AUTO-GC: Automatic translation of data mining applications to GPU clusters

Wenjing Ma, Gagan Agrawal
Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH, USA
In 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW) (April 2010), pp. 1-8.


   title={AUTO-GC: Automatic translation of data mining applications to GPU clusters},

   author={Ma, W. and Agrawal, G.},

   booktitle={Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on},





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Because of the very favorable price to performance ratio of the GPUs, a popular parallel programming configuration today is a cluster of GPUs. However, extracting performance on such a configuration would typically require programming in both MPI and CUDA, thus requiring a high degree of expertise and effort. It is clearly desirable to be able to support higher-level programming of this emerging high-performance computing platform. This paper reports on a code generation system that can translate data mining applications on a GPU cluster. Our work is driven by the observation that a common processing structure, that of generalized reductions, fits a large number of popular data mining algorithms. In our solution, the programmers simply need to specify the sequential reduction loop(s) with some additional information about the parameters. We use program analysis and code generation to automatically map the applications to the API of FREERIDE, which is a middleware for parallel data mining. We also automatically generate CUDA code for using the GPU on each node of the cluster. We have evaluated our system using two popular data mining applications, k-means clustering and Principal Component Analysis (PCA). We observed good scalability over the number of computing nodes, and the automatically generated version did not have any noticeable overheads compared to hand written codes. The speedup obtained by using GPU over using only the CPU on each node of a cluster is between 3 and 21.
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