SYSTEM FOR LEARNING AND APPLYING INTEGRATED TASK AND DATA PARALLEL STRATEGIES IN DYNAMIC APPLICATIONS

• US 20020091748A1
• Filed: 05/28/1998
• Published: 07/11/2002
• Est. Priority Date: 05/28/1998
• Status: Active Grant

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: NIKHIL, RISHIYURS, RAMACHANDRAN, UMAKISHORE, KNOBE, KATHLEEN, REHG, JAMES MATHEW
• Granted Patent: US 6,675,189 B2
• Time in Patent Office: Days
• US Class Current: 718/107
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A system for learning and applying a task and data parallel strategy to an application that includes at least one task for processing an input data stream to produce an output data stream includes the following components. A controller measuring an execution of the application to generate an action space representing a task and data paralell strategy. A run-time system applying the action space to implement the task and data prallel strategy.

Claims

1. A system for learning and applying a task and data parallel strategy to an application including at least one task for processing an input data stream to produce an output data stream, comprising:

a controller measuring an execution of the application to generate an action space representing a task and data parallel strategy;

a run-time system applying the action space to implement the task and data parallel strategy.

2. The system of claim 1 including means for measuring application and system states during the execution of the application.

3. The system of claim 1 wherein the application includes interacting tasks and data parallel tasks.

4. The system of claim 1 wherein the action space includes an action vector.

5. The system of claim 2 wherein the controller includes means for modeling the application and system states and means for exploring the model for particular application and system states, and a control policy for generating the action space.

6. The system of claim 5 wherein the means for modeling uses a forward model expressed as (q, a)→

g where q denotes particular states, a denote the action vector, and g denote a goal vector.

7. The system of claim 1 wherein the applying includes for generating a splitter task for partitioning the input data stream into a plurality of data chunks, a plurality worker tasks processing subsets of the data chunks, each worker task being an instance of the at least one task, and a joiner task combining the processed data chunks to produce the output data stream from the at least one task using the taks and data parallel strategy.