Investigating Memory Optimization of Hash-index for Next Generation Sequencing on Multi-core Architecture

Abstract

Next Generation Sequencing (NGS) is gaining interests due to the increased requirements and the decreased sequencing cost. The important and prerequisite step of most NGS applications is the mapping of short sequences, called reads, to the template reference sequences. Both the explosion of NGS data with over billions of reads generated each day and the data intensive computations pose great challenges to the capability of existing computing systems. In this paper, we take a hash index based algorithm (PerM) as an example to investigate the optimization approaches for accelerating NGS reads mapping on multi-core architectures. First, we propose a new parallel algorithm that reorders bucket access in hash index among multiple threads so that data locality in shared cache is improved. Second, in order to reduce the number of empty hash bucket, we propose a serialized hash index compression algorithm, which coincides with the sequential access nature of our new parallel algorithm. With reduced hash index size, it also becomes possible for us to use longer hash keys, which alleviates the hash conflicts and improves the query performance. Our experiment on an 8-socket 8-cores Intel Xeon X7550 SMP with 128 GB memory shows that the new parallel algorithm reduces LLC miss ratio to be 8%~15% of the original algorithm and the overall performance is improved by 4~11 times (6 times avg.).