基于交织预取率的帮助线程预取质量调节算法

预执行帮助线程在预取过程中需要进行动态预取调节,而传统静态枚举控制参数值的控制方法在预取执行过程中保持固定不变,从而使得该方法不能够有效的为主线程提供预取质量保证（quality of service,QoS）。针对该问题,提出了一种基于交织预取率的帮助线程预取质量参数调节方法。首先,对帮助线程的预取QoS优化进行了建模分析;其次,在前期交织预取工作的基础上,提出了基于交织预取率的帮助线程参数值调节算法;最后,在真实的商用多核平台上对所提出帮助线程预取调节算法进行了评测和分析。实验结果是所提出的帮助线程预取调节算法使得基准测试程序的几何平均性能加速比为1.114,而传统静态枚举方法的几何平均性能加速比为1.135。实验结果表明,所提出的帮助线程预取质量调节算法解决了帮助线程预取过程中的参数值自动调节问题,算法不需静态枚举参数值便可以快速获得与之相近似的预取性能提升。     

面向链式数据结构的间隔预取策略

由于链式数据结构的存储缺乏空间局部性,导致程序执行过程中对链式数据的访问会发生严重的Cache缺失行为。通过对面向链式结构的线程预取性能分析,研究链式数据结构程序热点循环的计算任务量与访存任务量比例特征对线程预取性能的影响。结合多核处理器平台特点,实现了一种适用于链式数据结构的帮助线程间隔预取方法。实验结果进一步验证了计算任务量与访存任务量比例特征对间隔预取性能的影响,表明间隔预取相比于传统线程预取技术有明显的性能优势。     

帮助线程预取技术研究综述

帮助线程预取是当前多核平台提高非规则数据密集应用预取效果性能的关键技术之一,近年来已成为国内外的研究热点。针对非规则数据密集应用访存规律的非连续局部性特征,帮助线程预取技术利用CMP平台的最后一级共享缓存(LLC)将应用的非连续局部性转换为瞬时的连续时空局部性(即时局部性),从而达到通过线程级数据预取提高程序性能的目的。归纳了帮助线程预取技术的分类,概括和比较了不同帮助线程实现技术的优势和局限性,深入分析和探讨了现有的几种典型帮助线程技术的预取控制策略。最后从帮助线程实时控制、参数动态选取和优化方面指出了帮助线程预取技术的研究方向。     

虚拟机环境下软件按需部署中的预取机制

针对大规模虚拟机环境下软件的按需部署,提出了一种基于预取的按需软件部署优化机制,能够降低用户端虚拟机的启动延迟以及为用户提供更好的虚拟机本地运行性能.基于用户使用软件的行为特点以及虚拟磁盘映像的细粒度分割,预取机制在后台对服务器端存储的虚拟磁盘映像进行预取,通过一种基于访问频率和优先级的预取目标识别算法AFPTR(access frequency and priority-based prefetch target recognition)和一种预取量动态调节机制,将预取集中在用户使用的少数小尺寸的虚拟磁盘映像上,并在预取过程中对预取量进行动态自适应地调节,以提高虚拟磁盘访问的本地命中率,进而提高用户端虚拟机的运行性能.基于QEMU虚拟机和Linux平台,实现了基于预取的按需软件部署原型系统.实验结果表明,预取机制能够有效地降低虚拟机的启动延迟,并能提高虚拟机的本地运行性能,支持虚拟机环境下按需、快速的软件部署.     

多线程环境的高效内存分配技术

介绍了一种新的多线程内存分配技术（NIXMalloc）的设计和实现,提出了两种高效的分配策略及其自适应调优方法,有效地提高多线程应用程序的内存管理性能;其中Local分配策略对超级块对象Span进行了线程私有化,基于超级块对象为单位的垃圾回收和内存布局调整使多线程性能更优越;Global分配策略采用了自适应调优方法,在动态检测应用程序内存使用情况的基础上进行内存预取和线程缓存限值的动态调整;实验证明NIXMalloc可改善内存管理性能,提高吞吐量,同时降低内存使用量;在多线程应用系统中能获得较好的时空效率。     

计算服务网格中基于服务聚类的元任务调度算法

在尊重网格资源本地调度策略前提下,提出一种基于云模型的动态服务能力评估方法;根据动态性能评估尺度对服务进行聚类,提出了一种基于PSO的自适应的服务动态聚类方法,将提供相同或相似QoS的服务划分到同一个服务簇中,从而缩小了任务调度的问题规模;基于服务动态聚类提出了一种元任务调度算法,理论分析该算法降低了不聚类调度算法的复杂度.实验结果表明本文提出的调度算法在时间复杂度与用户QoS保障方面优于以前提出的调度算法.     

