Intel Corporation

Julia Fedorova

Noam Itzhaki

Michael Berezalsky

Arik Narkis

Sion Berkowits

Anton V Gorshkov

Konstantin Levit-Gurevich

Denis Sergeevich Milov

Sunpyo Hong

Eugene V Tcipnjatov

Aleksey Alekseev

Konstantin Levit-Guervich

Disclosed examples to perform instruction-level graphics processing unit (GPU) profiling based on binary instrumentation include: accessing, via a GPU driver executed by a processor, binary code generated by a GPU compiler based on application programming interface (API)-based code provided by an application; accessing, via the GPU driver executed by the processor, instrumented binary code, the instrumented binary code generated by a binary instrumentation module that inserts profiling instructions in the binary code based on an instrumentation schema provided by a profiling application; and providing, via the GPU driver executed by the processor, the instrumented binary code from the GPU driver to a GPU, the instrumented binary code structured to cause the GPU to collect and store profiling data in a memory based on the profiling instructions while executing the instrumented binary code.

Methods and apparatus to perform instruction-level graphics processing unit (GPU) profiling based on binary instrumentation

Systems, apparatuses and methods may provide for technology that receives compiled code and identifies a plurality of blocks in the compiled code. Instrumented code may be generated from the compiled code by modifying the blocks to include probes to measure latencies of the blocks during execution of the instrumented code on a graphics processing unit.

Latency measurement technology

A cognitive control framework system for automatically controlling execution of an application program having a graphical user interface includes a recording component, an execution scenario script, and a playback component. The recording component is adapted to capture user input data and images displayed by the graphical user interface during a recording phase of execution of the application program, and to analyze the captured user input data and displayed images to generate an execution scenario (script) during the recording phase. The execution scenario may be written in a selected high level language (e.g., XML). The playback component is adapted to generate simulated user input data based on the execution scenario during a playback phase of execution of the application program.

Cognitive control framework for automatic control of application programs exposure a graphical user interface

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Nizhny Novgorod, Russia

Julia Fedorova