User-friendly Interface for GPGPU Programming

被引:0
作者
Gamaarachchi, Hasindu [1 ]
Fawsan, Mohamed [1 ]
Fasna, Fathima [1 ]
Elkaduwe, Dhammika [1 ]
机构
[1] Univ Peradeniya, Dept Comp Engn, Fac Engn, Peradeniya, Sri Lanka
来源
PROCEEDINGS OF THE 2017 6TH NATIONAL CONFERENCE ON TECHNOLOGY & MANAGEMENT (NCTM) - EXCEL IN RESEARCH AND BUILD THE NATION | 2017年
关键词
CUDA; GPGPU user-interface; web-interface; matrix manipulation;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Compute Unified Device Architecture (CUDA) is an attractive alternative for our ever growing need for high performance computing. However to extract the full potential of CUDA one should, at the least be familiar with the programming model and should have a fair understanding of the memory and the cache architecture. Yet most of the domain experts from domains that warrant high performance computing are ill trained to develop efficient CUDA programs that would extract the necessary performance. In this paper we argue that this gap can be bridged by exposing the CUDA architecture as an API for manipulating matrices. We observe that many of the high demanding scientific computations can be expressed as matrix manipulations, where the need for high performance stems for the size of the matrix. We present a Software as a Service (SaaS) solution to bridge this gap where a domain specialist uploads the data as matrices and specify the operations as an equation involving the uploaded matrices via web GUI. Then the hack end will process the request using CUDA and return the results via the GUI. The CUDA code for handling matrix operations are highly optimized and the domain specialist can simply use them without knowing the underlying intricate details.
引用
收藏
页码:99 / 104
页数:6
相关论文
共 21 条
  • [1] AdaptiveComputing, 2016, TORQ RES MAN
  • [2] [Anonymous], CUDA C PROGR GUID
  • [3] SPOC: GPGPU PROGRAMMING THROUGH STREAM PROCESSING WITH OCAML
    Bourgoin, Mathias
    Chailloux, Emmanuel
    Lamotte, Jean-Luc
    [J]. PARALLEL PROCESSING LETTERS, 2012, 22 (02)
  • [4] EMPhotonics, 2016, CUL TOOLS
  • [5] Fonim M. P., 1994, TECH REP
  • [6] Gamaarachchi H., 2016, WEB
  • [7] GPU Acceleration for FEM-Based Structural Analysis
    Georgescu, Serban
    Chow, Peter
    Okuda, Hiroshi
    [J]. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2013, 20 (02) : 111 - 121
  • [8] Kolesnichenko A., 2015, GPCE 2015, P75
  • [9] CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms
    Lee, Daren
    Dinov, Ivo
    Dong, Bin
    Gutman, Boris
    Yanovsky, Igor
    Toga, Arthur W.
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2012, 106 (03) : 175 - 187
  • [10] Improved GPU/CUDA Based Parallel Weather and Research Forecast (WRF) Single Moment 5-Class (WSM5) Cloud Microphysics
    Mielikainen, Jarno
    Huang, Bormin
    Huang, Hung-Lung Allen
    Goldberg, Mitchell D.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (04) : 1256 - 1265