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Boosting Data Throughput For Sequence Database Similarity Searches On Fpgas Using An Adaptive Buffering Scheme

机译:使用自适应缓冲方案提高Fpgas上序列数据库相似性搜索的数据吞吐量

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摘要

Searching on DNA and protein databases using sequence comparison algorithms has become one of the most powerful techniques to better understand the functionality of particular biological sequences. However, the requirements to process the biological data exceed the ability of general-purpose processors. FPGAs (Field Programmable Gate Arrays) connected to server processors have been used to accelerate similarity searches. However, reconfigurable computing platforms have utilized an external I/O bus as the communications channel, limiting the communication speed between the host processor and the FPGA. This communication bottleneck often offsets the application speedup enabled by FPGAs. In this paper we present an adaptive data prefetching scheme to avoid reconfigurable processing coprocessor stalls due to data unavailability through profiling methodologies and quantitative analysis. Experimental results on various query sequences show that the proposed scheme can effectively eliminate a major portion of the data access penalty, increase throughput of the FPGA implementation by up to 42%, and achieve a speedup of 110 for affine gap penalties over a standard PC implementation.
机译:使用序列比较算法搜索DNA和蛋白质数据库已成为最强大的技术之一,可以更好地理解特定生物学序列的功能。但是,处理生物数据的要求超出了通用处理器的能力。连接到服务器处理器的FPGA(现场可编程门阵列)已用于加速相似性搜索。但是,可重构计算平台已利用外部I / O总线作为通信通道,从而限制了主机处理器与FPGA之间的通信速度。这种通信瓶颈通常抵消了FPGA支持的应用程序加速。在本文中,我们提出了一种自适应数据预取方案,以避免通过分析方法和定量分析而导致由于数据不可用而导致可重新配置的处理协处理器停顿。在各种查询序列上的实验结果表明,该方案可以有效消除数据访问损失的主要部分,将FPGA实现的吞吐量提高多达42%,并且与标准PC实现相比,仿射间隙惩罚的速度提高了110% 。

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