GPU Computing

By John Douglas Owens, M. Houston, David P. Luebke, Simon Green, John E. Stone, James C. Phillips. 2008

1,935 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

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Citations in the corpus

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, background): “This success is often attributed in part to developments in hardware (e.g., GPUs [11] and TPUs [12]) and availability of large datasets (e.g., ImageNet [13]), but more importantly also to the architectural design of neural networks and the remarkable performance of stochastic gradient descent.”
A Comprehensive Performance Comparison of CUDA and OpenCL (2011, background): “All these programming frameworks allow programmers to develop a GPU computing application without mastering graphic terms, and enables them to build large applications easier [9].”
GPU-Accelerated Molecular Modeling Coming Of Age (2010, methodology): “The various challenges overcome include maintainingsufﬁcientnumericalaccuracydespitetheuseofsingle-precision ﬂoating point arithmetic, handling exclusions, and using CPU-fallbackmethodstoprovidecompatibilitywithadvancedsim-ulation features [2,5].”
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer (2011, neutral): “[5] express similar concerns.”
Verifiable Computation with Massively Parallel Interactive Proofs (2012, background): “CPU implementation. However, GPUs are increasingly widespread, cost-effective, and power-efﬁcient, and they offer the potential for speedups in excess of those possible with commodity multi-core CPUs [17, 14]. We obtain server-side speedups ranging from 40-120 for the general-purpose protocol due to”
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].”
Spike sorting for large, dense electrode arrays (2015, methodology): “High performance is achieved by porting most visualization computations to the GPU using custom shaders, and by minimizing the number of OpenGL API calls through batch rendering techniques.”
Graphics processing unit (GPU) programming strategies and trends in GPU computing (2013, background): “However, the proof-of-concept programs demonstrated that the use of GPUs could give dramatic speedups over central processing units (CPUs) for certain algorithms [20,4], and research onGPUs soon led to the development of higher-level third-party languages that abstracted away the graphics.”
Empowering Visual Categorization With the GPU (2011, neutral): “of algorithms which can be parallelized [21].”
Evaluation of the convection‐resolving climate modeling approach on continental scales (2017, methodology): “If the model codes are adapted properly, GPUs provide substantial parallelism and high throughput [Owens et al., 2008], resulting in speedups for weather and climate models [Michalakes and Vachharajani, 2008; Govett et al., 2010; Leutwyler et al., 2016].”
Towards dense linear algebra for hybrid GPU accelerated manycore systems (2009, background): “There are three computational characteristics of graphics rendering [24].”