摘要：Hardware/software partitioning is an essential step in hardware/software *** large size problems,it is difficult to consider both solution quality and *** paper presents an efficient GPU-based parallel tabu search algorithm(GPTS)for HW/SW partitioning.A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically.A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of *** further minimize the transfer overhead of GPTS between CPU and GPU,an optimized transfer strategy for GPU-based tabu evaluation is proposed,which considers that all the candidates do not satisfy the given *** show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW *** proposed parallelization is significant when considering the ordinary GPU platform.

摘要：软硬件划分是软硬件协同设计中关键步骤之一,并且随着设计复杂度的增加,逐步成为一个具有挑战性的优化问题.提出一种基于从众和声粒子群算法(conformity particle swarm optimization with harmony search,CPSO-HS)的并行软硬件划分方法.按生物行为学理论,个体粒子具有从众行为,趋向于靠近群体粒子聚集的安全地点,以避免被捕食者袭击.CPSO-HS算法通过模拟这种从众行为,能够保持搜索种群的多样性,以避免陷入局部最优,有利于逼近全局最优点.通过改进和声搜索算法(harmony search,HS)的初始化策略,将HS集成到CPSO-HS中,在当前全局最优解附近提高算法的搜索精度,有利于提升解的质量.以上两步的有机结合,增强了CPSO-HS算法搜索的多样性和集中性.进一步考虑软硬划分方法的特殊性,其中最耗时的过程是计算软硬件的通讯代价,因此在常用的PC平台上采用并行策略加速该过程,以便在大规模的软硬件划分问题中有效减少整体运行时间.最后,通过基准任务测试集验证了本文方法的有效性.

摘要：软硬件划分是软硬件协同设计中的关键步骤,决定了哪些功能由硬件执行,哪些功能由软件执行.软硬件划分属于NP难问题.现代嵌入式系统的复杂性提高,造成软硬件划分问题规模变大,需要采用启发式方法求解.禁忌搜索是求解软硬件划分的有效方法.然而,算法的求解过程非常耗时.已有的禁忌搜索求解软硬件划分是串行实现,要折中考虑解的质量和算法的运行时间.这种考虑牺牲了解的质量.本文提出基于GPU的自适应邻域压缩(compacting neighborhood)禁忌搜索的软硬件划分算法.首先,提出自适应策略.自适应策略能够增强算法的搜索集中性,提高解的质量.GPU的大规模并行特性可以降低算法的运行时间.其次,为了使算法在GPU上高效地执行,提出基于GPU的任务图表达、线程–候选解映射、数据布局和访存等一系列优化策略.最后,实验采用统一设备架构(CUDA)编程,并根据相关基准任务图,通过不同的计算–通信比和实时约束条件,对提出的方法进行验证.结果表明,本文方法的解质量要优于已有的方法.对比将自适应邻域压缩禁忌搜索自然移植到GPU后的运行时间,提出的GPU上的执行优化策略明显地降低了求解时间.另外,在更大规模的软硬件划分上验证了基于GPU的方法在时间上的优势.

摘要：The difference between real-time CSCW systems and traditional distributed systems is that the for- mer one is supposed to provide a natural, free and fast interface for multi-user interaction. However, the typical multi-user interaction method applied in 3D CAD systems is strict consistency maintenance, such as locking mecha- nism and floor control, which may generate a stagnant and unnatural interface. This paper proposes a 3D semantic- based Operational transformation (OT) to support less constrained multi-user interaction and to achieve consis- tency in collaborative CAD editing (co-CAD) systems, and it can be used in many collaborative CAD/CAM indus- tries.

摘要：针对目前协同设计过程中信息安全风险,从提高CAD模型的安全性出发,提出一种基于CAD特征模型局部变形的敏感信息保护方法,通过对草绘参数进行变换,使模型几何形状发生变化,达到隐藏原模型信息的目的;然后提出了基于加密矩阵的草绘参数加密技术,采用一个加密矩阵作为秘钥对草绘中的控制点参数进行加密转换;进一步,为更好地控制加密后模型的变形趋势,引入几何计算中的自由变形方法,使用加密控制晶格对草绘进行加密,提出基于自由变形的草绘参数加密方法.最后通过对CAD零件模型进行实验,证明了文中方法的有效性..

