A Survey of General‐Purpose Computation on Graphics Hardware

By John Douglas Owens, David P. Luebke, Naga K. Govindaraju, Mark J. Harris, Jens Harald Krüger, Aaron E. Lefohn, Timothy J. Purcell. Eurographics, 2007

2,619 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2bbf413f36f366fa73da4dc028a32131b5d205d6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

A survey of GPU-based medical image computing techniques. (2012, background): “In order to provide reference source for researchers who plan to develop modified GPGPU techniques for their research fields of interest, Owens et al. brought a detailed survey of general-purpose computation on graphics hardware and various general-purpose computing applications (25).”
GPUTeraSort: high performance graphics co-processor sorting for large database management (2006, background): “GPUs present an opportunity for anyone who can use them for tasks beyond graphics [34].”
High performance discrete Fourier transforms on graphics processors (2008, background): “For example, a $300 GPU can deliver peak theoretical performance of over 1 TFlop/s and peak theoretical bandwidth of over 100 GiB/s. Owens et al. [3] provides a survey of algorithms using GPUs for general purpose computing.”
A survey of medical image registration on graphics hardware (2011, result): “[6] J.”
PHAST: Hardware-Accelerated Shortest Path Trees (2011, methodology): “Although aimed primarily at computer graphics application s, GPUs have increasingly been used to accelerate generalpurpose computations [17].”
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation (2009, methodology): “In the early days of GPU programming, the programmer had to repurpose marginally programmable ﬁxed-function graphics hardware for computing purposes by a variety of methods [36].”
GPU Computing (2008, background): “Our recent survey of the field (completed in November 2006) summarizes this age of GPU computing [1].”
High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster (2010, background): “To date, there have been a few finite-element implementations in CUDA, such as the volumetric finite-element method to support the interactive rates demanded by tissue cutting and suturing simulations during medical operations [51,52,7].”
Larrabee: a many-core x86 architecture for visual computing (2008, background): “Additionally, a number of arithmetically intensive workloads perform very well on GPU-like architectures [GPGPU 2007; Owens et al. 2007].”
Parallel metaheuristics: recent advances and new trends (2012, background): “The novel general purpose on GPU (GPGPU) paradigm extends this advantage to other applications areas (Owens et al., 2007).”
Relational joins on graphics processors (2008, background): “3 Prefix scan A prefix scan applies a binary operator to the input relation of size n and generates an output relation of size n [30].”
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA (2008, background): “We refer the reader to a review of the main body of work done to map general purpose computation to GPUs by Owens et al. in [21].”
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures (2011, methodology): “While GPGPUs were historically programmed using standard graphical APIs [14], AMD’s F IRE S TREAM and especially NVIDIA’s CUDA are by far the most common way to program GPUs nowadays.”