Skip to main content
SpringerLink
Log in

Cuda/GPU

  • Living reference work entry
  • First Online:
Encyclopedia of GIS

Synonyms

General-purpose computing on graphics processing units (GPGPUs)

Definition

A graphics processing unit (GPU) is an electronic circuit originally designed to accelerate real-time computation for computer graphics. As one component of the basic hardware inside a modern personal computer, the GPU is connected to the central processing unit (CPU) through a system bus. For the purpose of fast image rendering, which requires that the whole process of image rendering should be completed within one frame (typically 1/30 s), the GPU has been inherently designed as a highly parallelized processor containing many cores, high memory bandwidth, and single-instruction multiple-data (SIMD) execution (Lindholm et al., 2008; Garland and Kirk, 2010).

In recent years, the high performance of modern GPUs has motivated researchers to explore general-purpose computing on GPUs (GPGPUs). This has resulted in GPUs taking over the computational tasks traditionally performed by CPUs, especially...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Bernabé S, Plaza A, Marpu PR et al (2012) A new parallel tool for classification of remotely sensed imagery. Comput Geosci 46:208–218

    Article  Google Scholar 

  • Brodtkorb AR, Hagen TR, Lie K-A et al (2010) Simulation and visualization of the Saint-Venant system using GPUs. Comput Vis Sci 13:341–353

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng T (2013) Accelerating universal Kriging interpolation algorithm using CUDA-enabled GPU. Comput Geosci 54:178–183

    Article  Google Scholar 

  • Cruz FA, Layton SK, Barba LA (2011) How to obtain efficient GPU kernels: an illustration using FMM & FGT algorithms. Comput Phys Commun 182:2084–2098

    Article  Google Scholar 

  • Du P, Weber R, Luszczek P et al (2012) From CUDA to OpenCL: towards a performance-portable solution for multi-platform GPU programming. Parallel Comput 38:391–407

    Article  Google Scholar 

  • Feichtinger C, Habich J, Kostler H et al (2011) A flexible patch-based lattice Boltzmann parallelization approach for heterogeneous GPU-CPU clusters. Parallel Comput 37:536–549

    Article  MathSciNet  Google Scholar 

  • Fort M, Sellares A, Valladares N (2014) A parallel GPU-based approach for reporting flock patterns. Int J Geogr Inf Sci 28(9):1877–1903

    Article  Google Scholar 

  • Garland M, Kirk DB (2010) Understanding throughput-oriented architectures. Commun ACM 53:58–66

    Article  Google Scholar 

  • Garland M, LeGrand S, Nickolls J et al (2008) Parallel computing experiences with CUDA. IEEE Micro 28(4):13–27

    Article  Google Scholar 

  • Kalyanapu AJ, Shankar S, Pardyjak ER et al (2011) Assessment of GPU computational enhancement to a 2D flood model. Environ Model Softw 26:1009–1016

    Article  Google Scholar 

  • Larsen ES, McAllister D (2001) Fast matrix multiplies using graphics hardware. Paper presented at Supercomputing, Denver, 10–16 Nov 2001

    Google Scholar 

  • Lindholm E, Nickolls J, Oberman S et al (2008) NVIDIA tesla: a unified graphics and computing architecture. IEEE Micro 28(2):39–55

    Article  Google Scholar 

  • Lukač N, Žalik B (2013) GPU-based roofs’ solar potential estimation using LiDAR data. Comput Geosci 52: 34–41

    Article  Google Scholar 

  • Munshi A (2012) The OpenCL specification (Version 1.2). Khronos OpenCL Working Group

    Google Scholar 

  • Nickolls J, Buck I, Garland M et al (2008) Scalable parallel programming with CUDA. ACM Queue 6(2): 40–53

    Article  Google Scholar 

  • NVIDIA Corp. (2012) NVIDIA CUDA C programming guide (Version 4.2)

    Google Scholar 

  • Oryspayev D, Sugumaran R, DeGroote J et al (2012) LiDAR data reduction using vertex decimation and processing with GPGPU and multicore CPU technology. Comput Geosci 43:118–125

    Article  Google Scholar 

  • Owens JD, Luebke D, Govindaraju N et al (2007) A survey of general-purpose computation on graphics hardware. Comput Graph Forum 26(1):80–113

    Article  Google Scholar 

  • Qin C-Z, Zhan L (2012) Parallelizing flow-accumulation calculations on Graphics Processing Units—from iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm. Comput Geosci 43:7–16

    Article  Google Scholar 

  • Qin C-Z, Zhan L-J, Zhu A-X et al (2014) A strategy for raster-based geocomputation under different parallel computing platforms. Int J Geogr Inf Sci 28(11):2127–2144

    Article  Google Scholar 

  • Siewertsen E, Piwonski J, Slawig T (2013) Porting marine ecosystem model spin-up using transport matrics to GPUs. Geosci Model Dev 6:17–28

    Article  Google Scholar 

  • Singh B, Pardyjak ER, Norgren A et al (2011) Accelerating urban fast response Lagrangian dispersion simulations using inexpensive graphics processor parallelism. Environ Model Softw 26:739–750

    Article  Google Scholar 

  • Stone JE, Gohara D, Shi G (2010) OpenCL: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73

    Article  Google Scholar 

  • Tang W (2013) Parallel construction of large circular cartograms using graphics processing units. Int J Geograph Inf Sci 27(11):2182–2206

    Article  Google Scholar 

  • Tang W, Bennett DA (2011) Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units. Ecol Model 222:3605–3615

    Article  Google Scholar 

  • Tristram D, Hughes D, Bradshaw K (2014) Accelerating a hydrological uncertainty ensemble model using graphics processing units (GPUs). Comput Geosci 62:178–186

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Resources & Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing P.R., China

    Cheng-Zhi Qin

Authors
  1. Cheng-Zhi Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng-Zhi Qin .

Editor information

Editors and Affiliations

  1. Minneapolis, Minnesota, USA

    Shashi Shekhar

  2. Rutgers Business School, Newark, New Jersey, USA

    Hui Xiong

  3. The University of Iowa Tippie College of Business, Iowa City, Iowa, USA

    Xun Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this entry

Cite this entry

Qin, CZ. (2016). Cuda/GPU. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-23519-6_1606-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23519-6_1606-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-23519-6

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation