于最大空闲矩形的增强型扫描线算法(ESLA),采用一维数组作为辅助空间,同时搜索有效宽度与最大空闲矩形。改进算法能快速计算出可重构系统在运行过程中的所有最大空闲矩形,实现任务间资源的合理分配。实验结果表明,改进算法能减少运行时间开销和存储空间代价,提高可重构系统的资源利用率。     

一种采用边界表进行可重构资源管理及硬件任务调度的算法

可重构计算兼具硬件的高效性和软件的灵活性,发挥可重构计算的高性能,对可重构资源及硬件任务进行有效管理和科学调度是关键.针对一维可重构器件中硬件任务调度问题,提出一种基于边界表的可重构资源管理方法,该方法用"边界表"数据结构记录R-T坐标系中的区域边界及其位置关系,实现对可重构资源的管理.以此为基础,提出了R-T坐标系下的任务调度及布局算法:BT-P算法,实现硬件任务的调度和布局.算法采用加权边界重叠长度作为任务调度的估值函数,与采用边界表的资源管理方法相结合,以较小的运行时开销实现调度的优化.实验表明,与Stuffing算法相比,BT-P算法下的可重构硬件的器件利用率随负载率的变化提高5%～11%,任务拒绝率随负载率和松弛因子的变化降低9%～11%,每个任务的平均调度布局时间开销在2～4μs之间.     

动态可重构技术研究综述

动态可重构技术可使硬件设备在运行时根据不同的计算任务实现不同的功能,在发挥应用程序效率的同时,又能充分利用系统软硬件资源。根据固定器件与可重构器件的关系,可以将可重构系统划分为不同的结构。适应各自结构的特点,将任务合理的分解为软件部分和硬件部分,是高效完成计算任务的基础。当硬件任务较多时,系统需要一个良好的算法来进行调度。最后,可重构系统应该为用户提供一个结构透明的开发平台,使用户可以方便的利用可重构计算的强大能力。     

采用预配置策略的可重构混合任务调度算法

在对可重构硬件资源进行抽象的基础上,采用软硬件混合任务有向无环图来描述应用,提出一种基于列表的混合任务调度算法.该算法通过任务计算就绪顺序及可重构资源状态确定硬件任务的动态预配置优先级,按此优先级进行硬件任务预配置,隐藏硬件任务的配置时间,从而获得硬件任务运算加速.实验结果表明,针对可重构系统中的软硬件混合任务调度,能够有效地降低配置时间对应用执行时间的影响.     

面向异质结构的可重构任务在线布局算法

基于同质结构模型的可重构任务布局算法和内部资源多样的可重构器件不相适应,不利于实际运用.针对BlockRAM等静态单元在器件上的分布对硬件任务存在位置约束的问题,建立了异质结构的器件和硬件任务模型,并提出一种基于相对任务覆盖度的在线布局算法.通过为布局任务等待队列设立滑动窗口,根据窗内任务集合对器件空闲单元的相对任务覆盖度选择当前任务的放置位置,兼顾后续任务的布局需求,从而提高了整体布局效率.实验结果表明,该算法能取得较低的任务平均等待时间和较高的器件利用率,优于First Fit算法.     

一种异构可重构片上系统的实时任务调度算法

动态可重构系统中为新到达的任务实时地安排任务启动时间和放置位置是硬件任务调度算法的关键.硬件任务的调度在很大程度上影响可重构计算系统的性能.提出了一种基于二维资源模型的分组-邻接边在线调度算法,该算法将硬件任务按照长宽比分为垂直任务和水平任务两组分别考虑在可重构资源上的放置位置,同时引入任务邻接边数作为选择合理放置位置的重要指标,可使得硬件任务放置更为紧凑,减少资源碎片,提高调度成功率.对两种硬件任务放置策略进行了对比,结果表明尽可能旱的安排任务启动有利于提升高负载情况下的调度成功率.仿真实验表明,与已有算法相比,该算法具有更高的任条接受率,而运行时开销没有显著增加.     

Configuration Reusing in On-Line Task Scheduling for Reconfigurable Computing Systems

Reconfigurable computing systems can be reconfigured at runtime and support partial reconfigurability which makes us able to execute tasks in a true multitasking manner.To manage such systems at runtime,a reconfigurable operating system is needed.The main part of this operating system is resource management unit which performs on-line scheduling and placement of hardware tasks at runtime.Reconfiguration overhead is an important obstacle that limits the performance of on-line scheduling algorithms in reconfigurable computing systems and increases the overall execution time.Configuration reusing (task reusing) can decrease reconfiguration overhead considerably,particularly in periodic applications or the applications in which the probability of tasks recurrence is high.In this paper,we present a technique called reusing-based scheduling (RBS),for on-line scheduling and placement in which configuration reusing is considered as a main characteristic in order to reduce reconfiguration overhead and decrease total execution time of the tasks.Several experiments have been conducted on the proposed algorithm.Obtained results show considerable improvement in overall execution time of the tasks.     

基于可重构系统的亚可抢占任务调度算法

针对目前在线调度算法忽略预留任务特殊性的问题,基于现有的放置策略,定义并证明一个可靠的基于最大邻接边数的放置策略。提出一种基于亚可抢占性的任务调度算法,即剥夺预留任务所占用的可重构资源再进行统一离线调度。实验表明,与已有算法相比,该算法具有更高的任务接受率和芯片利用率,且并未明显增加运行时的开销。     

基于放置代价的可重构系统任务统一调度算法

高效的任务调度算法对可重构系统的性能有极大的影响。针对目前可重构系统任务在线调度算法的不足,提出了一种基于放置代价的调度算法。该算法考虑了3种代价,分别为:硬件任务在FPGA上的执行时间、占用的FPGA面积以及FPGA的碎片情况,并且也考虑了软硬件任务的统一调度。在调度过程中,当代价超过设定的阈值时,就拒绝其在FPGA上运行,并由CPU执行其软实现。通过合理地拒绝一些代价较大的任务,能够从整体上提高任务调度成功率。实验表明,同已有算法相比,该算法能够获得更高的任务截止保证率。     

Online scheduling and placement of hardware tasks with multiple variants on dynamically reconfigurable field-programmable gate arrays

Hardware task scheduling and placement at runtime plays a crucial role in achieving better system performance by exploring dynamically reconfigurable Field-Programmable Gate Arrays (FPGAs). Although a number of online algorithms have been proposed in the literature, no strategy has been engaged in efficient usage of reconfigurable resources by orchestrating multiple hardware versions of tasks. By exploring this flexibility, on one hand, the algorithms can be potentially stronger in performance; however, on the other hand, they can suffer much more runtime overhead in selecting dynamically the best suitable variant on-the-fly based on its runtime conditions imposed by its runtime constraints. In this work, we propose a fast efficient online task scheduling and placement algorithm by incorporating multiple selectable hardware implementations for each hardware request; the selections reflect trade-offs between the required reconfigurable resources and the task runtime performance. Experimental studies conclusively reveal the superiority of the proposed algorithm in terms of not only scheduling and placement quality but also faster runtime decisions over rigid approaches.     

Resource management and task partitioning and scheduling on a run-time reconfigurable embedded system

There are many design challenges in the hardware-software co-design approach for performance improvement of data-intensive streaming applications with a general-purpose microprocessor and a hardware accelerator. These design challenges are mainly to prevent hardware area fragmentation to increase resource utilization, to reduce hardware reconfiguration cost and to partition and schedule the tasks between the microprocessor and the hardware accelerator efficiently for performance improvement and power savings of the applications.In this paper a modular and block based hardware configuration architecture named memory-aware run-time reconfigurable embedded system (MARTRES) is proposed for efficient resource management and performance improvement of streaming applications. Subsequently we design a task placement algorithm named hierarchical best fit ascending (HBFA) algorithm to prove that MARTRES configuration architecture is very efficient in increased resource utilization and flexible in task mapping and power savings. The time complexity of HBFA algorithm is reduced to O(n) compared to traditional Best Fit (BF) algorithm’s time complexity of O(n²), when the quality of the placement solution by HBFA is better than that of BF algorithm. Finally we design an efficient task partitioning and scheduling algorithm named balanced partitioned and placement-aware partitioning and scheduling algorithm (BPASA). In BPASA we exploit the temporal parallelism in streaming applications to reduce reconfiguration cost of the hardware, while keeping in mind the required throughput of the output data. We balance the exploitation of spatial parallelism and temporal parallelism in streaming applications by considering the reconfiguration cost vs. the data transfer cost. The scheduler refers to the HBFA placement algorithm to check whether contiguous area on FPGA is available before scheduling the task for HW or for SW.     

动态可重构片上系统的任务在线调度算法

针对动态可重构系统中为新到达任务安排任务启动时间和放置位置的问题,提出一种基于顶点链表的Look-aheadest算法。将任务调度的时间作为时间维,通过已到达任务与已放置任务在三维空间的邻接面构建代价函数,获取具有最大代价函数值的放置位置和启动时间,使硬件任务放置更为紧凑,提高调度成功率。仿真结果表明,在可接受的运行开销内,该算法能有效提高任务接受率。