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A performance prediction model for the CUDA GPGPU platform

Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan
Centre for Security, Theory and Algorithms, International Institute of Information Technology, Hyderabad – 500 032, India
In Proceedings of 2009 International Conference on High Performance Computing (HiPC) (December 2009), pp. 463-472

@conference{kothapalli2009performance,

   title={A performance prediction model for the cuda gpgpu platform},

   author={Kothapalli, K. and Mukherjee, R. and Rehman, MS and Patidar, S. and Narayanan, PJ and Srinathan, K.},

   booktitle={High Performance Computing (HiPC), 2009 International Conference on},

   pages={463–472},

   year={2009},

   organization={IEEE}

}

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The significant growth in computational power of modern Graphics Processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA’s CUDA, has seen GPUs emerging as a very popular parallel computing platform. Till recently, there has not been a performance model for GPGPUs. The absence of such a model makes it difficult to definitively assess the suitability of the GPU for solving a particular problem and is a significant impediment to the mainstream adoption of GPUs as a massively parallel (super)computing platform. In this paper we present a performance prediction model for the CUDA GPGPU platform. This model encompasses the various facets of the GPU architecture like scheduling, memory hierarchy, and pipelining among others. We also perform experiments that demonstrate the effects of various memory access strategies. The proposed model can be used to analyze pseudo code for a CUDA kernel to obtain a performance estimate, in a way that is similar to performing asymptotic analysis. We illustrate the usage of our model and its accuracy with three case studies: matrix multiplication, list ranking, and histogram generation.
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