An Optimal Offline Permutation Algorithm on the Hierarchical Memory Machine, with the GPU implementation

Akihiko Kasagi, Koji Nakano, and Yasuaki Ito
Department of Information Engineering, Hiroshima University, Kagamiyama 1-4-1, Higashi Hiroshima, 739-8527 Japan
International Conference on Parallel Processing (ICPP), 2013

   title={An Optimal Offline Permutation Algorithm on the Hierarchical Memory Machine, with the GPU implementation},

   author={Kasagi, Akihiko and Nakano, Koji and Ito, Yasuaki},

   booktitle={Proc. of International Conference on Parallel Processing},



Download Download (PDF)   View View   Source Source   



The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of computation on CUDA-enabled GPUs. The offline permutation is a task to copy numbers stored in an array a of size n to an array b of the same size along a permutation P given in advance. A conventional algorithm can complete the offline permutation by executing b[p[i]]<-a[i] for all i in parallel, where an array p stores the permutation P. This conventional algorithm simply performs three rounds of memory access for reading from a, reading from p, and writing in b. The main contribution of this paper is to present an optimal offline permutation algorithm running in O(n/w+L) time units using n threads on the HMM with width w and latency L. We also implement our optimal offline permutation algorithm on GeForce GTX-680 GPU and evaluate the performance. Quite surprisingly, our optimal offline permutation algorithm achieves better performance than the conventional algorithm in most permutations, although it performs 32 rounds of memory access. For example, the bit-reversal permutation for 4M float (32-bit) numbers can be completed in 780ms by our optimal permutation algorithm, while the conventional algorithm takes 2328ms. We can say that the experimental results of this paper provide a good example of GPU computation showing that a complicated but ingenious implementation with a larger constant factor in computing time can outperform a much simpler conventional algorithm.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1662 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

337 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: