10916

GPGPU-accelerated Interesting Interval Discovery and other Computations on GeoSpatial Datasets – A Summary of Results

Sushil K. Prasad, Shashi Shekhar, Michael McDermott, Xun Zhou, Michael Evans, Satish Puri
Georgia State University, Atlanta, GA
2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial-2013), 2013
@article{prasad2013gpgpu,

   title={GPGPU-accelerated Interesting Interval Discovery and other Computations on GeoSpatial Datasets–A Summary of Results},

   author={Prasad, Sushil K and Shekhar, Shashi and McDermott, Michael and Zhou, Xun and Evans, Michael and Puri, Satish},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

339

views

It is imperative that for scalable solutions of GIS computations the modern hybrid architecture comprising a CPUGPU pair is exploited fully. The existing parallel algorithms and data structures port reasonably well to multicore CPUs, but poorly to GPGPUs because of latter’s atypical fine-grained, single-instruction multiple-thread (SIMT) architecture, extreme memory hierarchy and coalesced access requirements, and delicate CPU-GPU coordination. Recently, our parallelization of the state-of-art interesting sequence discovery algorithms calculates one-dimensional interesting intervals over an image representing the normalized difference vegetation indices of Africa within 31 ms on an nVidia 480GTX. To our knowledge, this paper reports the first parallelization of these algorithms. This allowed us to process 612 images representing biweekly data from July 1981 through Dec 2006 within 22 seconds. We were also able to pipe the output to a display in almost real-time, which would interest climate scientists. We have also undertaken parallelization of two key tree-based data structures, namely R-tree and heap, and have employed parallel R-tree in polygon overlay system. These data structure parallelization are hard because of the underlying tree topology and the fine-grained computation leading to frequent access to such data structures severely stifling parallel efficiency.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

192 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1329 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: