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Adaptive OpenCL (ACL) Execution in GPU Architectures

Dan Connors, Kyle Dunn, Jeff Wiencrot
Department of Electrical Engineering, University of Colorado Denver, Denver, Colorado
International Workshop on Adaptive Self-tuning Computing Systems (ADAPT), co-located with HiPEAC, 2013

@article{connors2013adaptive,

   title={Adaptive OpenCL (ACL) Execution in GPU Architectures},

   author={Connors, Dan and Dunn, Kyle and Wiencrot, Jeff},

   year={2013}

}

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Open Compute Language (OpenCL) has been proposed as a platform-independent, parallel execution model to target heterogeneous systems, including multiple central processing units, graphics processing units (GPUs), and digital signal processors (DSPs). OpenCL parallelism scales with the available resources and hardware generational improvements due to the data-parallel nature of its kernels. Such parallel expressions must adhere to a rigid execution model, essentially forcing the run-time system to behave as a batch-scheduler forsmall, local workgroups of a larger global problem. In many scenarios, especially in the real-time computing environments of mobile computing, a mobile system must adapt to system constraints and problem characteristics. This paper investigates the concept of Adaptive OpenCL (ACL) to explore algorithm support for dynamically adapting data-model properties and runtime machine characteristics. We show that certain algorithms can be structured to dynamically balance problem correctness and performance.
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