摘要：为了解决CAD系统在线集成中的建模历史不一致问题,提出了一种基于建模历史一致性的协同CAD并发控制方法.首先,对协同CAD建模操作间的关系进行了详细分析.然后,构造了一种考虑建模历史的并发控制模型,该模型通过状态向量实现建模操作间的因果关系维护,通过重构操作历史来解决建模操作间的偏并发关系,并将存在并发关系的建模操作分为互换、屏蔽和互斥3类分别进行处理.最后,设计了一种支持建模历史一致性的协同CAD并发控制方法,本地站点产生的操作立即执行,远程站点调用并发控制模型中的处理机制以完成远程操作的并发控制.实验结果表明,在协同CAD环境中,所提方法为用户提供了自由并发交互协同工作的同时,能够维护各协同站点建模历史的一致性.

摘要：为了解决异构CAD协同设计中的一维拓扑元素匹配问题,在对其进行分类讨论的基础上,提出了一种新方法.首先在源CAD系统中,获取建模操作所引用的一维拓扑元素,并利用合并算法将这些拓扑元素进行合并,合并条件为:同类型、相邻接且合并所得的拓扑元素类型与原类型相同或相近.然后在目标CAD系统中,通过匹配算法为合并后的每个一维拓扑元素寻找相匹配的目标拓扑元素,匹配条件为:类型相匹配、属性相同且目标拓扑元素的2个端点与源拓扑元素的2个端点重合或在其内部.实验结果表明,该方法有效地解决了异构CAD协同设计中的多种一维拓扑元素匹配问题,从而弥补了现有方法的不足.

摘要：针对云设计与制造环境下的协同设计CAD模型安全共享问题,提出了一个基于服务的安全特征数据交换方法.使用基于模型变形的方法,改变特征模型建模参数,实现对模型敏感信息的加密隐藏;使用直接特征数据交换方法实现异构CAD系统间的参数化建模数据交换;最后,采用面向服务的架构,将特征CAD模型数据交换与安全保护方法以云端服务的形式提供给协同设计成员,并引入访问及授权控制机制确保服务为合法授权用户使用.实验结果证明了该思路的合理性及方法的有效性,可为云设计与制造提供灵活、高效、可扩展的安全数据交换解决方案.

摘要：In recent years, graphical processing unit （GPU）-accelerated intelligent algorithms have been widely utilized for solving combination optimization problems, which are NP-hard, These intelligent algorithms involves a common operation, namely reduction, in which the best suitable candidate solution in the neighborhood is selected. As one of the main procedures, it is necessary to optimize the reduction on the GPU. In this paper, we propose an enhanced warp-based reduction on the GPU. Compared with existing block-based reduction methods, our method exploit efficiently the potential of implementation at warp level, which better matches the characteristics of current GPU architecture. Firstly, in order to improve the global memory access performance, the vectoring accessing is utilized. Secondly, at the level of thread block reduction, an enhanced warp-based reduction on the shared memory are presented to form partial results. Thirdly, for the configuration of the number of thread blocks, the number of thread blocks can be obtained by maximizing the size of thread block and the maximum size of threads per stream multi-processor on GPU. Finally, the proposed method is evaluated on three generations of NVIDIA GPUs with the better performances than previous methods.

摘要：The heat transfer mathematic models are widely used in iron and steel industry area. Many computational models that represent this physical process is based on finite difference methods. The simulation of these phenomena demands a high computa- tional cost. In this paper we employ GPU for the development of algorithm for a two-dimensional heat transfer problem with f＇mite difference methods. The performance evaluation has been made and the comparison between CPU and GPU were discussed. The experimental result shows that GPU can solve this problem more efficiently when we need to divide calculation material into a large number of meshes.