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## Scientific Data Compression
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## Scientific Data Compression
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* :scroll: [Fast Error-bounded Lossy HPC Data Compression with SZ](https://www.mcs.anl.gov/papers/P5437-1115.pdf)
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* :scroll: [Fast Error-bounded Lossy HPC Data Compression with SZ](fast_error_bounded_Lossy_hpc_data_compression_with_sz.pdf)
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> This is the first version of SZ. In this paper, SZ is introduced to achieve data reduction using regression-based data point prediction.
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> This is the first version of SZ. In this paper, SZ is introduced to achieve data reduction using regression-based data point prediction.
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* :scroll: [Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization](https://arxiv.org/pdf/1706.03791.pdf)
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* :scroll: [Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization](Significantly_Improving_Lossy_Compression_for_Scientific_Data_Sets_Based_on_Multidimensional_Prediction_and_Error-Controlled_Quantization.pdf)
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> 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.
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> 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.
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* :scroll: [Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets](https://www.mcs.anl.gov/~hguo/publications/LiangDTLLGCC18.pdf)
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* :scroll: [Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets](Error-Controlled_Lossy_Compression_Optimized_for_High_Compression_Ratios_of_Scientific_Datasets.pdf)
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> 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.
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> 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.
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* :scroll: [Fixed-Rate Compressed Floating-Point Arrays](http://vis.cs.ucdavis.edu/vis2014papers/TVCG/papers/2674_20tvcg12-lindstrom-2346458.pdf)
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* :scroll: [Fixed-Rate Compressed Floating-Point Arrays](fixed-rate_compressed_floating_point_arrays.pdf)
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* :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)
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* :scroll: [FPC: A High-Speed Compressor for Double-Precision Floating-Point Data](fpc_a_high_speed_compressor_for_double_precision_floating_point_data.pdf)
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