A general purpose lossless data compression method for GPU-Reference-Cited by-同舟云学术

A general purpose lossless data compression method for GPU

Published:2015-01 Issue: Volume:75 Page:40-52
ISSN:0743-7315
Container-title:Journal of Parallel and Distributed Computing
language:en
Short-container-title:Journal of Parallel and Distributed Computing

Author:

Chłopkowski Marek,Walkowiak Rafał

Funder

National Science Centre, Poland

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference20 articles.

1. J. Abel, www.data-compression.info: The data compression resource on the Internet, December 2009. www.data-compression.info [on-line].

2. A. Aqrawi, A. Elster, Accelerating disk access using compression for large seismicdatasets on modern GPU and CPU. Extended Abstract No 131, Para 2010 State of the Art in Scientific and Parallel Computing, Reykjavik, June 2010.

3. Parallel variable-length encoding on GPGPUs;Balevic,2010

4. I. Castano, High quality DXT compression using OpenCL for CUDA. Whitepaper, March 2009. developer.download.nvidia.com/compute/cuda/3_0/sdk/website/OpenCL/website/OpenCL/src/oclDXTCompression/doc/opencl_dxtc.pdf [on-line].

5. Compression ratings, December 2009. http://compressionratings.com/ [on-line].

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