Fixed length lightweight compression for GPU revised

Limitations of I/O bandwidth and latency are a serious burden for many data intensive algorithms. This is even more noticeable in distributed heterogeneous clusters that heavily utilize GPU processors, where communication costs and limitation of memory play crucial role. Lightweight lossless compression methods are often applied to cope with those limitations. In this paper we investigate new approaches to parallel lossless lightweight compression methods based on fixed-length minimum bit encoding for GPU processors. By developing new memory organization and by utilizing various inter thread and inter warp communication abilities, we have proposed algorithms which suit GPU architecture better. As a result we have significantly improved compression ratio and bandwidths. This allows for many new applications in computational clusters as well as in computational algorithms. Our claims are supported by tests conducted using simulated data and TPC-H database benchmarking tools.