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Fast Histograms using Adaptive CUDA Streams

Sisir Koppaka, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan
Depts. Of Mechanical & Industrial Engineering, Indian Institute of Technology, Kharagpur, India
arXiv:1011.0235 [cs.DC] (1 Nov 2010)

@article{koppaka2010fast,

   title={Fast Histograms using Adaptive CUDA Streams},

   author={Koppaka, S. and Mudigere, D. and Narasimhan, S. and Narayanan, B. and Kharagpur, I. and Bangalore, I.},

   journal={Arxiv preprint arXiv:1011.0235},

   year={2010}

}

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Histograms are widely used in medical imaging, network intrusion detection, packet analysis and other stream-based high throughput applications. However, while porting such software stacks to the GPU, the computation of the histogram is a typical bottleneck primarily due to the large impact on kernel speed by atomic operations. In this work, we propose a stream-based model implemented in CUDA, using a new adaptive kernel that can be optimized based on latency hidden CPU compute. We also explore the tradeoffs of using the new kernel vis-‘a-vis the stock NVIDIA SDK kernel, and discuss an intelligent kernel switching method for the stream based on a degeneracy criterion that is adaptively computed from the input stream.
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