Applying Genetic Algorithms to Tune Heterogeneous Platform Configurations

Max Grossman, Sameer Kulkarni
Rice University
The International Conference on Parallel, Distributed and Grid Computing (PDGC), 2012

   title={Applying Genetic Algorithms to Tune Heterogeneous Platform Configurations},

   author={Grossman, M. and Kulkarni, S.},



Download Download (PDF)   View View   Source Source   



Present need to move towards heterogeneous architectures has been well established. This has increased the importance of parallelization of software to achieve good performance. Use of mixed architectures exponentially increases the need of the programmer to understand the intricacies of the underlying hardware to achieve optimal speedup. Obtaining optimal performance on one such architecture is difficult, but this process needs to be repeated for even small changes in the architecture or the source code. In these situations use of autotuning [1], [2] can not only reduce the amount of effort in trying to manually figure out the best optimization, but could also find configurations that are better than what was achieved by an expert on that architecture. We apply this machine learning technique on two Medical imagining applications. Like many other computationally intensive imaging applications these applications are best suited to running on inherently parallel architectures. We compare our results with a past study of the same applications where we modified the applications to run best on the given architectures. We achieved speedups of up to 159.8% improvement on an individual kernel basis, and 39.7% for overall application performance.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1665 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: