The Mathworks, Inc.

Halldor Narfi Stefansson

Loren P Dean

Edric Mark Ellis

Jocelyn Martin

Roy Lurie

John Norris Little

Brett W Baker

Piotr R Luszczek

Joseph F Hicklin

Penelope Anderson

Nausheen B Moulana

Michael Karr

Stephen C Johnson

Portia O'Callaghan

Elwin Yam Ming Chan

Jason A Kinchen

Brian J Fanous

David A Foti

Bryan T White

Jianzhong Xue

Jocelyn Luke Martin

Penelope L Anderson

Roy E Lurie

Michael E Karr

Brian Fanous

Systems and methods for function argument checking are disclosed. The systems and methods can use declarations and validation instructions based on the declarations. Validation instructions for a function can be generated automatically from a declaration for the function. The validation instructions can be executed in response to invocation of the function. The validation instructions can include instructions for determining whether an input satisfies a condition on a corresponding argument of the function, instructions for identifying a position of the input, and instructions for providing, in response to determining that the input does not satisfy the condition, an indication of the nonsatisfaction of the condition and the position. The condition can specify a datatype or size for the argument or one or more validation functions for checking the argument.

Systems and methods for function argument checking

Exemplary embodiments allow a user to create configurations for use in distributed computing environments. Configurations can be arranged in hierarchies in which elements of the hierarchy can inherit characteristics from elements in other layers of the hierarchy. Embodiments also allow a user to flatten a hierarchical configuration to remove hierarchical dependencies and/or inheriting capabilities of elements in the hierarchy. Exemplary embodiments further allow users to deploy a distributed computing configuration on their desktop to evaluate performance of the configuration and then deploy the configuration in a distributed computing environment without having to change programming code run on the desktop/distributed computing environment.

Dynamic definition for concurrent computing environments

A method comprising receiving data, where the data includes one or more elements, the data is associated with a first set of indices, and the first set of indices references the one or more elements. The method may further include folding a plurality of indices into a single index, where the single index references the one or more elements of the received data that were referenced by the plurality of indices, the folding generates a second set of indices, and the folding is performed when concurrent process threads are not generated based on the first set of indices. The method may further include determining whether concurrent process threads should be generated based on the second set of indices and generating the concurrent process threads for the second set of indices when the determining determines that the concurrent process threads should be generated based on the second set of indices.

Folding pair of adjacent indices based on optimum quantity of induces for parallel processing

A MAP decoder may be implemented in parallel. In one implementation, a device may receive an input array that represents received encoded data and calculate, in parallel, a series of transition matrices from the input array. The device may further calculate, in parallel, products of the cumulative products of the series of transition matrices and an initialization vector. The device may further calculate, in parallel and based on the products of the cumulative products of the series of transition matrices and the initialization vector, an output array that corresponds to a decoded version of the received encoded data in the input array.

Parallel implementation of maximum a posteriori probability decoder

A device receives a command to initiate parallel processing. The command includes an operator associated with an operation that is to be performed in connection with the parallel processing, and a reference to a multidimensional array to which the operator is to be applied. The operator is represented by a symbol, and the multidimensional array includes at least three dimensions. The command also includes an indication of one or more dimensions by which the multidimensional array is to be partitioned. The device partitions the multidimensional array, along the one or more dimensions, to divide the multidimensional array into multiple blocks, each of the multiple blocks representing a subset of the multidimensional array. The device controls application of the operator to the multiple blocks to cause the operator to be applied in parallel to at least two blocks of the multiple blocks.

Parallel processing of multidimensional arrays

A client device may receive information that identifies tasks to be performed by worker devices. The tasks may include at least one task that is to be performed by all worker devices, and may include at least one task that is to be performed by one worker device. The client device may identify, based on the information that identifies the tasks, an order and dependencies associated with performing the tasks. The client device may determine a task, of the tasks, to be performed by a work device, of the worker devices, based on the order and the dependencies. The client device may send, to the worker device, information associated with performing the task, and may receive, from the worker device, an indication that the task has been performed. The client device may determine, based on the order and the dependencies, another task to be performed by the worker device.

State management for task queues

A device receives a command to initiate parallel processing. The command includes an indication of a function that is to be performed in connection with the parallel processing, and a reference to a multidimensional array to which the function is to be applied. The multidimensional array includes at least three dimensions. The command also includes an indication of one or more dimensions by which the multidimensional array is to be partitioned. The device partitions the multidimensional array, along the one or more dimensions, to divide the multidimensional array into multiple blocks, each of the multiple blocks representing a subset of the multidimensional array. The device controls application of the function to the multiple blocks to cause the function to be applied in parallel to at least two blocks of the multiple blocks.

A device receives, via a technical computing environment, a program that includes a parallel construct and a command to be executed by graphical processing units, and analyzes the program. The device also creates, based on the parallel construct and the analysis, one or more instances of the command to be executed in parallel by the graphical processing units, and transforms, via the technical computing environment, the one or more command instances into one or more command instances that are executable by the graphical processing units. The device further allocates the one or more transformed command instances to the graphical processing units for parallel execution, and receives, from the graphical processing units, one or more results associated with parallel execution of the one or more transformed command instances by the graphical processing units.

Saving and loading graphical processing unit (GPU) arrays providing high computational capabilities in a computing environment

A device initiates a technical computing environment (TCE), and receives, via the TCE, a program command that permits the TCE to access a graphical processing unit that is remote to the device, where the program command permits the TCE to seamlessly transfer data to the remote GPU. The device transforms, via the TCE, the program command into a program command that is executable by the remote GPU, and provides the transformed program command to the remote GPU for execution. The device also receives, from the remote GPU, one or more results associated with execution of the transformed program command by the remote GPU, and utilizes the one or more results via the TCE.

Graphical processing unit (GPU) arrays providing high computational capabilities in a computing environment

Efficient index folding using indexing expression generated using selected pair of indices for parallel operations based on the number of indices exceeding a pre-determined threshold

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, analyzing and transforming the program, determining an inner context and an outer context of the program based on the analysis of the program, and allocating one or more portions of the inner context of the program to two or more labs for parallel execution. The method also includes receiving one or more results associated with the parallel execution of the one or more portions from the two or more labs, and providing the one or more results to the outer context of the program.

Growing community of inventors

Natick, MA, United States of America

Halldor Narfi Stefansson