Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Method of Resolving Gyro Zero Drift in Electronic Stabilization System
ICCAE '09 Proceedings of the 2009 International Conference on Computer and Automation Engineering
Reducing branch divergence in GPU programs
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
Anytime Algorithms for GPU Architectures
RTSS '11 Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium
Explorations in design space: unconventional electronics designthrough artificial evolution
IEEE Transactions on Evolutionary Computation
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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 for small, 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.