一种自适应阶梯式广义预测控制方法

针对线性时滞系统存在控制调节时间长、超调量大的问题,提出了一种自适应阶梯式广义预测控制(ASGPC)算法。为了解决传统广义预测控制求解最优控制律时矩阵求逆计算量大的问题,引入限定控制量变化的阶梯因子。固定阶梯因子未考虑到系统的动态响应过程,制约了控制性能的进一步提高。在分析不同阶梯因子对系统控制性能影响的基础上,设计了一个依赖于系统调节误差的自适应阶梯因子,根据系统响应偏差情况对阶梯因子进行动态调整。仿真结果表明,ASGPC算法能够根据系统误差实时调整阶梯因子,与广义预测控制、阶梯式广义预测控制相比,改善了系统的动态性能,调节时间更短、超调量更小。     

低资源成本的云计算自适应实时资源监控研究

分布式云计算的云端数据量巨大,而云端资源监控则需对大数据采样,因而产生大量的计算开销与资源消耗,对此,提出了一种两阶段的自适应监控方案。首先,在训练阶段,采集适量的资源样本,计算其资源变化相关的阈值参数,并计算资源变化的质量值;然后,在自适应监控阶段,使用训练阶段的算法,根据不同的资源变化剧烈程度更新采样周期,从而实现动态的采样周期调节。试验结果证明,与静态监控算法相比,该算法具有较好的监控性能,同时具有较低的资源开销。     

基于工业控制以太网的QoS机制研究

针对目前工业控制以太网实施QoS控制的难点，本文提出了一种基于分层结构的动态自适应QoS控制模型，并根据工业控制通信的特点，采用基于MPCA算法的数据挖掘技术，建立综合网管平台，实现良好的QoS监控管理。     

小计算量下非规则数据密集型热函数的性能优化

随着云计算的兴起和发展,基于多核的非规则数据密集型应用越来越多,而大量的数据缺失问题导致这类应用的性能严重下降.利用空闲核资源的传统帮助线程方法试图提前将主线程所需要的非规则数据放入共享的最后一级缓存(last layer cache,LLC),如果帮助线程相对于主线程具有恰当的运算速度,能在主线程访问之前将有关缺失数据放入LLC中,则热函数的性能可被改进.然而,如果热函数缺乏计算任务(称之为小计算量热函数),使用这样的传统方法就无法构建一个相对于主线程有效预取的帮助线程,其热函数性能的改善将会大大降低.针对源代码级小计算量下非规则数据密集型热函数的性能优化问题,先对帮助线程预取QoS进行了形式化描述.在此基础上,通过引入提前量等参数模型,提出了一种小计算量下热函数的性能优化方法.在Intel Core 2 Duo Processor 6550处理器上,通过对科学计算测试程序em3d,mst和SPEC CPU benchmark 2006中的mcf的进行实验,相对于传统方法分别获得了1.97%,31.63%和1.10%的性能提升.     

Software Controlled Adaptive Pre-Execution for Data Prefetching

Data prefetching mechanisms are widely used for hiding memory latency in data intensive applications. They mask the speed gap between CPUs and their memory systems by preloading data into the CPU caches, where accessing them is by at least one order of magnitude faster. Pre-execution is a combined prefetching method, which executes a slice of the original code preloading the code and its data at the same time. Pre-execution is often mentioned in the literature, but according to our knowledge, it has not been formally defined yet. We fill this void by presenting the formal definition of speculative and non-speculative pre-execution, and derive a lightweight software-based strategy which accelerates the main working thread by introducing an adaptive, non-speculative pre-execution helper thread. This helper thread acts as a perfect predictor, calculates memory addresses, prefetches the data and consumes cache misses early. The adaptive automatic control allows the helper thread to configure itself in run-time for best performance. The method is directly applicable to any data intensive application without requiring hardware modifications. Our method was able to achieve an average speedup of 10–30% in a real-life application.     

The Performance Optimization of Threaded Prefetching for Linked Data Structures

Helper threaded prefetching based on Chip Multiprocessor is a well known approach to reducing memory latency and has been explored in linked data structures accesses. However, conventional helper threaded prefetching often suffers from useless prefetches and cache thrashing, which affect its effectiveness. In this paper, we first analyzed the shortcomings of conventional helper threaded prefetching for linked data structures. Then we proposed an improved helper threaded prefetching, Skip Helper Threaded Prefetching, for hotspots with two level data traversals. Our solution is to profile the applications and balance delinquent loads between main thread and prefetching thread based on the characteristic of operations in their hotspots. Evaluations show that the proposed solution improves average performance by 8.9% (-O2) and 8.5% (-O3) over the conventional helper threaded prefetching that greedily prefetches all delinquent loads. We also compare our proposal with the active threaded prefetching which synchronizes with main thread by semaphore, and find that our proposal provides better performance for the targeted applications.     

