Win32环境下的多线程同步技术的研究

由于多线程同步技术是当今软件开发中的一项重要技术,所以在许多软件中得到广泛的应用。针对目前Win32环境下几种常用的线程同步技术进行了详细的研究,分析了它们各自的特点,总结了各种线程同步技术的应用场合。为了能够进一步提高多线程同步技术的执行效率,文中在此基础上,提出了一种利用链表对现有的线程同步技术进行改进的方法。改进后的算法较好地解决了CPU时间片的浪费问题,提高了CPU时间片的利用率。     

MFC多线程技术在多媒体教学系统上的应用

多线程技术是多任务实时处理程序常用到的技术，本文对在MFC环境下如何生成线程，以及线程间的通信与同步机制进行了探讨，最后介绍了应用MFC多线程技术在多媒体教学系统上的应用。     

Windows 多线程同步技术研究

首先对多线程的优点和缺点及线程同步重要性进行了论述,然后重点对Windows操作系统线程问多种同步技术进行深入探讨,对每种同步技术给出了应用实例,并提供了应用实例源代码。     

基于VB.NET的多线程技术应用

采用多线程技术可充分提高应用程序运行效率,微软的.NET框架提供了新的线程类库,从而可以方便地创建多线程应用程序。本文首先简述了多线程应用的意义,介绍了进程、线程以及应用程序域的相互关系,随后举例介绍了使用Visual Basic.NET进行线程的创建与管理的过程,通过对Thread基类的引用可创建一个线程,然后可利用线程的start等方法属性进行线程的管理,并强调了线程的同步技术的应用,通过多线程编程技术的应用来开发效率更高、响应速度更快的应用程序。     

基于三维视景的火箭飞行安控系统的任务管理

在PC机上进行基于三维视景的功能应用开发需要考虑充分并合理利用CPU资源的问题。该文分析基于三维视景的火箭飞行安全控制系统的多任务组成,并从多线程技术角度讨论系统的任务管理方法,即系统各个组成结构模块的任务调度策略和运行方式,解决系统多线程实现中的一些关键问题,包括任务的驱动类型、时钟管理、线程同步与互斥方法,并使用任务模式简化了视景渲染线程与其他线程的交互,保证了系统多任务处理时正确的逻辑关系和数据传递的正确性。     

基于Java synchronized同步锁实现线程交互

Java多线程能够提高CPU利用效率,但也容易造成线程不安全、线程死锁等问题。本文详细介绍了Java线程各状态之间的关系及其切换,并用实例展示了使用同步锁synchronized保证同一时刻只有一个线程操作同一资源,使用wait()、notify()切换线程状态保证线程操作的前后顺序实现线程交互。理解Java线程各状态之间的关系及其切换,能帮助用户在使用Java多线程的场景有效避免多线程带来的不安全问题。     

LINUX系统下多线程与多进程性能分析

采用多进程处理多个任务,会占用很多系统资源(主要是CPU和内存的使用)。在LINUX中,则对这种弊端进行了改进,在用户态实现了多线程处理多任务。本文系统论述了多线程间通信技术的使用,通过对单cpu系统中多线程和多进程技术的比较和和性能分析,结合线程间通信技术的有关特性提出了应用该项技术所应遵循的原则和思路,并提出了改进思路,同时还讨论了多线程通信中存在的一些问题。     

LINUX系统下多线程与多进程性能分析

采用多进程处理多个任务,会占用很多系统资源(主要是CPU和内存的使用).在LINUX中,则对这种弊端进行了改进,在用户态实现了多线程处理多任务.本文系统论述了多线程间通信技术的使用,通过对单cpu系统中多线程和多进程技术的比较和和性能分析,结合线程间通信技术的有关特性提出了应用该项技术所应遵循的原则和思路,并提出了改进思路,同时还讨论了多线程通信中存在的一些问题.     

