Abstract
As a consequence of the immense computational power available in GPUs, the usage of these platforms for running data-intensive general purpose programs has been increasing. Since memory and processor architectures of CPUs and GPUs are substantially different, programs designed for each platform are also very different and often resort to a very distinct set of algorithms and data structures. Selecting between the CPU or GPU for a given program is not easy as there are variations in the hardware of the GPU, in the amount of data, and in several other performance factors.
ÆminiumGPU is a new data-parallel framework for developing and running parallel programs on CPUs and GPUs. ÆminiumGPU programs are written in a Java using Map-Reduce primitives and are compiled into hybrid executables which can run in either platforms. Thus, the decision of which platform is going to be used for executing a program is delayed until run-time and automatically performed by the system using Machine-Learning techniques.
Our tests show that ÆminiumGPU is able to achieve speedups up to 65x and that the average accuracy of the platform selection algorithm, in choosing the best platform for executing a program, is above 92%.
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Fonseca, A., Cabral, B. (2013). ÆminiumGPU: An Intelligent Framework for GPU Programming. In: Keller, R., Kramer, D., Weiss, JP. (eds) Facing the Multicore-Challenge III. Lecture Notes in Computer Science, vol 7686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35893-7_9
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DOI: https://doi.org/10.1007/978-3-642-35893-7_9
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