A performance prediction model for the CUDA GPGPU platform

The significant growth in computational power of modern Graphics Processing Units(GPUs) coupled with the advent of general purpose programming environments like NVIDA's CUDA, has seen GPUs emerging as a very popular parallel computing platform. However, despite their popularity, there is no performance model of any GPGPU programming environment. 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 perfor...

• Type: Journal
• Year: 2009
• Where: HIPC
• Authors: Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan
• Keywords: CUDA, Distributed And Parallel Computing, HIPC 2009, Model, Programming Environment