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Cited by 283Parallel Computing and Optimization TechniquesComputer Graphics and Visualization TechniquesDistributed and Parallel Computing Systems

Graphics processing unit (GPU) programming strategies and trends in GPU computing

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Brodtkorb, André R., Hagen, Trond R., Sætra, Martin L.·2013·Journal of Parallel and Distributed Computing
DOI 10.1016/j.jpdc.2012.04.003
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Abstract

Over the last decade, there has been a growing interest in the use of graphics processing units (GPUs) for non-graphics applications. From early academic proof-of-concept papers around the year 2000, the use of GPUs has now matured to a point where there are countless industrial applications. Together with the expanding use of GPUs, we have also seen a tremendous development in the programming languages and tools, and getting started programming GPUs has never been easier. However, whilst getting started with GPU programming can be simple, being able to fully utilize GPU hardware is an art that can take months or years to master. The aim of this article is to simplify this process, by giving an overview of current GPU programming strategies, profile-driven development, and an outlook to future trends.

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  • NVIDIA is more widely used than AMD in academic and industrial applications.

    “Akademik ve endüstriyel uygulamalarda ise NVIDIA, AMD'ye göre daha yaygın olarak kullanılmaktadır [1].”

    From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [1]

    The Introduction says NVIDIA “appears to be the clear predominant supplier” in academic and industrial environments, in a discussion contrasting NVIDIA and AMD. This supports the comparison, but the citing sentence presents the paper’s qualified observation as a definite fact. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • GPU performance and bandwidth have increased exponentially compared to CPU.

    “GPU'nun performans ve bant genişliği CPU'ya göre üstel olarak artmaktadır.”

    From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [1]

    The Introduction (page not provided) says GPU performance was growing exponentially while CPUs had reached a serial-performance ceiling. It also reports a roughly sevenfold GPU–CPU gap in theoretical peak bandwidth and computing performance, but does not say that GPU bandwidth itself increased exponentially relative to CPU bandwidth. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Authors
Brodtkorb, A. R., Hagen, T. R., & Sætra, M. L.
Affiliations
  • SINTEF (NO)
  • University of Oslo (NO)
Corresponding author
Brodtkorb, André R.
ORCID
Venue
Journal of Parallel and Distributed Computing
Publisher
Elsevier BV
Published
2013-01
Volume
73
Issue
1
Pages
4-13
ISSN
0743-7315 (ISSN-L)
DOI
10.1016/j.jpdc.2012.04.003
OpenAlex
W2070630412
Cited by
283
References
14
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FWCI
33.63
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Parallel and Distributed Computing (publishedVersion, public-domain)
Funders
  • Nvidia
  • Universitetet i Oslo
Topics
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
Keywords
GPU programming, graphics processing units, GPU computing, programming strategies, GPU programming languages, hardware resource utilization

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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