CRI: Field Programmable Gate Array (FPGA) based Hypercomputing Acceleration Platform for Bioinformatics

PI将进行现场可编程门阵列（fpga）的研究，包括多个fpga、计算机和存储器之间的划分算法；开发硬件高效算法；探索交互式可视化方法。该研究将应用于生物信息学。PI有使用fpga进行全局序列比较的经验。他们将考虑其他生物信息学算法，如用于任务划分的模拟退火，用于fpga应用的序列的BLAST和FASTA。该项目正在与北卡罗来纳生物信息学项目合作。大学在生物信息学方面的新举措，包括北卡罗来纳大学夏洛特分校斥资3500万美元新建的中心，将提供一个发挥最大影响的环境。该研究将被整合到大学的教育项目和课程中；PI将使用基于团队的项目和基于问题的学习来加强教育。PI计划建立一个WWW网站，通过JAVA接口共享来自其他大学的结果和处理数据。

The PI will conduct research on Field Programmable Gate Arrays (FPGAs) including partitioning algorithms among several FPGAs, computers and memories; developing hardware efficient algorithms; and exploring interactive visualization methods. The research will be applied to bioinformatics. The PI has experience with using FPGAs for global sequence comparison. They will consider other bioinformatics algorithms such as simulated annealing for task partitioning, BLAST and FASTA for sequences for applications of FPGAs. The project is working with the North Carolina bioinformatics program. New university initiatives in bioinformatics including a new $35 million center at UNC Charlotte will provide a setting to maximize impact.The research will be integrated into education programs and courses at the university; the PI will use team-based projects and problem based learning to enhance education. The PI plans a WWW site to share results and process data from other universities through a JAVA interface.