线程级并行计算在图形渲染引擎中的研究

针对并行计算技术在当前图形渲染系统中应用层面较浅的问题,为提高多核平台下图形应用程序CPU利用率,提出了一种新的Fork-Join多线程渲染方案。对当前流行的开源图形渲染引擎——OGRE引擎的渲染框架进行了多线程优化,用OpenMP方法对图形引擎的逻辑帧实现并行化,根据Win32线程库和DirectX11的多线程支持提出了一种渲染帧并行化方法,并将其应用于图形引擎。在多核平台上的实验结果表明,该方案能有效提高渲染速率和CPU利用率,改善CPU负载均衡。     

LabWindows/CVI多线程技术在电负载系统中的应用

针对电负载系统程序在顺序执行时,CPU利用率低且容易阻塞等问题,提出了利用LabWindows/CVI的多线程技术,建立独立的线程池线程分别用于数据采集和数据处理的方法.用多线程的方法解决了程序任务堵塞和资源利用率低的问题,提高了整体性能和系统的可靠性、稳定性.同时利用线程安全队列TSQ在线程间传递数据解决了数据同步的...     

红外景象产生器中的线程同步问题研究与实现

该文将Ｗｉｎｄｏｗｓ ＮＴ操作系统中的线程调度机制和应用程序中的线程同步控制方法相结合,对红外景象产生器软件系统中的各线程的性能进行了分析,研究了其典型线程之间的同步实现方法,同时提出了为提高应用程序效率而进行的改进并介绍了新系统的整体性能。实验证明,改进后的系统运行速度有显著的提高,应用程序对ＣＰＵ也实现了较高的利用率。     

飞行颤振数据处理软件多线程编程的有效实现

多线程编程提高了飞行颤振数据处理软件的灵活性和易操作性，但也增加了软件编程和维护的复杂程度。针对这一矛盾，本文引进新的设计思想，提出新的多线程编程框图实现方法（Diagram method for multiple thread programming,DM/MTP），从而简化多线程的设计思路，特别适合于大型工程软件的设计。文中详细介绍了DM／MTP在模态参数识别算法NLUP中的应用。     

Windows操作系统多核CPU内核线程管理方法

Windows是采用CPU时间片轮转多任务分配机制的非实时操作系统,无法满足实时性要求比较高的任务需要。而随着CPU技术的快速发展,当前市场上双核甚至多核CPU已成为主流,这使得在Windows平台上用多核CPU的某些核独立执行任务,从而有效地提高软件的实时性成为可能。本文介绍一种在Windows内核模式下编写Windows驱动程序,有效分配多核CPU资源从而提高软件实时性的方法。     

CPU/FPGA混合架构上的硬件线程加速方法

CPU/FPGA混合架构是可重构计算的普遍结构,为了简化混合架构上FPGA的使用,提出了一种硬件线程方法,并设计了硬件线程的执行机制,以硬件线程的方式使用可重构资源.同时,软硬件线程可以通过共享数据存储方式进行多线程并行执行,将程序中计算密集部分以FPGA上的硬件线程方式执行,而控制密集部分则以CPU上的软件线程方式执行.在Simics仿真软件模拟的混合架构平台上,对DES,MD5SUM和归并排序算法进行软硬件多线程改造后的实验结果表明,平均执行加速比达到了2.30,有效地发挥了CPU/FPGA混合架构的计算性能.     

Analyzing GPU-controlled communication with dynamic parallelism in terms of performance and energy

Graphic Processing Units (GPUs) are widely used in high performance computing, due to their high computational power and high performance per Watt. However, one of the main bottlenecks of GPU-accelerated cluster computing is the data transfer between distributed GPUs. This not only affects performance, but also power consumption. The most common way to utilize a GPU cluster is a hybrid model, in which the GPU is used to accelerate the computation, while the CPU is responsible for the communication. This approach always requires a dedicated CPU thread, which consumes additional CPU cycles and therefore increases the power consumption of the complete application. In recent work we have shown that the GPU is able to control the communication independently of the CPU. However, there are several problems with GPU-controlled communication. The main problem is intra-GPU synchronization, since GPU blocks are non-preemptive. Therefore, the use of communication requests within a GPU can easily result in a deadlock. In this work we show how dynamic parallelism solves this problem. GPU-controlled communication in combination with dynamic parallelism allows keeping the control flow of multi-GPU applications on the GPU and bypassing the CPU completely. Using other in-kernel synchronization methods results in massive performance losses, due to the forced serialization of the GPU thread blocks. Although the performance of applications using GPU-controlled communication is still slightly worse than the performance of hybrid applications, we will show that performance per Watt increases by up to 10% while still using commodity hardware.     

