Techniques for designing GPGPU games

Mark Joselli, Jose Ricardo da Silva Junior, Marcelo Zamith, Esteban Clua, Mateus Pelegrino, Evandro Mendonca, Eduardo Soluri
MediaLab-IC/UFF, Universidade Federal Fluminense
IEEE International Games Innovation Conference (IGIC), 2012

   title={Techniques for designing GPGPU games},

   author={Joselli, M. and Ricardo da Silva, J. and Zamith, M. and Clua, E. and Pelegrino, M. and Mendonca, E.},

   booktitle={Games Innovation Conference (IGIC), 2012 IEEE International},





Download Download (PDF)   View View   Source Source   



The increasing level of realism in digital games depends not only on the enhancement of modeling and rendering effects, but also on the improvement of different aspects such as animation, characters artificial intelligence and physics simulation. Normally, games process most of their tasks in the CPU, using the GPU only for graphics processing. Several games and previous works also use GPU computing to process some selected non-graphics subtasks (normally the game physics), while the remaining tasks remain on the CPU. Such a processing partition is frequently pointed to as the bottleneck in these games, as it may induce several expensive data transfers between the CPU and GPU. This paper shows some game examples that execute all their methods entirely on the GPU using of a new GPU computing architecture for keeping the GPU-CPU communication to a minimum. Using the techniques explained in this paper, we present a game that can have an average optimization of about 25% when compared to traditional GPGPU game programing (i.e. only using GPGPU for physics and processing the AI in the CPU) and 200% when compared to traditional CPU programming.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1666 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

339 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: