9415

Implicit Adaptive Volume Ray Casting

Rohit Nigam, Surinder Sood
International Institute of Information Technology, Hyderabad
International Institute of Information Technology, 2013
@article{nigam2013implicit,

   title={Implicit Adaptive Volume Ray Casting},

   author={Nigam, Rohit and Sood, Surinder},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

383

views

Ray Casting is an important visual application, used to visualize 3D datasets, such as CT data used in medical imaging. High quality image generation algorithms, known as ray casting, cast rays through the volume, performing compositing of each voxel into a corresponding pixel, based on voxel opacity and color. Since all rays perform the computations independently, the problem is very much portable for parallel architectures. Tracing multiple rays using SIMD is challenging, because rays can access non-contiguous memory locations, resulting in incoherent and irregular memory accesses. The aim of this project is to first develop optimized algorithms for both CPU and GPU implementations and then move on to a hybrid version. We wish to demonstrate that a hybrid implementation is much faster than either a CPU or a GPU implementation.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1496 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

252 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: