Parallelizing Multi-featured Content Based Search and Retrieval of Videos through High Performance Computing

Nasreen, Azra; Shobha, G

doi:10.11591/ijeecs.v5.i1.pp214-219

Cited by 2 publications

(1 citation statement)

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“…The process flow of CUDA programming is described below [10]: a. Data is copied from the main memory to the GPU global memory b. CPU sends the processing instructions to the GPU c. Each core at the GPU memory parallelly executes the data d. Result from GPU global memory is copied back to main memory In this paper, we propose an implementation of ALAC audio compression on NVIDIA GPUs.…”

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confidence: 99%

Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

Ahmed¹,

Islam²,

Uddin³

2018

IJECE

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As majority of the compression algorithms are implementations for CPU architecture, the primary focus of our work was to exploit the opportunities of GPU parallelism in audio compression. This paper presents an implementation of Apple Lossless Audio Codec (ALAC) algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. The core idea was to identify the areas where data parallelism could be applied and parallel programming model CUDA could be used to execute the identified parallel components on Single Instruction Multiple Thread (SIMT) model of CUDA. The dataset was retrieved from European Broadcasting Union, Sound Quality Assessment Material (SQAM). Faster execution of the algorithm led to execution time reduction when applied to audio coding for large audios. This paper also presents the reduction of power usage due to running the parallel components on GPU. Experimental results reveal that we achieve about 80-90% speedup through CUDA on the identified components over its CPU implementation while saving CPU power consumption.

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confidence: 99%