基于云免疫算法的认知无线网络参数优化

为了提高认知无线网络的参数优化效果,提出了一种基于免疫优化的认知引擎参数调整算法。免疫克隆优化是一种有效的智能优化算法,适合求解认知无线网络的引擎参数调整问题。免疫优化中,变异概率影响着算法的搜索能力;利用正态云模型云滴的随机性和稳定倾向性特点,提出了一种基于云模型的自适应变异概率调整方法,并用于认知无线网络的参数优化。在多载波环境下对算法进行了仿真实验。结果表明,所提算法收敛速度较快,参数调整结果与对目标函数的偏好一致,能够实现认知引擎参数优化。     

基于自适应蚁群算法的QoS组播路由算法

提出一种改进的自适应蚁群优化算法,在信息素更新策略中引入全局最优系数,研究多约束条件下的QoS组播路由问题。动态更新信息素能够确保自适应地改进全局搜索能力和收敛性能,避免陷入局部最优解。仿真结果表明,该算法比蚂蚁-遗传算法在解决多约束条件下的QoS组播路由问题时更有效。     

面向QoS保障的虚拟可信服务*

针对服务计算模型中对应用的可信特性支持不足问题,提出面向QoS保障的虚拟可信服务VTS,并依据VTS设计了一种基于反馈控制的自适应QoS保障机制。首先将VTS的QoS保障转换为反馈控制问题,给出一种自适应QoS保障框架,再在此框架下对VTS的QoS维护过程和策略进行建模,设计和实现了相应的动态组建、调节算法和实时评估策略。最后通过仿真实验的结果分析表明,该保障机制能够有效地增强VTS对服务实体运行时的QoS保障能力。     

Estimating Effective Prefetch Distance in Threaded Prefetching for Linked Data Structures

Helper threaded prefetching based on chip multiprocessor has been shown to reduce memory latency and improve overall system performance, and has been explored in linked data structures accesses. In our earlier work, we had proposed an effective threaded prefetching technique that balances delinquent loads between main thread and helper thread to improve effectiveness of prefetching. In this paper, we analyze memory access characteristic of specific application to estimate effective prefetch distance range for our proposed threaded prefetching technique. The effect of hardware prefetchers on the estimation is also exploited. We discuss key design issues of our proposed method and present preliminary experimental results. Our experimental evaluations indicated that the bounded range of effective prefetch distance can be determined using our method, and the optimal prefetch distances can be determined based on the estimated effective prefetch distance range by few trial runs.     

改进二进制和声搜索算法求解多维背包问题

和声搜索（HS）是一种已广泛应用于连续优化问题的元启发式方法。针对典型的组合优化问题——多维背包问题（MKP）,提出一种改进二进制和声搜索（IBHS）算法。算法通过伯努利随机过程生成二进制群体,在候选和声生成算子中,引入动态自适应参数,通过算法参数的自适应调整来协调算法的全局搜索和局部搜索,并提出一种新的更有效的衡量商品多维加权价值密度的方法用于二进制个体修正和优化;引入精英局部搜索机制进行协同寻优,提高IBHS的收敛速度。通过求解10组不同规模的典型多维背包算例和与贪心二进制狮群优化（GBLSO）算法、改进的差分演化（MBDE）算法以及二进制修正和声（BMHS）算法的对比分析,实验结果表明,所提算法在求解MKP时有具有良好的收敛效率、较高的寻优精度和很好的鲁棒性。     

Prefetching with Helper Threads for Loosely Coupled Multiprocessor Systems

This paper presents a helper thread prefetching scheme that is designed to work on loosely coupled processors, such as in a standard chip multiprocessor (CMP) system or an intelligent memory system. Loosely coupled processors have an advantage in that resources such as processor and L1 cache resources are not contended by the application and helper threads, hence preserving the speed of the application. However, interprocessor communication is expensive in such a system. We present techniques to alleviate this. Our approach exploits large loop-based code regions and is based on a new synchronization mechanism between the application and helper threads. This mechanism precisely controls how far ahead the execution of the helper thread can be with respect to the application thread. We found that this is important in ensuring prefetching timeliness and avoiding cache pollution. To demonstrate that prefetching in a loosely coupled system can be done effectively, we evaluate our prefetching by simulating a standard unmodified CMP system and an intelligent memory system where a simple processor in memory executes the helper thread. Evaluating our scheme with nine memory-intensive applications with the memory processor in DRAM achieves an average speedup of 1.25. Moreover, our scheme works well in combination with a conventional processor-side sequential L1 prefetcher, resulting in an average speedup of 1.31. In a standard CMP, the scheme achieves an average speedup of 1.33. Using a real CMP system with a shared L2 cache between two cores, our helper thread prefetching plus hardware L2 prefetching achieves an average speedup of 1.15 over the hardware L2 prefetching for the subset of applications with high L2 cache misses per cycle.     

融合可信性评价的Web服务组合QoS优化

针对复杂网络环境下存在的恶意欺诈行为,提出一种将服务可信性评价和基于QoS感知的服务组合优化相结合的方法框架.首先,基于Web服务的历史行为分别采用贝叶斯学习理论和用户评价方法,从客、主观两方面对Web服务可信性进行评估;然后,利用可信性度量后的QoS属性构建多目标优化模型,并提出一种改进的多目标灰狼优化(IMOGWO)算法用于模型求解;最后,通过实验数据验证所提出方法框架在服务组合优化时的有效性.