Nec Laboratories America, Inc.

Surendra Byna

Srimat T Chakradhar

Srihari Cadambi

Michela Becchi

Dong Li

A method includes configuring a shared library, stored in a memory, to be loaded into applications to intercept graphics processing unit (GPU) computation requests for different types of workload kernals corresponding to the applications. The method further includes generating a power prediction and a performance prediction for at least one candidate kernel combination for execution on a GPU responsive to the GPU computations requests. The at least one candidate kernel combination pertains to at least two of the workload kernals. The method also includes rendering a decision of whether to execute the at least one candidate kernel combination or to execute the at least two of the workload kernals pertaining thereto separately, based on the power prediction and the performance prediction.

Energy-aware task consolidation on graphics processing unit (GPU)

Systems and method for data-aware scheduling of applications on a heterogeneous platform having at least one central processing unit (CPU) and at least one accelerator. Such systems and methods include a function call handling module configured to intercept, analyze, and schedule library calls on a processing element. The function call handling module further includes a function call interception module configured to intercept function calls to predefined libraries, a function call analysis module configured to analyze argument size and location, and a function call redirection module configured to schedule library calls and data transfers. The systems and methods also use a memory unification module, configured to keep data coherent between memories associated with the at least one CPU and the at least one accelerator based on the output of the function call redirection module.

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Princeton, NJ, United States of America

Surendra Byna