Abstract: A computer-implemented method of load balancing including calculating an expected cost set associated with an application-specific task of an application executing on a processing resource in a cloud computing environment, and communicating the expected cost set from the processing resource to a cloud management system. Resource mapping of applications currently executing in the cloud computing environment are retrieved, the application-specific task is assigned to a specific computational resource in the cloud computing environment based on the expected cost set and the resource mapping of applications currently executing in the cloud computing environment. A task to VM (virtual machine) assignment is determined based on the assignment of the application-specific task to the specific computational resource. The task to VM assignment is transferred from the cloud management system to the application executing on the processing resource in the cloud computing environment.

Abstract: A spatio-temporal image of an object is reconstructed based on captured data characterizing the object. The spatio-temporal image comprises a plurality of spatial images in respective time intervals, and at least a given one of the spatial images in one of the time intervals is reconstructed using not only captured data from a frame associated with that time interval but also captured data associated with one or more additional frames associated with other time intervals. The spatio-temporal image may be reconstructed by iteratively obtaining a solution to a minimization or maximization problem in a sparse domain and transforming the solution to an image domain. The transformation between the sparse domain and the image domain may utilize a spatio-temporal transformation implemented using a plurality of basis functions, one or more of which may be determined at least in part based on secondary information associated with the imaged object.

Abstract: Methods and systems for managing energy sources and energy consumers in an integrated system are provided. Certain subject matter presented herein relates to automatically scheduling jobs and their sub-tasks to maximize profit by comparing power source configurations and determining the best job schedule for the power source configurations. This system broadly involves two sub-problems: determining the best power source configuration and determining the best job schedule for the given power source configuration.

Abstract: Apparatus for evaluating the performance of DMA-based algorithmic tasks on a target multi-core processing system includes a memory and at least one processor coupled to the memory. The processor is operative: to input a template for a specified task, the template including DMA-related parameters specifying DMA operations and computational operations to be performed; to evaluate performance for the specified task by running a benchmark on the target multi-core processing system, the benchmark being operative to generate data access patterns using DMA operations and invoking prescribed computation routines as specified by the input template; and to provide results of the benchmark indicative of a measure of performance of the specified task corresponding to the target multi-core processing system.

Abstract: Techniques for transferring a matrix for performing one or more operations are provided. The techniques include applying a permutation on at least one of one or more columns and one or more rows of a matrix to group each of at least one of one or more columns and one or more rows of the matrix with a same alignment, blocking at least one of the grouped columns and grouped rows, and performing one or more operations on each matrix block.

Abstract: A method for evaluating performance of DMA-based algorithmic tasks on a target multi-core processing system includes the steps of: inputting a template for a specified task, the template including DMA-related parameters specifying DMA operations and computational operations to be performed; evaluating performance for the specified task by running a benchmark on the target multi-core processing system, the benchmark being operative to generate data access patterns using DMA operations and invoking prescribed computation routines as specified by the input template; and providing results of the benchmark indicative of a measure of performance of the specified task corresponding to the target multi-core processing system.

Abstract: Measurement errors arising from presentation aspects of a survey are detected and can be corrected, based upon responses from a selected set of respondents to different survey presentations. The survey presentations are different not in their information content, but in their presentation, such as using a different order of questions. An alternative presentation or presentations for the survey can be derived such that the measurement error is reduced. Accordingly, an “unbiased” survey can be designed in the pre-deployment phase, or measurement errors can be detected and corrected during the deployment phase.

Abstract: Apparatus for evaluating the performance of DMA-based algorithmic tasks on a target multi-core processing system includes a memory and at least one processor coupled to the memory. The processor is operative: to input a template for a specified task, the template including DMA-related parameters specifying DMA operations and computational operations to be performed; to evaluate performance for the specified task by running a benchmark on the target multi-core processing system, the benchmark being operative to generate data access patterns using DMA operations and invoking prescribed computation routines as specified by the input template; and to provide results of the benchmark indicative of a measure of performance of the specified task corresponding to the target multi-core processing system.

Abstract: A method for evaluating performance of DMA-based algorithmic tasks on a target multi-core processing system includes the steps of: inputting a template for a specified task, the template including DMA-related parameters specifying DMA operations and computational operations to be performed; evaluating performance for the specified task by running a benchmark on the target multi-core processing system, the benchmark being operative to generate data access patterns using DMA operations and invoking prescribed computation routines as specified by the input template; and providing results of the benchmark indicative of a measure of performance of the specified task corresponding to the target multi-core processing system.

Abstract: Apparatus for evaluating the performance of DMA-based algorithmic tasks on a target multi-core processing system includes a memory and at least one processor coupled to the memory. The processor is operative: to input a template for a specified task, the template including DMA-related parameters specifying DMA operations and computational operations to be performed; to evaluate performance for the specified task by running a benchmark on the target multi-core processing system, the benchmark being operative to generate data access patterns using DMA operations and invoking prescribed computation routines as specified by the input template; and to provide results of the benchmark indicative of a measure of performance of the specified task corresponding to the target multi-core processing system.

Abstract: A method for evaluating performance of DMA-based algorithmic tasks on a target multi-core processing system includes the steps of: inputting a template for a specified task, the template including DMA-related parameters specifying DMA operations and computational operations to be performed; evaluating performance for the specified task by running a benchmark on the target multi-core processing system, the benchmark being operative to generate data access patterns using DMA operations and invoking prescribed computation routines as specified by the input template; and providing results of the benchmark indicative of a measure of performance of the specified task corresponding to the target multi-core processing system.

Abstract: Historical data accumulated during routine business operations is analyzed to prepare a ranked list of prospective objectives for a survey. Specified relationships are used in conjunction with relationships inferred from the historical data. Collectively, these relationships are referred to as the model. Deviation of the current data or a subset of the data from the model, and the extent of this deviation, is used to prepare a ranked list of prospective objectives for a survey. Surveys focusing on one or more of these objectives are prepared for obtaining business intelligence. A list of prospective objectives of a survey is validated against the model to design more informative surveys.

Abstract: Measurement errors arising from presentation aspects of a survey are detected and can be corrected, based upon responses from a selected set of respondents to different survey presentations. The survey presentations are different not in their information content, but in their presentation, such as using a different order of questions. An alternative presentation or presentations for the survey can be derived such that the measurement error is reduced. Accordingly, an “unbiased” survey can be designed in the pre-deployment phase, or measurement errors can be detected and corrected during the deployment phase.