Graphics processing unit (GPU) programming strategies and trends in GPU computing
By André Rigland Brodtkorb, Trond Runar Hagen, M. L. Sætra. J. Parallel Distributed Comput., 2013
299 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Hardware and Architecture, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/0d8bed4acf7d94a3cf4ea9a57ae0effeae76d408
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Hardware and Architecture
Citations in the corpus
- Computational Physics on Graphics Processing Units (2012, background): “[11], while, for the less eager to get their hands dirty, a review of available MD software packages is included in Ref.”
- The hardware lottery (2020, background): “GPUs had one critical advantage over CPUs - they were far better at parallelizing a set of simple decomposable instructions such as matrix multiples (Brodtkorb et al., 2013; Dettmers, 2020).”