Deriving a Nested Chain of Densest Subgraphs from a Graph
    1.
    发明申请
    Deriving a Nested Chain of Densest Subgraphs from a Graph 有权
    从图中衍生出一个嵌套的致密子图链

    公开(公告)号:US20130226840A1

    公开(公告)日:2013-08-29

    申请号:US13406843

    申请日:2012-02-28

    申请人: Bin Zhang Meichun Hsu

    发明人: Bin Zhang Meichun Hsu

    IPC分类号: G06F15/18

    CPC分类号: G06F17/30958

    摘要: A nested chain of densest subgraphs is derived by a computer from a given graph that has multiple vertices and edges. The two ends of each edge are assigned with respective incident weights, and each vertex is given a vertex weight. A weight balancing process is carried out by the computer to iteratively go through the edges to adjust the incident weights of each edge and the vertex weights of the vertices connected by that edge to reduce a difference between the vertex weights of the two vertices. After the balancing, the vertex weights are put in an ordered sequence according to their values, and a nested chain of densest subgraphs is derived from the ordered sequence.

    摘要翻译: 密集子图的嵌套链由计算机从具有多个顶点和边的给定图导出。 每个边缘的两端被分配有相应的事件权重,并且每个顶点被赋予顶点权重。 计算机进行权重平衡处理,以迭代地遍历边缘,以调整每个边缘的入射权重和由该边连接的顶点的顶点权重,以减少两个顶点的顶点权重之间的差异。 在平衡之后,顶点权重根据它们的值被置于有序序列中,并且从有序序列导出嵌套的密集子图链。

    METHOD, SYSTEM, AND PRODUCT FOR PERFORMING UNIFORMLY FINE-GRAIN DATA PARALLEL COMPUTING
    2.
    发明申请
    METHOD, SYSTEM, AND PRODUCT FOR PERFORMING UNIFORMLY FINE-GRAIN DATA PARALLEL COMPUTING 有权
    用于执行均匀微粒数据并行计算的方法,系统和产品

    公开(公告)号:US20120096244A1

    公开(公告)日:2012-04-19

    申请号:US12904829

    申请日:2010-10-14

    IPC分类号: G06F9/302

    摘要: A method is disclosed that includes computing, using at least one uniformly fine-grain data parallel computing unit, a mean-square error regression within a regression clustering algorithm. The mean-square error regression is represented in the form of at least one summation of a vector-vector multiplication. A computer program product and a computer system are also disclosed.

    摘要翻译: 公开了一种包括使用至少一个均匀细粒度数据并行计算单元在回归聚类算法内计算均方误差回归的方法。 均方误差回归以矢量向量乘法的至少一个求和的形式表示。 还公开了一种计算机程序产品和计算机系统。

    Network balancing procedure that includes redistributing flows on arcs incident on a batch of vertices
    4.
    发明授权
    Network balancing procedure that includes redistributing flows on arcs incident on a batch of vertices 有权
    网络平衡过程,包括重新分布在一组顶点上的弧上的流

    公开(公告)号:US09003419B2

    公开(公告)日:2015-04-07

    申请号:US12512246

    申请日:2009-07-30

    IPC分类号: G06F9/46 H04L12/803

    CPC分类号: H04L47/125

    摘要: A representation of a flow network having vertices connected by arcs is provided. The vertices include a first set of vertices that provide flow to a second set of vertices over arcs connecting the first set and second set of vertices. A balancing procedure in the network is performed that includes redistributing flows on arcs incident on the second set of vertices. The balancing procedure includes selecting a batch of the vertices in the second set, and redistributing flows on arcs incident on the selected batch of vertices. The selecting and redistributing are repeated for other batches of vertices in the second set.

