Teresa L Johnson

US Patent:

20040025153, Feb 5, 2004

Filed:

Jul 30, 2002

Appl. No.:

10/210016

Inventors:

Teresa Johnson - Los Gatos CA, US
Richard Hank - San Jose CA, US

International Classification:

G06F009/45

US Classification:

717/161000

Abstract:

The present invention is a software pipeline method and system. In one embodiment a software pipeline method commences pipeline operations. If a flow control condition is valid, a branch operation is performed. After the pausing the software pipeline method returns to the pipeline operations at the same point in the pipeline operations at which the pause initiated.

US Patent:

20090077350, Mar 19, 2009

Filed:

May 29, 2007

Appl. No.:

11/754391

Inventors:

Sujoy Saraswati - Bangalore, IN
Teresa Johnson - Cupertino CA, US

International Classification:

G06F 9/312

US Classification:

712207, 712E09033

Abstract:

A method, system and computer program for modifying an executing application, comprising monitoring the executing application to identify at least one of a hot load instruction, a hot store instruction and an active prefetch instruction that contributes to cache congestion; where the monitoring identifies a hot load instruction, enabling at least one prefetch associated with the hot load instruction; where the monitoring identifies a hot store instruction, enabling at least one prefetch associated with the hot store instruction; and where the monitoring identifies an active prefetch instruction that contributes to cache congestion, one of disabling the active prefetch instruction and reducing the effectiveness of the active prefetch instructions.

US Patent:

20160224324, Aug 4, 2016

Filed:

Feb 27, 2015

Appl. No.:

14/634401

Inventors:

- Mountain View CA, US
Teresa Louise JOHNSON - Mountain View CA, US
Rong XU - Mountain View CA, US

Assignee:

GOOGLE INC. - Mountain View CA

International Classification:

G06F 9/45

Abstract:

Provided are methods and systems for inter-procedural optimization (IPO). A new IPO architecture (referred to as “ThinLTO”) is designed to address the weaknesses and limitations of existing IPO approaches, such as traditional Link Time Optimization (LTO) and Lightweight Inter-Procedural Optimization (LIPO), and become a new link-time-optimization standard. With ThinLTO, demand-driven and summary-based fine grain importing maximizes the potential of Cross-Module Optimization (CMO), which enables as much useful CMO as possible ThinLTO also provides for global indexing, which enables fast function importing; parallelizes some performance-critical but expensive inter-procedural analyses and transformations; utilizes demand-driven, lazy importing of debug information that minimizes memory consumption for the debug build; and allows easy integration of third-party distributed build systems. In addition, ThinLTO may also be implemented using an IPO server, thereby removing the need for the serial step.