The Mathworks, Inc.

Penelope Anderson

Jocelyn Martin

Loren P Dean

Halldor Narfi Stefansson

Roy Lurie

John Norris Little

Cleve B Moler

Patrick D Quillen

Sheung Hun Cheng

Edric Mark Ellis

Brett W Baker

Piotr R Luszczek

Joseph F Hicklin

Thomas Lane

Nausheen B Moulana

Michael Karr

Stephen C Johnson

Wei Wang

Michael Procopio

Richard Amos

Jason A Kinchen

James Stewart

Aaditya Kalsi

Anandan Rangasamy

Yashwanth Annapureddy

Nicholas Haddad

Aaron J Powers

Jos Martin

Loren Shure

Lucio Andrade-Cetto

Danil Kirsanov

Kari Sortland

Benjamin Tordoff

According to some embodiments, a method for making input data available for processing by one or more processors comprises storing one or more parameters, wherein the one or more parameters comprise information identifying a location of the input data; and creating a datastore object using the one or more parameters, wherein the datastore object interfaces the input data and includes a read method for reading a chunk, the chunk being a subset of the input data, and having a size that does not exceed a memory size assigned to the one or more processors. According to some embodiments, the one or more parameters further comprise one or more of a type of the input data; a format of the input data; an offset for reading from the input data; a size of the chunk; a condition for determining the chunk; and a query for deriving the input data.

Datastore mechanism for managing out-of-memory data

A device may include one or more processors. The device may receive program code that identifies operations to be performed with regard to a data set to determine an output value. The device may identify a plurality of read operations corresponding to the operations. The plurality of read operations may be performed to obtain respective inputs of the operations. The device may generate a first data structure that identifies the operations and the plurality of read operations. The device may identify two or more operations, of the operations, that receive a particular input. The device may generate a second data structure that includes a combined operation. The combined operation may be generated based on the two or more operations, and may be associated with fewer read operations than the two or more operations. The device may determine the output value based on the second data structure.

Big data read-write reduction

According to some embodiments, a method for processing input data comprises creating a MapReducer object corresponding to a MapReduce environment; and receiving, by a MapReduce interface, a plurality of input parameters comprising the input data; a mapper function; a reducer function; and the MapReducer object; and using the MapReduce interface to process the input data by one or more processors in the MapReduce environment using the mapper function and the reducer function. According to some embodiments, the method further comprises creating a second MapReducer object, wherein the second MapReducer object corresponds to a second MapReduce environment; receiving, by the MapReduce interface, the second MapReducer object in place of the first MapReducer object; and utilizing the MapReduce interface to process the input data by the one or more processors in the second MapReduce environment using the mapper function and the reducer function.

Unified mapreduce framework for large-scale data processing

The present invention provides a method and system for the dynamic distribution of an array in a parallel computing environment. The present invention obtains a criterion for distributing an array and performs flexible portioning based on the obtained criterion. In some embodiment analysis may be performed based on the criterion. The flexible portioning is then performed based on the analysis.

Dynamic distribution for distributed arrays and related rules

One or more computer-readable media store executable instructions that, when executed by processing logic, perform parallel processing. The media store one or more instructions for initiating a single programming language, and identifying, via the single programming language, one or more data distribution schemes for executing a program. The media also store one or more instructions for transforming, via the single programming language, the program into a parallel program with an optimum data distribution scheme selected from the one or more identified data distribution schemes, and allocating the parallel program to two or more labs for parallel execution. The media further store one or more instructions for receiving one or more results associated with the parallel execution of the parallel program from the two or more labs, and providing the one or more results to the program.

Performing parallel processing of distributed arrays

A computing device-implemented method includes receiving a program created by a technical computing environment, analyzing the program, generating multiple program portions based on the analysis of the program, dynamically allocating the multiple program portions to multiple software units of execution for parallel programming, receiving multiple results associated with the multiple program portions from the multiple software units of execution, and providing the multiple results or a single result to the program.

Parallel programming interface to dynamicaly allocate program portions

Media for performing parallel processing of distributed arrays

A computing device-implemented method includes initiating a single programming language, and identifying, via the single programming language, one or more data distribution schemes for executing a program. The method also includes transforming, via the single programming language, the program into a parallel program with an optimum data distribution scheme selected from the one or more identified data distribution schemes, and allocating the parallel program to two or more labs for parallel execution. The method further includes receiving one or more results associated with the parallel execution of the parallel program from the two or more labs, and providing the one or more results to the program.

Parallel processing of distributed arrays and optimum data distribution

A device for performing parallel processing includes a processor to initiate a single programming language, and identify, via the single programming language, one or more data distribution schemes for executing a program. The processor also transforms, via the single programming language, the program into a parallel program with an optimum data distribution scheme selected from the one or more identified data distribution schemes, and allocates the parallel program to two or more labs for parallel execution. The processor further receives one or more results associated with the parallel execution of the parallel program from the two or more labs, and provides the one or more results to the program.

Device for performing parallel processing of distributed arrays

A computing system receives a program created by a technical computing environment, analyzes the program, generates multiple program portions based on the analysis of the program, dynamically allocates the multiple program portions to multiple software units of execution for parallel programming, receives multiple results associated with the multiple program portions from the multiple software units of execution, and provides the multiple results or a single result to the program.

Parallel programming computing system to dynamically allocate program portions

Parallel programming interface to dynamically allocate program portions

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Newton, MA, United States of America

Penelope Anderson