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    SHARED MEMORY EIGENSOLVER
    1.
    发明申请
    SHARED MEMORY EIGENSOLVER 有权
    共享内存EIGENSOLVER

    公开(公告)号：US20150134714A1

    公开(公告)日：2015-05-14

    申请号：US14537839

    申请日：2014-11-10

    Applicant: Silicon Graphics International Corp.

    Inventor： Cheng Liao

    IPC: G06F17/16

    CPC classification number: G06F9/544 ,  G06F9/4887 ,  G06F9/5016 ,  G06F9/5066 ,  G06F12/08 ,  G06F17/16 ,  G06F2212/2542 ,  G06T1/60

    Abstract: Disclosed herein is a shared memory systems that use a combination of SBR and MRRR techniques to calculate eigenpairs for dense matrices having very large numbers of rows and columns. The disclosed system allows for the use of a highly scalable tridiagonal eigensolver. The disclosed system likewise allows for allocating a different number of t00hreads to each of the different computational stages of the eigensolver.

    Abstract translation: 这里公开了一种共享存储器系统，其使用SBR和MRRR技术的组合来计算具有非常大数量的行和列的密集矩阵的特征对。 所公开的系统允许使用高度可缩放的三角形固定器。 所公开的系统同样允许将不同数量的t00hreads分配给固定器的不同计算阶段。

    SHARED MEMORY EIGENSOLVER
    2.
    发明申请
    SHARED MEMORY EIGENSOLVER 审中-公开
    共享内存EIGENSOLVER

    公开(公告)号：US20160299874A1

    公开(公告)日：2016-10-13

    申请号：US15132085

    申请日：2016-04-18

    Applicant: Silicon Graphics International Corp.

    Inventor： Cheng Liao

    IPC: G06F17/16

    CPC classification number: G06F17/16 ,  G06F12/0207

    Abstract: Disclosed herein is a shared memory system that use a combination of SBR and MRRR techniques to calculate eigenpairs for dense matrices having very large numbers of rows and columns. The disclosed system allows for the use of a highly scalable tridiagonal eigensolver. The disclosed system likewise allows for allocating a different number of threads to each of the different computational stages of the eigensolver.

    Abstract translation: 这里公开了一种共享存储器系统，其使用SBR和MRRR技术的组合来计算具有非常大数量的行和列的密集矩阵的特征对。 所公开的系统允许使用高度可缩放的三角形固定器。 所公开的系统同样允许将不同数量的线程分配给固定器的不同计算阶段。

    Scalable Matrix Multiplication in a Shared Memory System
    5.
    发明申请
    Scalable Matrix Multiplication in a Shared Memory System 审中-公开
    在共享内存系统中的可扩展矩阵乘法

    公开(公告)号：US20140331014A1

    公开(公告)日：2014-11-06

    申请号：US14041974

    申请日：2013-09-30

    Applicant: Silicon Graphics International Corp.

    Inventor： Cheng Liao

    IPC: G06F12/00

    CPC classification number: G06F15/17306 ,  G06F17/16

    Abstract: High performance computing systems perform complex or data-intensive calculations using a large number of computing nodes and a shared memory. Disclosed methods and systems provide nodes having a special-purpose coprocessor to perform these calculations, along with a general-purpose processor to direct the calculations. Computational data transfer from the shared memory to the coprocessor incurs a data copying latency. To reduce this latency as experienced by the coprocessor, a complex computation is divided into work units, and one or more threads executing on the processor copy the work units from the shared memory to a local buffer memory of a computing node. By buffering these data for transfer from the local memory to coprocessor memory, and by ensuring that new data are copied while the coprocessor operates on older data, data copying latency is hidden from the coprocessor.

    Abstract translation: 高性能计算系统使用大量计算节点和共享内存执行复杂或数据密集型计算。 公开的方法和系统提供具有专用协处理器的节点来执行这些计算，以及用于指导计算的通用处理器。 从共享存储器到协处理器的计算数据传输引起数据复制延迟。 为了减少由协处理器所经历的延迟，复杂的计算被划分为工作单元，并且在处理器上执行的一个或多个线程将工作单元从共享存储器复制到计算节点的本地缓冲存储器。 通过缓冲这些数据从本地存储器转移到协处理器存储器，并且通过确保在协处理器对旧数据进行操作时复制新数据，数据复制等待时间将从协处理器中隐藏起来。

    Shared memory eigensolver
    6.
    发明授权
    Shared memory eigensolver 有权
    共享记忆体分子

    公开(公告)号：US09547882B2

    公开(公告)日：2017-01-17

    申请号：US14537839

    申请日：2014-11-10

    Applicant: Silicon Graphics International Corp.

    Inventor： Cheng Liao

    IPC: G06T1/60 ,  G06F17/16 ,  G06F12/08 ,  G06F9/54 ,  G06F9/50

    CPC classification number: G06F9/544 ,  G06F9/4887 ,  G06F9/5016 ,  G06F9/5066 ,  G06F12/08 ,  G06F17/16 ,  G06F2212/2542 ,  G06T1/60

    Abstract: Disclosed herein is a shared memory systems that use a combination of SBR and MRRR techniques to calculate eigenpairs for dense matrices having very large numbers of rows and columns. The disclosed system allows for the use of a highly scalable tridiagonal eigensolver. The disclosed system likewise allows for allocating a different number of threads to each of the different computational stages of the eigensolver.

    Abstract translation: 这里公开了一种共享存储器系统，其使用SBR和MRRR技术的组合来计算具有非常大数量的行和列的密集矩阵的特征对。 所公开的系统允许使用高度可缩放的三角形固定器。 所公开的系统同样允许将不同数量的线程分配给固定器的不同计算阶段。

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