Accelerating Multimedia Applications Using Intel Threading Building Blocks on Multi-Core Processors

Cheong-Ghil Kim
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引用次数: 6

Abstract

The recent development on semiconductor process and design technologies enables multi-core processors to become a dominant market trend in desk-top PCs as well as high end mobile devices. At the same time, the increasing popularity of high quality digital contents processing makes processors to quip with dedicated instructions based on sub-word parallelism in order to process streaming data. This paper presents a way of optimizations of 2D convolution operator, a widely used technique in image and signal processing applications, on speed in Intel multi-core processors. Two optimization techniques are discussed in detail. One is the streaming SIMD (Single Instruction Multiple Data) extension (SSE) technology, available in Intel processors, for data parallelism. The other is the Intel TBB (Threading Building Block) run-time library to exploit parallelism in task level. As a result, this paper can investigate the advantage of two different parallelisms, both data and task, concurrently. For the performance evaluation, we implemented Sobel operator using SSE and TBB with different combinations and compared their processing speeds. The results show that both technologies have a significant effect on the performance and the processing speed can be greatly improved when using two technologies at the same time.
在多核处理器上使用英特尔线程构建块加速多媒体应用程序
近年来半导体工艺和设计技术的发展使多核处理器成为台式个人电脑和高端移动设备的主导市场趋势。与此同时,高质量数字内容处理的日益普及,使得处理器配备了基于子字并行的专用指令来处理流数据。二维卷积算子是一种广泛应用于图像和信号处理的技术,本文提出了一种在Intel多核处理器上优化二维卷积算子速度的方法。详细讨论了两种优化技术。一种是流SIMD(单指令多数据)扩展(SSE)技术,可用于英特尔处理器,用于数据并行性。另一个是英特尔TBB(线程构建块)运行时库,用于利用任务级的并行性。因此,本文可以研究数据并行和任务并行两种不同并行的优势。为了进行性能评估,我们使用不同组合的SSE和TBB实现了Sobel算子,并比较了它们的处理速度。结果表明,两种技术对性能都有显著的影响,同时使用两种技术可以大大提高处理速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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