10926

Adaptive implementation selection in the SkePU skeleton programming library

Usman Dastgeer, Lu Li, Christoph Kessler
IDA, Linkoping University, 58183 Linkoping, Sweden
Biennial Conference on Advanced Parallel Processing Technology (APPT-2013), 2013
@article{dastgeer2013adaptive,

   title={Adaptive implementation selection in the SkePU skeleton programming library},

   author={Dastgeer, Usman and Li, Lu and Kessler, Christoph},

   year={2013}

}

Download Download (PDF)   View View   Source Source   Source codes Source codes

Package:

334

views

In earlier work, we have developed the SkePU skeleton programming library for modern multicore systems equipped with one or more programmable GPUs. The library internally provides four types of implementations (implementation variants) for each skeleton: serial C++, OpenMP, CUDA and OpenCL targeting either CPU or GPU execution respectively. Deciding which implementation would run faster for a given skeleton call depends upon the computation, problem size(s), system architecture and data locality. In this paper, we present our work on automatic selection between these implementation variants by an offline machine learning method which generates a compact decision tree with low training overhead. The proposed selection mechanism is flexible yet high-level allowing a skeleton programmer to control different training choices at a higher abstraction level.We have evaluated our optimization strategy with 9 applications/kernels ported to our skeleton library and achieve on average more than 94% (90%) accuracy with just 0.53% (0.58%) training space exploration on two systems. Moreover, we discuss one application scenario where local optimization considering a single skeleton call can prove sub-optimal, and propose a heuristic for bulk implementation selection considering more than one skeleton call to address such application scenarios.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

168 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1275 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: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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 © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: