Enhancing Data Migration Performance via Parallel Data Compression
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Time-varying, multivariate volume data reduction
Proceedings of the 2005 ACM symposium on Applied computing
Fast and Efficient Compression of Floating-Point Data
IEEE Transactions on Visualization and Computer Graphics
JPEG2000 compatible lossless coding of floating-point data
Journal on Image and Video Processing
MEMMU: Memory expansion for MMU-less embedded systems
ACM Transactions on Embedded Computing Systems (TECS)
A method of adaptive coarsening for compressing scientific datasets
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
RDIF a preprocessing filter for HDF5
AMERICAN-MATH'10 Proceedings of the 2010 American conference on Applied mathematics
Fast and effective lossy compression algorithms for scientific datasets
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
ARC'13 Proceedings of the 9th international conference on Reconfigurable Computing: architectures, tools, and applications
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Applications in scientific computing operate with high-volume numerical data and the occupied space should be reduced. Traditional compression algorithms cannot provide sufficient compression ratio for such kind of data.We propose a lossless algorithm of delta-compression (a variant of predictive coding) that packs the higher-order differences between adjacent data elements. The algorithm takes into account varying domain (typically, time) steps. The algorithm is simple, it has high performance and delivers high compression ratio for smoothly changing data. Both lossless and lossy variants of the algorithm can be used. The algorithm has been successfully applied to the output from a simulation application that uses a solver of ordinary differential equations.