    摘要翻译: 提供了具有由弧连接的顶点的流网络的表示。 顶点包括第一组顶点,其在连接第一组和第二组顶点的弧上提供流向第二组顶点的流。 执行网络中的平衡过程,其包括重新分布入射在第二组顶点的弧上的流。 平衡过程包括选择第二组中的一批顶点,并重新分布入侵在所选批次顶点上的圆弧上的流。 对第二组中的其他批次的顶点重复选择和重新分配。

    Hierarchical cluster determination based on subgraph density
    5.
    发明授权
    Hierarchical cluster determination based on subgraph density 有权
    基于子图密度的分层聚类确定

    公开(公告)号:US08971665B2

    公开(公告)日:2015-03-03

    申请号:US13562598

    申请日:2012-07-31

    申请人: Bin Zhang Meichun Hsu

    发明人: Bin Zhang Meichun Hsu

    IPC分类号: G06K9/36 G06F7/00

    CPC分类号: G06K9/6219

    摘要: Densest subgraphs of a graph are determined. The graph includes vertices and edges interconnecting the vertices. Each edge connects two of the vertices and has a weight. The vertices and the edges form subgraphs from which the densest subgraphs are determined as those subgraphs having densities greater than a threshold. Clusters at levels of a hierarchy are determined based on the densest subgraphs. Each cluster includes a set of the vertices and a set of the edges of the graph. Each level corresponds to a different density of the clusters. The hierarchy is ordered from a most-dense level of the clusters to a least-dense level of the clusters.

    摘要翻译: 确定曲线图的发光子图。 该图包括互连顶点的顶点和边。 每个边缘连接两个顶点并具有重量。 顶点和边缘形成子图,最密集子图确定为密度大于阈值的子图。 根据最密集的子图确定层次结构级别的群集。 每个群集包括一组顶点和一组图的边。 每个级别对应于簇的不同密度。 层次结构从集群的最密集级别排列到集群的最低密度级别。

    Coordination of tasks executed by a plurality of threads using two synchronization primitive calls
    6.
    发明授权
    Coordination of tasks executed by a plurality of threads using two synchronization primitive calls 有权
    使用两个同步原语调用协调由多个线程执行的任务

    公开(公告)号:US08904406B2

    公开(公告)日:2014-12-02

    申请号:US12512256

    申请日:2009-07-30

    申请人: Bin Zhang Meichun Hsu

    发明人: Bin Zhang Meichun Hsu

    IPC分类号: G06F9/46 G06F9/52

    CPC分类号: G06F9/52 G06F9/522

    摘要: To coordinate tasks executed by a plurality of threads that each includes plural task sections, a call of a mark primitive to mark a first point after a first of the plural task sections is provided. Also, a call of a second primitive is provided to indicate that a second of the plural task sections is not allowed to begin until after the plurality of threads have each reached the first point.

    摘要翻译: 为了协调由包括多个任务部分的多个线程执行的任务,提供了在多个任务部分中的第一个之后标记第一点的标记原语的调用。 此外,提供第二原语的调用以指示在多个线程各自到达第一点之后,多个任务部分中的第二个不允许开始。

    Multi-input, multi-output-per-input user-defined-function-based database operations
    7.
    发明授权
    Multi-input, multi-output-per-input user-defined-function-based database operations 有权
    多输入,多输出输入的基于用户定义功能的数据库操作

    公开(公告)号:US08805870B2

    公开(公告)日:2014-08-12

    申请号:US13192373

    申请日:2011-07-27

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30415

    摘要: The current application discloses a database management system that provides multiple-input, multiple-output-per-input user-defined-function-based operations. The database management system comprises at least one processor and electronic memory, a database-query processor, executed on a computer processor controlled by computer instructions stored in a computer-readable memory, that makes multiple calls to a multiple-input, multiple-output-per-input user-defined-function, in each call transmitting a next input to the multiple-input, multiple-output-per-input user-defined-function, and the multiple-input, multiple-output-per-input user-defined-function, executed on a computer processor controlled by computer instructions stored in a computer-readable memory, that uses three different memory buffers, the contents of which are maintained for three different time periods, to compute and return to the database-query processor multiple outputs in response to at least one of the multiple inputs.

