title: MICA: A fast short-read aligner that takes full advantage of Many Integrated Core Architecture (MIC).
abstract: BACKGROUND
  Short-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative 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 card contains only ~60 cores (while a GPU card 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 MIC's limitation and the extra parallelism inside each MIC core. By utilizing the 512-bit vector units in the MIC and implementing a new seeding strategy, experiments on aligning 150 bp paired-end reads show that MICA using one MIC card is 4.9 times faster than BWA-MEM (using 6 cores of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICA's simplicity allows very efficient scale-up when multiple MIC cards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM).
  
  
  CONCLUSIONS
  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 using 400 nodes. MICA has impressive performance even though MIC is only in its initial stage of development.
  
  
  METHODS
  MICA's source code is freely available at http://sourceforge.net/projects/mica-aligner under GPL v3.
  
  
  BACKGROUND
  Supplementary information is available as "Additional File 1". Datasets are available at www.bio8.cs.hku.hk/dataset/mica.
authors.forename: Ruibang
authors.initials: R
authors.lastname: Luo
authors.forename: Jeanno
authors.initials: J
authors.lastname: Cheung
authors.forename: Edward
authors.initials: E
authors.lastname: Wu
authors.forename: Heng
authors.initials: H
authors.lastname: Wang
authors.forename: Sze-Hang
authors.initials: SH
authors.lastname: Chan
authors.forename: Wai-Chun
authors.initials: WC
authors.lastname: Law
authors.forename: Guangzhu
authors.initials: G
authors.lastname: He
authors.forename: Chang
authors.initials: C
authors.lastname: Yu
authors.forename: Chi-Man
authors.initials: CM
authors.lastname: Liu
authors.forename: Dazong
authors.initials: D
authors.lastname: Zhou
authors.forename: Yingrui
authors.initials: Y
authors.lastname: Li
authors.forename: Ruiqiang
authors.initials: R
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Zhu
authors.forename: Shaoliang
authors.initials: S
authors.lastname: Peng
authors.forename: Tak-Wah
authors.initials: TW
authors.lastname: Lam
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D011381:Programming Languages; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software