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Application-independent Autotuning for GPUs
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Autotuning is an established technique for adjusting performance-critical parameters of applications to their specific run-time environment. In this paper, we investigate the potential of online autotuning for general purpose computation on GPUs. Our application-independent autotuner AtuneRT optimizes GPU-specific parameters such as block size and loop-unrolling degree. We also discuss the peculiarities of autotuning on GPUs. We demonstrate tuning potential using CUDA and by instrumenting the parallel algorithms library Thrust. We evaluate our online autotuning approach with various GPUs and sample applications.
Title: Application-independent Autotuning for GPUs
Description:
Autotuning is an established technique for adjusting performance-critical parameters of applications to their specific run-time environment.
In this paper, we investigate the potential of online autotuning for general purpose computation on GPUs.
Our application-independent autotuner AtuneRT optimizes GPU-specific parameters such as block size and loop-unrolling degree.
We also discuss the peculiarities of autotuning on GPUs.
We demonstrate tuning potential using CUDA and by instrumenting the parallel algorithms library Thrust.
We evaluate our online autotuning approach with various GPUs and sample applications.
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Abstract
Modern applications often involve complex, structured or data-parallel computations on large datasets. Traditionally, GPUs have served as the primary acc...