GPU-accelerated DNA distance matrix computation

被引：4
|
作者：
Ying Z. [1 ]
Lin X. [1 ]
See S.C.-W. [1 ,2 ,3 ]
Li M. [1 ,4 ]
机构：
[1] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai
[2] Institute of High Performance Computing, AStar, Singapore
来源：
Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011 | 2011年
关键词：
distance matrix; GPU; OpenCL; phylogeny;
D O I：
10.1109/ChinaGrid.2011.11
中图分类号：
学科分类号：
摘要：
Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein's DNADIST program by using OpenCL to exploit the great computation capability of graphic card. The GPU-accelerated DNADIST program achieves more than 12-fold speedup over the serial CPU program on a personal workstation with a 2.66GHz quad-core Intel CPU and an AMD HD5850 graphics card. And dual HD5850 cards on the same platform perform linear improvement of 24-fold speedup. The program also shows good performance portability by achieving 16-fold speedup with a NVIDIA Tesla C2050 card. © 2011 IEEE.
引用
收藏
页码：42 / 47
页数：5
相关论文
共 50 条
  • [21] GPU-accelerated Preconditioned GMRES Solver
    Yang, Bo
    Liu, Hui
    Chen, Zhangxin
    Tian, Xuhong
    2016 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY), IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING (HPSC), AND IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS), 2016, : 280 - 285
  • [22] GPUNFV: a GPU-Accelerated NFV System
    Yi, Xiaodong
    Duan, Jingpu
    Wu, Chuan
    PROCEEDINGS OF THE 2017 ASIA-PACIFIC WORKSHOP ON NETWORKING (APNET '17), 2017, : 85 - 91
  • [23] GPU-Accelerated BFS for Dynamic Networks
    Ziche, Filippo
    Bombieri, Nicola
    Busato, Federico
    Giugno, Rosalba
    EURO-PAR 2024: PARALLEL PROCESSING, PT III, EURO-PAR 2024, 2024, 14803 : 74 - 87
  • [24] A NEW APPROACH OF GPU-ACCELERATED STOCHASTIC GRADIENT DESCENT METHOD FOR MATRIX FACTORIZATION
    Li, Feng
    Ye, Yunming
    Li, Xutao
    Lu, Jiajie
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2019, 15 (02): : 697 - 711
  • [25] An Efficient Approach of GPU-accelerated Stochastic Gradient Descent Method for Matrix Factorization
    Li, Feng
    Ye, Yunming
    Li, Xutao
    JOURNAL OF INTERNET TECHNOLOGY, 2019, 20 (04): : 1087 - 1097
  • [26] Efficient GPU-Accelerated Extraction of Imperfect Inverted Repeats from DNA Sequences
    Baskett, William
    Spencer, Matthew
    Shyu, Chi-Ren
    2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2017, : 516 - 520
  • [27] GPU-accelerated level-set segmentation
    Julián Lamas-Rodríguez
    Dora B. Heras
    Francisco Argüello
    Dagmar Kainmueller
    Stefan Zachow
    Montserrat Bóo
    Journal of Real-Time Image Processing, 2016, 12 : 15 - 29
  • [28] GPU-Accelerated Finite-Element Matrix Generation for Lossless, Lossy, and Tensor Media
    Dziekonski, Adam
    Sypek, Piotr
    Lamecki, Adam
    Mrozowski, Michal
    IEEE ANTENNAS AND PROPAGATION MAGAZINE, 2014, 56 (05) : 186 - 197
  • [29] GPU-Accelerated Simulation of Elastic Wave Propagation
    Kadlubiak, Kristian
    Jaros, Jiri
    Treeby, Bredly E.
    PROCEEDINGS 2018 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING & SIMULATION (HPCS), 2018, : 188 - 195
  • [30] GPU-accelerated name lookup with component encoding
    Wang, Yi
    Dai, Huichen
    Zhang, Ting
    Meng, Wei
    Fan, Jindou
    Liu, Bin
    COMPUTER NETWORKS, 2013, 57 (16) : 3165 - 3177
12345