Wei Jyh-Da, Cheng Hui-Jun, Lin Chun-Yuan, Ye Jin, Yeh Kuan-Yu
Department of Computer Science and Information Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan.
Department of Ophthalmology, Chang Gung Memorial Hospital, Keelung, Taiwan.
Evol Bioinform Online. 2017 Aug 8;13:1176934317724764. doi: 10.1177/1176934317724764. eCollection 2017.
High-end graphics processing units (GPUs), such as NVIDIA Tesla/Fermi/Kepler series cards with thousands of cores per chip, are widely applied to high-performance computing fields in a decade. These desktop GPU cards should be installed in personal computers/servers with desktop CPUs, and the cost and power consumption of constructing a GPU cluster platform are very high. In recent years, NVIDIA releases an embedded board, called Jetson Tegra K1 (TK1), which contains 4 ARM Cortex-A15 CPUs and 192 Compute Unified Device Architecture cores (belong to Kepler GPUs). Jetson Tegra K1 has several advantages, such as the low cost, low power consumption, and high applicability, and it has been applied into several specific applications. In our previous work, a bioinformatics platform with a single TK1 (STK platform) was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform). Complex system installation and setup are necessary procedures at first. Then, 2 job assignment modes are designed for the MTK platform to provide services for users. Finally, ClustalW v2.0.11 and ClustalWtk will be ported to the MTK platform. The experimental results showed that the speedup ratios achieved 5.5 and 4.8 times for ClustalW v2.0.11 and ClustalWtk, respectively, by comparing 6 TK1s with a single TK1. The MTK platform is proven to be useful for multiple sequence alignments.
高端图形处理单元（GPU），如英伟达Tesla/Fermi/Kepler系列芯片，每个芯片拥有数千个核心，在过去十年中被广泛应用于高性能计算领域。这些桌面GPU卡应安装在配备桌面CPU的个人电脑/服务器中，构建GPU集群平台的成本和功耗非常高。近年来，英伟达发布了一款嵌入式开发板，名为Jetson Tegra K1（TK1），它包含4个ARM Cortex-A15 CPU和192个计算统一设备架构核心（属于Kepler GPU）。Jetson Tegra K1具有成本低、功耗低和适用性强等优点，已被应用于多个特定应用中。在我们之前的工作中，构建了一个基于单个TK1的生物信息学平台（STK平台），并且通过将STK平台与桌面CPU和GPU进行比较，这项先前的工作也用于证明可以在STK平台上以良好的性价比实现网络和移动服务。在这项工作中，将构建一个基于多个TK1的嵌入式GPU集群平台（MTK平台）。首先，复杂的系统安装和设置是必要的步骤。然后，为MTK平台设计了2种作业分配模式，以便为用户提供服务。最后，将ClustalW v2.0.11和ClustalWtk移植到MTK平台。实验结果表明，通过将6个TK1与单个TK1进行比较，ClustalW v2.0.11和ClustalWtk的加速比分别达到了5.5倍和4.8倍。MTK平台被证明对于多序列比对是有用的。
