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Heterogeneous computing with an algorithmic skeleton framework
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The Graphics Processing Unit (GPU) is present in almost every modern day personal
computer. Despite its specific purpose design, they have been increasingly used for general
computations with very good results. Hence, there is a growing effort from the community
to seamlessly integrate this kind of devices in everyday computing. However, to
fully exploit the potential of a system comprising GPUs and CPUs, these devices should
be presented to the programmer as a single platform.
The efficient combination of the power of CPU and GPU devices is highly dependent
on each device’s characteristics, resulting in platform specific applications that cannot
be ported to different systems. Also, the most efficient work balance among devices is
highly dependable on the computations to be performed and respective data sizes.
In this work, we propose a solution for heterogeneous environments based on the
abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of
the power of all CPU and GPU devices present in a system, without the need for different
kernel implementations nor explicit work-distribution.To that end, we extended Marrow,
an algorithmic skeleton framework for multi-GPUs, to support CPU computations and
efficiently balance the work-load between devices. Our approach is based on an offline
training execution that identifies the ideal work balance and platform configurations for
a given application and input data size.
The evaluation of this work shows that the combination of CPU and GPU devices can
significantly boost the performance of our benchmarks in the tested environments, when
compared to GPU-only executions.
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Palavras-chave
Algorithmic skeletons OpenCL GPGPU Heterogeneous computing