Archives/papers-we-love_papers-we-love
1
0
Fork 0
mirror of https://github.com/papers-we-love/papers-we-love.git synced 2026-03-02 03:40:31 +00:00
Code Issues Projects Releases Wiki Activity

added some literatures covering scientific data compression. (#569)

Browse Source 
* added some literatures covering scientific data compression.

* fixed the 📜 notation in readme file

* added the urls to the paper in readme file; added reasons for the paper in readme file.

* fixed the name of the paper in readme

Co-authored-by: Sean Broderick <hakutsuru@mac.com>
This commit is contained in:
Jon.W
2020-03-29 15:06:55 -04:00
committed by GitHub
parent 37dbc93c33
commit 68986bb4fd
9 changed files with 8418 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
data_compression/README.md
View File

@@ -2,4 +2,22 @@
* :scroll: [Data Compression](data-compression.pdf)
> This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano and Huffman in the 40's, to a technique developed in 1986.
> This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano and Huffman in the 40's, to a technique developed in 1986.
## Scientific Data Compression
* :scroll: [Fast Error-bounded Lossy HPC Data Compression with SZ](https://www.mcs.anl.gov/papers/P5437-1115.pdf)
> This is the first version of SZ. In this paper, SZ is introduced to achieve data reduction using regression-based data point prediction.
* :scroll: [Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization](https://arxiv.org/pdf/1706.03791.pdf)
> This work is known as SZ-1.4. In this work, SZ employs multi-dimensional data prediction so that data with dimension larger than 1 is no longer linearized into single dimension before compression. In this way, more data locality is preserved thus compression ratio is improved.
* :scroll: [Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets](https://www.mcs.anl.gov/~hguo/publications/LiangDTLLGCC18.pdf)
> This work is known as SZ-2.0. In this work, authors proposed an online selection tool between 2 predictors, the mean-integrated Lorenzo predictor and linear regression-based predictor. Users can choose the predictor that yields larger compression ratio with higher prediction accuracy.
* :scroll: [Fixed-Rate Compressed Floating-Point Arrays](http://vis.cs.ucdavis.edu/vis2014papers/TVCG/papers/2674_20tvcg12-lindstrom-2346458.pdf)
* :scroll: [FPC: A High-Speed Compressor for Double-Precision Floating-Point Data](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.388.2782&rep=rep1&type=pdf)