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Analyzing Performance of the Parallel-based Fractal Image Compression Problem on Multicore Systems

机译:分析多核系统的并联分形图像压缩问题的性能

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Both the size and the resolution of images always were key topics in the graphical computing area. Especially, they become more and more relevant in the big data era. We can observe that often a huge amount of data is exchanged by medium/low bandwidth networks or yet, they need to be stored on devices with limited space of memory. In this context, the present paper shows the use of the Fractal method for image compression. It is a lossy method known by providing higher indexes of file reduction through a highly time consuming phase. In this way, we developed a model of parallel application for exploiting the power of multiprocessor architectures in order to get the Fractal method advantages in a feasible time. The evaluation was done with different-sized images as well as by using two types of machines, one with two and another with four cores. The results demonstrated that both the speedup and efficiency are highly dependent of the number of cores. They emphasized that a large number of threads does not always represent a better performance.
机译:图像的大小和分辨率始终是图形计算区域中的关键主题。特别是,它们在大数据时代变得越来越相关。我们可以观察到,媒体/低带宽网络经常更换大量数据,或者还需要存储在具有有限内存空间的设备上。在这种情况下,本文显示了分形方法进行图像压缩的用途。通过高度耗时的阶段,通过提供更高的文件索引来了解的损失方法。通过这种方式,我们开发了一种用于利用多处理器架构的功率的并行应用模型,以便在可行的时间内获得分形方法优势。评估是用不同大小的图像进行的,以及使用两种机器,一个带有两个和另一个有四个核心的机器。结果表明,加速和效率都高度依赖于核心数。他们强调,大量线程并不总是表示更好的性能。

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