IP Library Granted Patent US 10,810,220
Granted Patent B2
US 10,810,220 · App. 15/339,186 · Granted Oct 20, 2020

Platform and software framework for data intensive applications in the cloud

Inventor: Sanhita Sarkar (Fremont, CA)
Assignee: Hewlett Packard Enterprise Development LP
G06F16/248G06F9/445G06F9/46G06F16/25H04L43/00H04L43/045H04L43/0817H04L43/50G06F16/258
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Quick Facts
Patent No.
US 10,810,220
App. No.
15/339,186
Granted
Oct 20, 2020
Kind
B2
Abstract

A system deploys visualization tools, business analytics software, and big data software in a multi-instance mode on a large, coherent shared memory many-core computing system. The single machine solution provides or high performance and scalability and may be implemented remotely as a large capacity server (i.e., in the cloud) or locally to a user. Most big data software running in a single instance mode has limitations in scalability when running on a many-core and large coherent shared memory system. A configuration and deployment technique using a multi-instance approach, which also includes visualization tools and business analytics software, maximizes system performance and resource utilization, reduces latency and provides scalability as needed, for end-user applications in the cloud.

Claims (28)

1. A method comprising:

a plurality of processors executing instructions on a large coherent shared memory many-core computer system to provide at least one of a database engine, a virtual machine layer, a graphics database or a middleware layer; and

executing one or more instances of an application program on the computing system by the plurality of processors, wherein:

the executing comprises at least one of maximizing of system performance, resource utilization, reduced latency, or scalability for end-user applications in a cloud, wherein:

each instance of the one or more instances of the application program is executed on a group of processors of the plurality of processors, and

the processors of each group of the groups are allocated from one or more sets of adjacent processing cores,

wherein each of the one or more sets of adjacent processing cores include processing cores from a single multi-core processor or from a plurality of processing sockets that access memory associated with a node when performing analytic computations according to the one or more instances of the application program, the method further comprising:

providing visualization data based on analytics by a visualization application, where the visualization is configured on a single step machine; and

configuring the instances to handle the analytics.

2. The method of claim 1 , wherein the instances comprise instances of client applications, the method further comprising using the instances of the client applications to perform analytics on data stored in the computer system.

3. The method of claim 1 , wherein the processors of each group are configured to include the database engine.

4. The method of claim 1 , wherein the processors of each group are configured to comprise the virtual machine layer.

5. The method of claim 1 , wherein the processors of each group are configured to comprise the graphics database.

6. The method of claim 1 , wherein the processors of each group are configured to comprise the middleware layer and wherein the middleware layer is configured to comprise one or more of information management software and business intelligence application software.

7. The method of claim 1 , wherein the application program is selected from a group consisting of visualization tools, business analytics software, and big data software.

8. A method comprising:

executing one or more instances of an application program on a large coherent shared memory many-core computing system by a plurality of processors, wherein:

the executing comprises at least one of maximizing of system performance, resource utilization, reduced latency, or scalability for end-user applications in a cloud,

each instance of the one or more instances of the application program is executed on a group of processors of the plurality of processors, and

the processors of each group of the groups are allocated from one or more sets of adjacent processing cores,

wherein each of the one or more sets of adjacent processing cores include processing cores from a single multi-core processor or from a plurality of processing sockets that access memory associated with a node when performing analytic computations according to the one or more instances of the application program, the method further comprising:

providing visualization data based on analytics by a visualization application, where the visualization is configured on a single step machine; and

configuring the instances to handle the analytics.

9. The method of claim 8 , wherein the instances comprise instances of client applications, the method further comprising using the instances of the client applications to perform analytics on data stored in the computer system.

10. The method of claim 8 , wherein the processors of each group of the groups are configured to include a database engine.

11. The method of claim 8 , wherein the processors of each group of the groups are configured to comprise a virtual machine layer.

12. The method of claim 8 , wherein the processors of each group of the groups are configured to comprise a graphics database.

13. The method of claim 8 , wherein the processors of each group of the groups are configured to comprise a middleware layer and wherein the middleware layer is configured to comprise one or more of information management software and business intelligence application software.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 14, 2020
From: SILICON GRAPHICS INTERNATIONAL CORP.
To: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Reel/Frame 053767/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 2, 2017
From: SARKAR, SANHITA
To: SILICON GRAPHICS INTERNATIONAL CORP.
Reel/Frame 041158/0969 →
Continuity (4)
Continuation 14266764 · Apr 30, 2014
Provisional Application 61818288 · May 1, 2013
Provisional Application 61841262 · Jun 28, 2013
Related Publication 20170109415A1 · Apr 20, 2017