    摘要翻译: 当前的应用公开了一种数据库管理系统,其提供多输入多输出用户定义的基于功能的操作。 数据库管理系统包括至少一个处理器和电子存储器,数据库查询处理器,其在由存储在计算机可读存储器中的计算机指令控制的计算机处理器上执行,其对多输入多输出 - 每个输入用户定义的功能,在每次呼叫中传送下一个输入到多输入多输出用户定义函数,以及多输入多输出用户定义函数, 定义的功能,在由存储在计算机可读存储器中的计算机指令控制的计算机处理器上执行,其使用三个不同的存储器缓冲器,其内容维持三个不同的时间段,以计算并返回到数据库查询处理器 多个输出响应多个输入中的至少一个。

    Hierarchical cluster determination based on subgraph density
    8.
    发明申请
    Hierarchical cluster determination based on subgraph density 有权
    基于子图密度的分层聚类确定

    公开(公告)号:US20140037227A1

    公开(公告)日:2014-02-06

    申请号:US13562598

    申请日:2012-07-31

    申请人: Bin Zhang Meichun Hsu

    发明人: Bin Zhang Meichun Hsu

    IPC分类号: G06K9/36

    CPC分类号: G06K9/6219

    摘要: Densest subgraphs of a graph are determined. The graph includes vertices and edges interconnecting the vertices. Each edge connects two of the vertices and has a weight. The vertices and the edges form subgraphs from which the densest subgraphs are determined as those subgraphs having densities greater than a threshold. Clusters at levels of a hierarchy are determined based on the densest subgraphs. Each cluster includes a set of the vertices and a set of the edges of the graph. Each level corresponds to a different density of the clusters. The hierarchy is ordered from a most-dense level of the clusters to a least-dense level of the clusters.

    摘要翻译: 确定曲线图的发光子图。 该图包括互连顶点的顶点和边。 每个边缘连接两个顶点并具有重量。 顶点和边缘形成子图,最密集子图确定为密度大于阈值的子图。 根据最密集的子图确定层次结构级别的群集。 每个群集包括一组顶点和一组图的边。 每个级别对应于簇的不同密度。 层次结构从集群的最密集级别排列到集群的最低密度级别。

    COMBINING DATA VALUES THROUGH ASSOCIATIVE OPERATIONS
    9.
    发明申请
    COMBINING DATA VALUES THROUGH ASSOCIATIVE OPERATIONS 有权
    通过相关操作组合数据值

    公开(公告)号:US20130061023A1

    公开(公告)日:2013-03-07

    申请号:US13224090

    申请日:2011-09-01

    IPC分类号: G06F9/318

    摘要: A method for combining data values through associative operations. The method includes, with a processor, arranging any number of data values into a plurality of columns according to natural parallelism of the associative operations and reading each column to a register of an individual processor. The processors are directed to combine the data values in the columns in parallel using a first associative operation. The results of the first associative operation for each column are stored in a register of each processor.

    摘要翻译: 一种通过关联操作组合数据值的方法。 该方法包括与处理器根据关联操作的自然并行性将多个数据值排列到多个列中,并将每列读取到单个处理器的寄存器。 处理器旨在使用第一关联操作来并行地并列列中的数据值。 每列的第一个关联操作的结果存储在每个处理器的寄存器中。

    METHOD AND SYSTEM FOR BLOCKING DATA ON A GPU
    10.
    发明申请
    METHOD AND SYSTEM FOR BLOCKING DATA ON A GPU 有权
    用于在GPU上阻塞数据的方法和系统

    公开(公告)号:US20110057937A1

    公开(公告)日:2011-03-10

    申请号:US12556401

    申请日:2009-09-09

    IPC分类号: G06F15/80 G06T1/00

    CPC分类号: G06F15/8023 G06T1/00

    摘要: A method is provided for optimizing computer processes executing on a graphics processing unit (GPU) and a central processing unit (CPU). Process data is subdivided into sequentially processed data and parallel processed data. The parallel processed data is subdivided into a plurality of data blocks assigned to a plurality of processing cores of the GPU. The data blocks on the GPU are processed with other data blocks in parallel on the plurality of processing cores. Sequentially processed data is processed on the CPU. Result data processed on the CPU is returned.

    摘要翻译: 提供了一种用于优化在图形处理单元(GPU)和中央处理单元(CPU)上执行的计算机处理的方法。 过程数据被细分为顺序处理的数据和并行处理的数据。 并行处理的数据被细分为分配给GPU的多个处理核心的多个数据块。 GPU上的数据块在多个处理核上并行处理与其他数据块。 在CPU上处理顺序处理的数据。 返回CPU处理的结果数据。