论文标题
用于超分辨率显微镜的高性能计算在一组计算机上
High-performance computing for super-resolution microscopy on a cluster of computers
论文作者
论文摘要
多个信号分类算法（音乐）提供了一种超分辨率显微镜方法。在先前的研究中，音乐剧在台式计算机或基于Linux的服务器上启用了数据并行性。但是，运行时间需要短。本文将在一组计算机上开发具有高效率和可扩展性的新的并行音乐。我们通过使用集群核心的最佳速度，最新的并行编程技术以及高性能计算库（例如Intel螺纹构建块（TBB），Intel Math Mather kernel库（MKL）以及统一的计算机群集群的统一并行C ++（UPC ++）来实现目的。我们的实验结果表明，新的平行音乐在256核群集上以93.86％的效率在10秒内达到了240.29倍的加速。我们的音乐剧为现实生活中的应用提供了很高的可能性，可以在几秒钟内进行超分辨率显微镜。
Multiple signal classification algorithm (MUSICAL) provides a super-resolution microscopy method. In the previous research, MUSICAL has enabled data-parallelism well on a desktop computer or a Linux-based server. However, the running time needs to be shorter. This paper will develop a new parallel MUSICAL with high efficiency and scalability on a cluster of computers. We achieve the purpose by using the optimal speed of the cluster cores, the latest parallel programming techniques, and the high-performance computing libraries, such as the Intel Threading Building Blocks (TBB), the Intel Math Kernel Library (MKL), and the unified parallel C++ (UPC++) for the cluster of computers. Our experimental results show that the new parallel MUSICAL achieves a speed-up of 240.29x within 10 seconds on the 256-core cluster with an efficiency of 93.86%. Our MUSICAL offers a high possibility for real-life applications to make super-resolution microscopy within seconds.
