A general purpose lossless data compression method for GPU

• We proposed a novel approach to parallel lossless data compression on GPU.
• The efficiency of the approach is based on parallelization of data processing on different hardware and logical levels.
• We obtained better compression speed than popular data compression programs used in PC (compression ratio is sustained).
• Our approach outperforms other known GPU based methods to lossless data compression.

The paper describes a parallel method for a lossless data compression that uses graphical processing units (GPUs). Two commonly used statistical and dictionary approaches to data compression have been applied in our method. The reduction of compression time was possible due to the implementation of multi level parallel methods that use a single GPU or a set of GPUs efficiently. The base of our method is a search for repetitions in data that is executed in parallel with the use of sorted suffix tables. On the second level of concurrency operations on different data blocks: data file reading, match search, coding, compression and data file writing are performed in parallel. The methods proposed, supplying a comparable compression ratio, achieve a better compression speed than a standard CPU-based compression tools used in personal computers. Experiments performed in technologically comparable systems showed that our approach is similar or even better in terms of power and cost efficiency.