Medusa: Simplified Graph Processing on GPUs

Jianlong Zhong, Bingsheng He
School of Computer Engineering, Nanyang Technological University, Singapore, 639798
Nanyang Technological University, Technical Report, 2012

   title={Medusa: Simplified Graph Processing on GPUs},

   author={Zhong, J. and He, B.},



Graphs are the de facto data structures for many applications, and efficient graph processing is a must for the application performance. Recently, the graphics processing unit (GPU) has been adopted to accelerate various graph processing algorithms such as BFS and shortest path. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To simplify graph processing on GPUs, we propose a programming framework called Medusa which enables developers to leverage the capabilities of GPUs by writing sequential C/C++ code. Medusa offers a small set of user-defined APIs, and embraces a runtime system to automatically execute those APIs in parallel on the GPU. We develop a series of graph-centric optimizations based on the architecture features of GPU for efficiency. Additionally, Medusa is extended to execute on multiple GPUs within a machine. Our experiments show that (1) Medusa greatly simplifies implementation of GPGPU programs for graph processing, with much fewer lines of source code written by developers; (2) The optimization techniques significantly improve the performance of the runtime system, making its performance comparable with or better than the manually tuned GPU graph operations.
VN:F [1.9.22_1171]
Rating: 5.0/5 (3 votes cast)
Medusa: Simplified Graph Processing on GPUs, 5.0 out of 5 based on 3 ratings

* * *

* * *

Follow us on Twitter

HGPU group

1666 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

338 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: