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

PI将对现场可编程门阵列(FGA)进行研究，包括在几个现场可编程门阵列、计算机和存储器之间划分算法；开发硬件高效算法；以及探索交互式可视化方法。这项研究将应用于生物信息学。PI具有使用现场可编程门阵列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法，用于现场可编程门阵列应用的序列的BLAST和FASTA。该项目正在与北卡罗来纳州的生物信息学计划合作。大学在生物信息学方面的新举措，包括在北卡罗来纳大学夏洛特分校投资3500万美元的新中心，将提供一个最大限度地发挥影响的环境。研究将被整合到大学的教育计划和课程中；PI将使用基于团队的项目和基于问题的学习来促进教育。PI计划建立一个WWW站点，通过Java接口共享结果并处理来自其他大学的数据。

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.