Parallel implementation of 2D Daubechies - D4 transform in a cluster

Jaumin Ajdari, F. Hoxha
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引用次数: 4

Abstract

Wavelet transform is an important mathematical tool with strong application in signal processing. From the mathematical point of view many different wavelet transforms are developed, where orthogonal wavelet transforms are more important. Also, various algorithms for their computations are developed. In this paper we present the parallel implementation of the orthogonal wavelet transform named Daubechies D4 transform, which consists of four taps filters. At the beginning, a short mathematical introduction of the Daubechies D4 transform is presented. After that the sequential and parallel algorithm implementations are analyzed. A parallel implementation for the PC cluster environment is done. We have analyzed two ways of parallel implementation, one with row mapping and the other with block mapping (mesh architecture of the processing elements). For practical implementation the C++ programming language and MPI paradigm (for parallel environment) are used. Finally, the mathematical complexity of these implementations is presented. In particular, we have analyzed separately the complexity of computations and communications. It is shown, also, that the complexity is between O(log2 n) and O(n2 / p). At the end, a comparison of sequential and parallel implementations is presented according to the results obtained by testing the practical implementations.
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在集群中并行实现2D涂抹- D4变换
小波变换是一种重要的数学工具,在信号处理中有很强的应用。从数学角度出发,提出了许多不同的小波变换,其中正交小波变换更为重要。此外,还开发了各种计算算法。本文提出了一种正交小波变换的并行实现方法——Daubechies D4变换,该变换由四个抽头滤波器组成。首先,对Daubechies D4变换进行了简单的数学介绍。然后分析了顺序算法和并行算法的实现。对PC机集群环境进行了并行实现。我们分析了并行实现的两种方式,一种是行映射,另一种是块映射(处理元素的网格结构)。在实际实现中,使用了c++编程语言和MPI范式(用于并行环境)。最后,给出了这些实现的数学复杂性。特别是,我们分别分析了计算和通信的复杂性。复杂度在O(log2 n)和O(n2 / p)之间。最后,根据实际实现的测试结果,对顺序实现和并行实现进行了比较。
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