Optimization and performance evaluation of graphic processing units for voice processing. Issue 4 (December 2017)

• Record Type: Journal Article
• Authors: Sayadi, Fatma E; Bahri, Haythem; Chouchene, Marwa; Atri, Mohamed
• Is Part Of: Journal of algorithms & computational technology. Volume 11:Issue 4(2017)
• Page Start: 388
• Page End: 394
• Publication Date: 2017-12
• Subjects: Linear predictive coding -- graphic processing unit -- field-programmable gate arrays -- optimization strategies -- shared memory; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301817725314
• Languages: English
• ISSNs: 1748-3018

Abstract

With the advancement in the device technology and parallel architecture, field-programmable gate arrays (FPGAs) can well perform the speech processing operation. FPGAs have very impressive results, despite their low operating frequency, by completely extracting the parallelism. Nevertheless, recent central processing unit and graphic processing unit (GPU) have also an inherent feature for high performance. In fact, recent GPUs enable dramatic increases in computing performance by harnessing great number of cores. In this context, we seek to analyze the performance of the linear prediction coding algorithm implementation on two different platforms: one based on the GPU NVIDIA GeForce GTX 480 and another on the FPGA Spartan-6. Subsequently, we try to apply several optimization strategies on those platforms. The experimental results highlight the relative robustness or weakness of both these platforms. The tests prove that, for several samples, GPU manages speedups of up to 4× compared to the FPGA and around 48× compared to a sequential execution.