Parallel external sorting for CUDA-enabled GPUs with load balancing and low transfer overhead

Hagen Peters, Ole Schulz-Hildebrandt, Norbert Luttenberger
Research Group for Communication Systems, Department of Computer Science, Christian-Albrechts-University Kiel, Germany
In 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW) (April 2010), pp. 1-8.

   title={Parallel external sorting for CUDA-enabled GPUs with load balancing and low transfer overhead},

   author={Peters, H. and Schulz-Hildebrandt, O. and Luttenberger, N.},

   booktitle={Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on},





Download Download (PDF)   View View   Source Source   



Sorting is a well-investigated topic in Computer Science in general and by now many efficient sorting algorithms for CPUs and GPUs have been developed. There is no swapping, paging, etc. available on GPUs to provide more virtual memory than physically available, thus if one wants to sort sequences that exceed GPU memory using the GPU the problem of external sorting arises. In this contribution we present a novel merge-based external sorting algorithm for one or more CUDA-enabled GPUs. We reduce the performance impact of memory transfers to and from the GPU by using an approach similar to regular samplesort and by overlapping memory transfers with GPU computation. We achieve a good utilization of GPUs and load balancing among them by carefully choosing the samples and the amount of GPU memory used for computation. We demonstrate the performance of our algorithm by extended testing. Using two GTX280 the implementation outperforms the fastest CPU sorting algorithms known to the authors.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1655 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 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: