Patent · US Active

Hybrid parallelization strategies for machine learning programs on top of MapReduce

US9286044B2 · kind B2 · utility

8Cited by

0References

20Claims

0Family size

Assignee

International Business Machines Corporation · US

Inventors

Matthias Boehm · Neuendettelsau, DE
Douglas R. Burdick · San Jose, US
Berthold Reinwald · San Jose, US
Prithviraj Sen · San Jose, US
Shirish Tatikonda · Santa Clara, US
Yuanyuan Tian · San Jose, US
Shivakumar Vaithyanathan · San Jose, US

Key dates

Filing date	Jun 27, 2014
Grant date	Mar 15, 2016
Priority date	—
Expiry date	Jun 27, 2034

Classification

Technology area (CPC G)Physics
CPC primaryG06F9/4881
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Hybrid parallelization strategies for machine learning programs on top of MapReduce are provided. In one embodiment, a method of and computer program product for parallel execution of machine learning programs are provided. Program code is received. The program code contains at least one parallel for statement having a plurality of iterations. A parallel execution plan is determined for the program code. According to the parallel execution plan, the plurality of iterations is partitioned into a plurality of tasks. Each task comprises at least one iteration. The iterations of each task are independent.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.