Parallel Computing Experiences with CUDA

By Michael Garland, Scott M. Le Grand, John R. Nickolls, Joshua Anderson, J. Hardwick, S. Morton, E. Phillips, Yao Zhang, V. Volkov. IEEE Micro, 2008

584 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/4d8bebe9f9ceeddb261185c43d4c04180c90e448

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Citations in the corpus

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster (2010, background): “The research field of GPGPU (‘general purpose computation on GPUs’) or ‘GPU computing’ has clearly been established, see for instance recent surveys on the topic [1, 2, 3, 4].”
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation (2014, methodology): “We had the choice between 3 implementation options: • for larger batches over small matrices, the cublasCgemmBatched library call; • for smaller batches over larger matrices, multiple cublasCgemm calls from the host; • for intermediate batch and matrix sizes, devices of compute capability 3.5 and hi”
A Comprehensive Performance Comparison of CUDA and OpenCL (2011, background): “Increasing parallelism, rather than increasing clock rate, has become the primary engine of processor performance growth, and this trend is likely to continue [1].”
GPU-Accelerated Molecular Modeling Coming Of Age (2010, methodology): “Specializing in molecular dynamics simulations of polymer systems, HOOMD 7 is freely available software explicitly designed for GPU execution [3,7].”
Medical image processing on the GPU - Past, present and future (2013, methodology): “The release of the compute unified device architecture (CUDA) programming language made using Nvidia GPUs for more general purpose calculations much easier (Nickolls et al., 2008; Garland et al., 2008; Che et al., 2008).”
The GPU Computing Era (2010, background): “Many applications—both academic research and industrial products—have been accelerated using CUDA to achieve significant parallel speedups.(10) Such applications fall into a variety of problem domains, including quantum chemistry, molecular dynamics, computational fluid dynamics, quantum physics, fi”
Empowering Visual Categorization With the GPU (2011, methodology): “The model is also applicable to multicore CPUs, as has been shown for CUDA by Stratton et al. [39] and Diamos et al. [40], [41].”