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High-level synthesis of a genomic database search engine

机译:基因组数据库搜索引擎的高级综合

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Genomic database search is an I/O-bound problem, so avoiding unnecessary I/O transactions is a key consideration for improving search throughput. Many approximate search tools such as NCBI BLAST perform a database scan for each query, lacking a mechanism to avoid access to portions of the database that offer no potential for a match. In this paper we present an approach for using an FPGA-based pattern filter to convert each search query into a set of potential database matches that reduces the average portion of the database accessed per query. The approach is based on a hardware design for a pattern filter that can achieve a sustained recognition rate of one pattern per cycle. We used Vivado HLS to design the filter. Despite the presence of loop-carried dependencies, the final design meets the maximum possible throughout as constrained by the code's arithmetic intensity and available memory bandwidth. In this paper we describe the filter implementation and our code tuning methodology.
机译:基因组数据库搜索是I / O绑定的问题,因此避免不必要的I / O事务是提高搜索吞吐量的关键考虑因素。许多近似搜索工具(如NCBI Blast)为每个查询执行数据库扫描,缺少机制,以避免访问不提供匹配可能的潜力的数据库的部分。在本文中,我们介绍了一种使用基于FPGA的模式滤波器的方法来将每个搜索查询转换为一组潜在的数据库匹配,这减少了每个查询访问的数据库的平均部分。该方法基于用于模式滤波器的硬件设计,该模式滤波器可以实现每个周期的一个图案的持续识别率。我们使用Vivado HLS设计过滤器。尽管存在循环依赖性,但最终设计符合代码的算术强度和可用内存带宽的限制。在本文中,我们描述了过滤器实现和我们的代码调整方法。

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