| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 98 === The development of information technology is rapid. With the network bandwidth and storage devices continue to evolve, require for digital data storage demand is rising. Data compression has become an important and unavoidable issue. The lossless compression has the ability to ensure data accuracy. In consideration of coding efficiency, coding delay and complexity of coding, how to strike a balance between the values is important research direction.
In this paper, we rewrite the lossless compression Bzip2 algorithms in the way for the parallel processing, first introduced the Nvidia CUDA the parallel programming environment. Using GPU on the graphics cards to achieve more operations besides 3D graphics computing, GPGPU, by a powerful graphics computing power to carry out the work of compression. As the CUDA support to the use of C language, so the threshold get lower for the development of GPU programming is a very suitable experimental environment. Apart from the evolution of the current lossless compression, but also the way introduce CUDA programming architecture and hardware.
For lossless coding improvements, this paper will execute Burrows-Wheeler Transformation and the Move-To-Front transformation before the compression entropy coding. This method can improve the lossless compression ratio, and our program also focuses on the parallel operation on both transform. In addition to the concept of distribute the program, we will compare the performance about CUDA GPU program and Bzip2 CPU program. This paper checks the results for comparison and analysis, finally discuss the impact of parallel compression algorithm implemented on this system.
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