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The Journal of Supercomputing

, Volume 68, Issue 1, pp 65–86 | Cite as

A CUDA implementation of the Continuous Space Language Model

  • Elizabeth A. Thompson
  • Timothy R. Anderson
Article

Abstract

The training phase of the Continuous Space Language Model (CSLM) was implemented in the NVIDIA hardware/software architecture Compute Unified Device Architecture (CUDA). A detailed explanation of the CSLM algorithm is provided. Implementation was accomplished using a combination of CUBLAS library routines, NVIDIA NPP functions, and CUDA kernel calls on three different CUDA enabled devices of varying compute capability and a time savings over the traditional CPU approach demonstrated. The efficiency of the CUDA version of the open source implementation is analyzed and compared to that using the Intel Math Kernel Libraries (MKL) on a variety of CUDA enabled and multi-core CPU platforms. It is demonstrated that substantial performance benefit can be obtained using CUDA, even with nonoptimal code. Techniques for optimizing performance are then provided. Furthermore, an analysis is performed to determine the conditions in which the performance of CUDA exceeds that of the multi-core MKL realization.

Keywords

CUDA CSLM GPU Statistical signal processing CUBLAS Math Kernel Library BLAS High performance computing 

Notes

Acknowledgements

Many thanks to Mike Pressler, IPFW Manager Electronics and Computer Support Services, for his outstanding technical support.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Purdue University Fort WayneFort WayneUSA
  2. 2.Air Force Research Laboratory, 711th Human Performance WingWright Patterson Air Force BaseDaytonUSA

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