Video coding on multicore graphics processors (GPUs)

Bruno Alexandre de Medeiros
Instituto Superior Tecnico, Universidade Tecnica de Lisboa
Universidade Tecnica de Lisboa, 2012

   title={Video coding on multicore graphics processors (GPUs)},

   author={de Medeiros, B.A.},



Download Download (PDF)   View View   Source Source   



H.264/AVC is a recent video standard embraced by many multimedia applications. Because of its demanding encoding requirements, a high amount of computational effort is often needed in order to compress a video stream in real time. The intra-prediction and encoding are two of several modules included by H.264 that requires a high computational power. On the other hand, the GPU computational capabilities are exhibiting their supremacy for solving certain types of problems with data parallelism, which composes the majority of the intra-prediction and encoding process. This dissertation presents a parallel implementation of the intra-prediction and encoding modules by adopting a parallel programming API denoted by CUDA, which explores the massive parallelization capabilities of recent NVIDIA graphic cards in order to reduce the encoding time. The developed solution is integrated in an existing encoder, where the intra-prediction and the respective encoding are processed sequentially. Through the result of several conducted tests it is demonstrated that the developed module is capable to speed up the sequential execution beyond the computing capabilities of a recent CPU.
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: