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Related Experiment Video

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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

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Embedded-Based Graphics Processing Unit Cluster Platform for Multiple Sequence Alignments.

Jyh-Da Wei^1,2, Hui-Jun Cheng¹, Chun-Yuan Lin¹

¹Department of Computer Science and Information Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan.

Evolutionary Bioinformatics Online

|August 25, 2017

Summary

This summary is machine-generated.

A new embedded GPU cluster platform using multiple Jetson Tegra K1 boards (MTK platform) offers a cost-effective solution for high-performance computing. This MTK platform significantly accelerates bioinformatics tasks like multiple sequence alignments.

Keywords:

CUDA NVIDIA Jetson TK1 cluster multiple sequence alignment parallel processing

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Area of Science:

Computer Science
Bioinformatics
High-Performance Computing

Background:

High-end Graphics Processing Units (GPUs) are essential for high-performance computing but are costly and power-intensive.
NVIDIA's Jetson Tegra K1 (TK1) offers an embedded, low-cost, low-power alternative with GPU capabilities.
Previous work established a single TK1 (STK) platform for cost-effective bioinformatics services.

Purpose of the Study:

To construct an embedded-based GPU cluster platform using multiple Jetson Tegra K1 boards (MTK platform).
To design job assignment modes for the MTK platform.
To evaluate the performance of the MTK platform for bioinformatics applications, specifically multiple sequence alignments.

Main Methods:

Construction of a multi-TK1 embedded GPU cluster platform (MTK platform).
Implementation of two distinct job assignment modes.
Porting and performance evaluation of ClustalW v2.0.11 and ClustalWtk on the MTK platform.

Main Results:

The MTK platform was successfully constructed and configured.
Experimental results demonstrated speedup ratios of 5.5x for ClustalW v2.0.11 and 4.8x for ClustalWtk on 6 TK1s compared to a single TK1.
The platform proved effective for accelerating multiple sequence alignment tasks.

Conclusions:

The developed embedded GPU cluster platform (MTK) provides a viable and cost-effective alternative to traditional high-performance computing clusters.
The MTK platform significantly enhances the performance of bioinformatics tools like ClustalW.
This approach is suitable for accelerating demanding computational tasks in bioinformatics and other fields.