Fast lossless compression of scientific floating-point data

By Paruj Ratanaworabhan, Jian Ke, Martin Burtscher. 2006

184 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jonathan Frankle
2. Sheng Di
3. Franck Cappello
4. John V. Guttag
5. Davis W. Blalock
6. Jose Javier Gonzalez Ortiz
7. Zizhong Chen
8. Dingwen Tao

In Computer Networks and Communications

1. Sheng Di
2. Franck Cappello
3. Zizhong Chen
4. Dingwen Tao
5. Peter Lindstrom

In Hardware and Architecture

1. Peter Lindstrom
2. Franck Cappello

Citations in the corpus

• What is the State of Neural Network Pruning? (2020, background): “…literature has long used to mean original sizecompressed size (Siedelmann et al., 2015; Zukowski et al., 2006; Zhao et al., 2015; Lindstrom, 2014; Ratanaworabhan et al., 2006; Blalock et al., 2018), but many pruning authors define (usually without making the formula explicit) as 1− compressed…”
• Gorilla: A Fast, Scalable, In-Memory Time Series Database (2015, methodology): “This scheme leveraged XOR comparison with previous values to generate a delta encoding [25, 17].”
• Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization (2017, background): “As we mentioned in Section I, some lossless compressors can provide a compression factor up to 2 [15].”
• Fixed-Rate Compressed Floating-Point Arrays (2014, methodology): “The methods by Burtscher and co-workers [5, 39] are notable in that they rely on hash functions to extract non-linear relationships.”