公开(公告)号：US10854331B2

公开(公告)日：2020-12-01

申请号：US15522246

申请日：2014-10-26

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Henggang Cui ,  Kimberly Keeton ,  Indrajit Roy ,  Krishnamurthy Viswanathan ,  Haris Volos

IPC: G06F16/2453 ,  G16H40/63 ,  G06F16/25 ,  G06F17/14 ,  G06F17/18

Abstract: A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

公开(公告)号：US10089761B2

公开(公告)日：2018-10-02

申请号：US15143273

申请日：2016-04-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Krishnamurthy Viswanathan ,  Maria Gonzalez ,  Fei Chen ,  Hernan Laffitte

IPC: G06T11/20 ,  G06T1/60 ,  G06T1/20 ,  G06F17/30

Abstract: Examples herein involve graph processing using a shared memory. An example method includes distributing vertices of a graph to a plurality of graph partition processors of a system, the graph partition processors to process non-overlapping sets of vertices of the graph; storing a master copy of a vertex state of the graph in a shared memory of the system; instructing the graph partition processors to iteratively process respective vertices of the graph partitions based on local copies of the state of the graph stored in respective local memories of the graph partition processors; and updating the master copy of the state of the graph in the shared memory based on the iterative processing of the partitions of the vertices by the graph partition processors until convergence of the graph.

公开(公告)号：US20170316591A1

公开(公告)日：2017-11-02

申请号：US15143273

申请日：2016-04-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Krishnamurthy Viswanathan ,  Maria Gonzalez ,  Fei Chen ,  Fei Chen ,  Hernan Laffitte

IPC: G06T11/20 ,  G06T1/60 ,  G06T1/20

CPC classification number: G06T11/206 ,  G06F17/30958 ,  G06T1/20 ,  G06T1/60

Abstract: Examples herein involve graph processing using a shared memory. An example method includes distributing vertices of a graph to a plurality of graph partition processors of a system, the graph partition processors to process non-overlapping sets of vertices of the graph; storing a master copy of a vertex state of the graph in a shared memory of the system; instructing the graph partition processors to iteratively process respective vertices of the graph partitions based on local copies of the state of the graph stored in respective local memories of the graph partition processors; and updating the master copy of the state of the graph in the shared memory based on the iterative processing of the partitions of the vertices by the graph partition processors until convergence of the graph.

公开(公告)号：US20180114132A1

公开(公告)日：2018-04-26

申请号：US15568307

申请日：2015-05-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Fei Chen ,  Maria Teresa Gonzalez Diaz ,  Hideaki Kimura ,  Krishnamurthy Viswanathan

IPC: G06N5/04 ,  G06F17/30

CPC classification number: G06N5/048 ,  G06F12/0875 ,  G06F16/2379 ,  G06F16/278 ,  G06F16/9024 ,  G06F2212/302 ,  G06T1/60 ,  H04N19/42

Abstract: A technique includes performing graph inference in a graph inference engine that includes multiple processing nodes to determine assignments for vertices of a graph. Performing the graph inference includes controlling remote memory accesses within the engine, including storing first data in a local memory of the first processing node, where the first data represents at least assignments for a plurality of vertices of the graph; in the first processing node, determining updates for the assignments for a subset of the plurality of vertices of a partition of the graph assigned to the first processing node and modifying the first data based on the updates; and communicating the updates to at least one other processing node of the multiple processing nodes, where at least one other partition of the graph is assigned to the other processing node(s).

公开(公告)号：US20170322987A1

公开(公告)日：2017-11-09

申请号：US15522246

申请日：2014-10-26

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Henggang Cui ,  Kimberly Keeton ,  Indrajit Roy ,  Krishnamurthy Viswanathan ,  Haris Volos

IPC: G06F17/30 ,  G06F17/14 ,  G06F17/18

CPC classification number: G16H40/63 ,  G06F16/2453 ,  G06F16/254 ,  G06F17/148 ,  G06F17/18

Abstract: A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

公开(公告)号：US10423616B2

公开(公告)日：2019-09-24

申请号：US15307043

申请日：2014-04-30

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Jun Li ,  Krishnamurthy Viswanathan ,  Mijung Kim

IPC: G06F16/22 ,  G06F16/903 ,  G06F16/2453

Abstract: An example technique includes assigning partitions of a dataset of multidimensional points to a plurality of local memory nodes of a multicore machine and using the local memory nodes for a search query to determine similarity matches in the dataset for a given multidimensional point. The using includes parallel searching with the local memory nodes in the assigned partitions to identify candidate similarity matches to the given multidimensional point using indexes derived from the multidimensional points, the parallel searching for each node progressing through a sequence of search distances and providing an ongoing search result for each search distance from the given multidimensional point and regulating an extent of the parallel searching based on the ongoing search results.

公开(公告)号：US20170046382A1

公开(公告)日：2017-02-16

申请号：US15307043

申请日：2014-04-30

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Jun Li ,  Krishnamurthy Viswanathan ,  Mijung Kim

IPC: G06F17/30

Abstract: An example technique includes assigning partitions of a dataset of multidimensional points to a plurality of local memory nodes of a multicore machine and using the local memory nodes for a search query to determine similarity matches in the dataset for a given multidimensional point. The using includes parallel searching with the local memory nodes in the assigned partitions to identify candidate similarity matches to the given multidimensional point using indexes derived from the multidimensional points, the parallel searching for each node progressing through a sequence of search distances and providing an ongoing search result for each search distance from the given multidimensional point and regulating an extent of the parallel searching based on the ongoing search results.

Abstract translation: 一个示例性技术包括将多维点的数据集的分区分配给多核机器的多个本地存储器节点，并且使用本地存储器节点进行搜索查询来确定给定多维点的数据集中的相似性匹配。 所述使用包括使用分配的分区中的本地存储器节点进行并行搜索，以使用从多维点导出的索引来识别与给定多维点的候选相似性匹配，并行搜索每个节点通过搜索距离序列进行并提供正在进行的搜索 对于从给定的多维点的每个搜索距离的结果，并且基于正在进行的搜索结果来调节并行搜索的范围。

公开(公告)号：US20160253381A1

公开(公告)日：2016-09-01

申请号：US15027842

申请日：2013-10-31

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mijung Kim ,  Jun Li ,  Manish Marwah ,  Krishnamurthy Viswanathan

IPC: G06F17/30

Abstract: Storing time series data for a search query includes identifying a time series whose representation is to be pre-computed based on available memory storage, pre-computing at least one representation of the identified time series, and storing the at least one representation in the memory storage.

Abstract translation: 存储用于搜索查询的时间序列数据包括标识其表示将基于可用存储器存储预先计算的时间序列，预先计算所识别的时间序列的至少一个表示，以及将至少一个表示存储在存储器中 存储。