通用线程库的设计与实现

随着采集、处理、解释一体化地震勘探软件的发展,构建跨硬件、操作系统的云计算平台成为必要;而云计算平台中,通用线程库成为开发有大计算量算法的地震勘探软件的关键.通过对线程模型和同步机制的分析,分类整理出线程的属性、线程的控制、线程执行流的构建和同步的属性及操作;最后在这些通用的组件基础上,通过对各种本地线程库的封装,实现基于任意本地线程库的跨平台通用线程库.     

Toward low CPU usage and efficient DPDK communication in a cluster

In recent years, DPDK (Data Plane Development Kit, a data plane development tool set provided by Intel, focusing on high-performance processing of data packets in network applications), one of the high-performance packet I/O frameworks, is widely used to improve the efficiency of data transmission in the cluster. But, the busy polling used in DPDK will not only waste a lot of CPU cycles and cause certain power consumption, but also the high CPU usage will have a great impact on the performance of other applications in the host. Although some technologies, such as DVFS (dynamic voltage and frequency scaling, which is to dynamically adjust the operating frequency and voltage of the chip according to the different needs of the computing power of the application running on the chip, so as to achieve the purpose of energy saving) and LPI (low power idle, a technology that saves power by turning off the power of certain supporting circuits when the CPU core is idle), can reduce power consumption by adjusting CPU voltage and frequency, they can also cause performance degradation in other applications. Using thread sleep technology is a promising method to reduce the CPU usage and power consumption. However, it is challenging because the appropriate thread sleep duration cannot be obtained accurately. In this paper, we propose a model that finds the optimal thread sleep duration to solve the above challenges. From the model, we can balance the thread CPU usage and transmission efficiency to obtain the optimal sleep duration called the transmission performance threshold. Experiments show that the proposed models can significantly reduce the thread CPU usage. Generally, while the communication performance is slightly reduced, the CPU utilization is reduced by about 80%.

     

Parallelization of a color-entropy preprocessed Chan–Vese model for face contour detection on multi-core CPU and GPU

Face tracking is an important computer vision technology that has been widely adopted in many areas, from cell phone applications to industry robots. In this paper, we introduce a novel way to parallelize a face contour detecting application based on the color-entropy preprocessed Chan–Vese model utilizing a total variation G-norm. This particular application is a complicated and unsupervised computational method requiring a large amount of calculations. Several core parts therein are difficult to parallelize due to heavily correlated data processing among iterations and pixels.We develop a novel approach to parallelize the data-dependent core parts and significantly improve the runtime performance of the model computation. We implement the parallelized program on OpenCL for both multi-core CPU and GPU. For 640 * 480 input images, the parallelized program on a NVidia GTX970 GPU, a NVidia GTX660 GPU, and an AMD FX8530 8-core CPU is on average 18.6, 12.0 and 4.40 times faster than its single-thread C version on the AMD FX8530 CPU, respectively. Some parallelized routines have much higher performance improvement compared to the whole program. For instance, on the NVidia GTX970 GPU, the parallelized entropy filter routine is on average 74.0 times faster than its single-thread C version on the AMD FX8530 8-core CPU. We discuss the parallelization methodologies in detail, including the scalability, thread models, as well as synchronization methods for both multi-core CPU and GPU.     

基于NUMECA FINE/Turbo的并行计算测试

为具体了解CFD软件NUMECA FINE/Turbo的并行计算性能,良好把握后续的科研工作进度,分别研究在激活超线程情况下单节点计算与多节点并行计算以及CPU在激活超线程前、后计算速度的差异.结果表明:在多节点并行计算时,计算速度与实际参加并行计算的CPU物理核心数量成正比;在激活超线程的情况下,并行计算节点数在超过实际物理核心数后明显降低计算速度的提升.