High-performance computing for super-resolution microscopy on a cluster of computers
Abstract
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.
Cite
@article{arxiv.2206.03231,
title = {High-performance computing for super-resolution microscopy on a cluster of computers},
author = {Quan Do and Jon Ivar Kristiansen and Krishna Agarwal and Phuong Hoai Ha},
journal= {arXiv preprint arXiv:2206.03231},
year = {2022}
}
Comments
The requests I have received from my co-authors