2014/02/20 by Sze-Hang Chan, Jeanno Cheung, Chan, Sze-Hang +16
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Engineering #Distributed #Distributed and Parallel Computing Systems #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Gene expression and cancer classification #Genomics (q-bio.GN) #Genomics and Phylogenetic Studies #Parallel #and Cluster Computing (cs.DC) #and Science (cs.CE) #cs.CE #cs.DC #q-bio.GN
paper · pdf · doi:10.48550/arxiv.1402.4876
2 pages, 1 figure, 2 tables, submitted to Bioinformatics as an application note
arxiv created 2014/02/20 · openalex publication_date 2014/02/20 · arxiv updated 2014/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Background: Short-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alternative to GPU is Intel MIC; supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily; however, the performance is often inferior to GPU counterparts as an MIC board contains only ~60 cores (while a GPU board typically has over a thousand cores). Results: To better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MICs limitation and the extra parallelism inside each MIC core. Experiments on aligning 150bp paired-end reads show that MICA using one MIC board is 4.9 times faster than the BWA-MEM (using 6-core of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICAs simplicity allows very efficient scale-up when multiple MIC boards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM). Summary: MICA can be readily used by MIC-enabled supercomputers for production purpose. We have tested MICA on Tianhe-2 with 90 WGS samples (17.47 Tera-bases), which can be aligned in an hour less than 400 nodes. MICA has impressive performance even though the current MIC is at its initial stage of development (the next generation of MIC has been announced to release in late 2014).