A CUDA Implementation of the Continuous Space Language Model
Journal of Supercomputing
Springer New York LLC
Place of Publication
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.
CUDA, CSLM, GPU, Statistical signal processing, CUBLAS, Math Kernel Library, BLAS, High performance computing
Electrical and Computer Engineering | Engineering | Signal Processing
Elizabeth A. Thompson and Timothy R. Anderson Ph.D.. (2014).
A CUDA Implementation of the Continuous Space Language Model. Journal of Supercomputing.68 (1), 65-86. United States: Springer New York LLC.
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