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Evolution of the Graphics Processing Unit (GPU)
IEEE Micro, 2021Graphics processing units (GPUs) power today’s fastest supercomputers, are the dominant platform for deep learning, and provide the intelligence for devices ranging from self-driving cars to robots and smart cameras. They also generate compelling photorealistic images at real-time frame rates.
William J. Dally +2 more
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A survey of graph processing on graphics processing units
The Journal of Supercomputing, 2018Graphics processing units (GPUs) have become popular high-performance computing platforms for a wide range of applications. The trend of processing graph structures on modern GPUs has also attracted an increasing interest in recent years. This article aims to review research works on adapting the massively parallel architecture of GPUs to accelerate ...
Ha Nguyen Tran, Erik Cambria
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Graphical Processing Units for Quantum Chemistry
Computing in Science & Engineering, 2008The paper provide a brief overview of electronic structure theory and details the implementation of quantum chemistry methods on a graphical processing unit. The paper also analyze algorithm performance in terms of floating-point operations and memory bandwidth, and assess the adequacy of single-precision accuracy for quantum chemistry applications.
Ivan S. Ufimtsev, Todd J. Martínez
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Green computing on graphics processing units
Concurrency and Computation: Practice and Experience, 2015SummaryTo answer the question ‘How much energy is consumed for a numerical simulation running on Graphic Processing Unit?’, an experimental protocol is here established. The current provided to a graphic processing unit (GPU) during computation is directly measured using amperometric clamps.
Magoulès, Frédéric +2 more
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Genetic programming on graphics processing units
Genetic Programming and Evolvable Machines, 2009The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when programmed in the CUDA language.
Denis Robilliard +2 more
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An energy model for graphics processing units
2010 IEEE International Conference on Computer Design, 2010We present an energy model for a graphics processing unit (GPU) that is based on the amount and type of work performed in various parts of the unit. By designing and running directed tests on a GPU, we measure the energy consumed when performing different arithmetic and memory operations, allowing us to accurately predict the energy that any arbitrary ...
Jeff Pool, Anselmo Lastra, Montek Singh
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Graphical processing units and scientific applications
The International Journal of High Performance Computing Applications, 2012This special issue of the International Journal of High Performance Computing Applications collects extended versions of the best three papers presented at the International Workshop on GPUs and Scientific Applications (GPUScA 2010) held in Vienna in September 2010, in conjunction with PACT 2010 – the Annual International Conference on Parallel ...
DI MARTINO, Beniamino +3 more
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Cell placement on graphics processing units
Proceedings of the 20th annual conference on Integrated circuits and systems design, 2007Graphics Processing Units (GPUs) can be viewed as stream processors and, therefore, can be applied to improve the performance of data-parallel algorithms. GPUs can beat CPUs in most stream-like algorithms and have been successfully applied to solve problem in areas such as biology, audio and image processing, database queries and others.
Guilherme Flach +3 more
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Hyperspectral processing in graphical processing units
SPIE Proceedings, 2011With the advent of the commercial 3D video card in the mid 1990s, we have seen an order of magnitude performance increase with each generation of new video cards. While these cards were designed primarily for visualization and video games, it became apparent after a short while that they could be used for scientific purposes.
Michael E. Winter, Edwin M. Winter
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Triangular matrix inversion on Graphics Processing Unit
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, 2009Dense matrix inversion is a basic procedure in many linear algebra algorithms. A computationally arduous step in most dense matrix inversion methods is the inversion of triangular matrices as produced by factorization methods such as LU decomposition. In this paper, we demonstrate how triangular matrix inversion (TMI) can be accelerated considerably by
RIES, FLORIAN +3 more
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