3HUIRUPDQFH,PSURYHPHQWRI%:$0(0 $OJRULWKPXVLQJ'DWDSDUDOOHOZLWK&RQFXUUHQW 3DUDOOHOL)DWLRQ

Burrows-Wheeler Transform(BWT) is the widely used data compression technique in the next-generation sequencing (NGS) analysis. Due to the advancement in the NGS technology, the genome data size was increased rapidly and these higher volumes of genome data need to be processed by empirical parallelism. Generally, these NGS data will be processed by traditional parallelprocessing approaches like (i) thread parallelization(ii) Data parallelizationand (iii) Concurrent parallelization, which are their own performance bottlenecks in, thread scalability, scattering/gathering of data and memory bandwidthlimitations respectively. To eliminate these drawbacks, we introduced the hybrid parallelizationapproach called " data-parallelwith concurrent parallelization" to process our genome alignment. We used BWA MEM algorithm for aligning human genome sequence, which are dominated by huge memory intensive operations and the performance is limited due to cache/TLB misses. To eliminate the cache/TLB miss, the genome data is partitioned into multiple pieces (i.e., reducing the read size) using data parallelizationand concurrently processing these multiple pieces of genome data within the same cache/ memory hierarchy. Hence, the performance of proposed data-parallelwith concurrent parallelizationis 45% better than traditional parallelizationapproaches. Additionally, we provided proof of concept to process higher volume of genome data using BWA MEM algorithm on the high-end desktop machines.