Simulation of cloud dynamics on graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics
The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics
Hardware-aware analysis and optimization of stable fluids
Proceedings of the 2008 symposium on Interactive 3D graphics and games
Using GPUs to improve multigrid solver performance on a cluster
International Journal of Computational Science and Engineering
Roofline: an insightful visual performance model for multicore architectures
Communications of the ACM - A Direct Path to Dependable Software
Ypnos: declarative, parallel structured grid programming
Proceedings of the 5th ACM SIGPLAN workshop on Declarative aspects of multicore programming
Parallel multiclass classification using SVMs on GPUs
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Stochastic path tracing on consumer graphics cards
Proceedings of the 24th Spring Conference on Computer Graphics
Considering GPGPU for HPC centers: is it worth the effort?
Facing the multicore-challenge
Mind the gap!: bridging the dichotomy of design and implementation
Proceedings of the 4th International Workshop on Software Engineering for Computational Science and Engineering
Considering GPGPU for HPC centers: is it worth the effort?
Facing the multicore-challenge
Spiking neural P system simulations on a high performance GPU platform
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
A spiking neural p system simulator based on CUDA
CMC'11 Proceedings of the 12th international conference on Membrane Computing
Real-time adaptive blur for reducing eye strain in stereoscopic displays
ACM Transactions on Applied Perception (TAP)
Population dynamics p systems on CUDA
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
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Recently, graphics processors have emerged as a powerful computational platform. A variety of encouraging results, mostly from researchers using GPUs to accelerate scientific computing and visualization applications, have shown that significant speedups can be achieved by applying GPUs to data-parallel computational problems. However, attaining these speedups requires knowledge of GPU programming and architecture.The preceding chapters have described the architecture of modern GPUs and the trends that govern their performance and design. Continuing from the concepts introduced in those chapters, in this chapter we present intuitive mappings of standard computational concepts onto the special-purpose features of GPUs. After presenting the basics, we introduce a simple GPU programming framework and demonstrate the use of the framework in a short sample program.