A method for speeding up beam-tracing simulation using thread-level parallelization

Marjan Sikora, Ivo Mateljan
University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, R. Boskovica 32, 21000 Split, Croatia
Engineering with Computers, 2013

   title={A method for speeding up beam-tracing simulation using thread-level parallelization},

   author={Sikora, Marjan and Mateljan, Ivo},

   journal={Engineering with Computers},





Download Download (PDF)   View View   Source Source   



In recent years, the computational power of modern processors has been increasing mainly because of the increase in the number of processor cores. Computationally intensive applications can gain from this trend only if they employ parallelism, such as thread-level parallelization. Geometric simulations can employ thread-level parallelization because the main part of a geometric simulation can be divided into a subset of mutually independent tasks. This approach is especially interesting for acoustic beam tracing because it is an intensive computing task. This paper presents the parallelization of an existing beam-tracing simulation composed of three algorithms. Two of them are iterative algorithms, and they are parallelized with an already known technique. The most novel method is the parallelization of the third algorithm, the recursive octree generation. To check the performance of the multi-threaded parallelization, several tests are performed using three different computer platforms. On all of the platforms, the multithreaded octree generation algorithm shows a significant speedup, which is linear when all of the threads are executed on the same processor.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

244 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1468 peoples are following HGPU @twitter

* * *

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: