Data Encoding in Lossless Prediction-Based Compression Algorithms.

The increase in compute power and development of sophisticated simulation models with higher resolution output triggers a need for compression algorithms for scientific data. Several compression algorithms are currently under development. Most of these algorithms are using prediction-based compression algorithms, where each value is predicted and the residual between the prediction and true value is saved on disk. Currently there are two established forms of residual calculation: Exclusive-or and numerical difference. In this paper we will summarize both techniques and show their strengths and weaknesses. We will show that shifting the prediction and true value to a binary number with certain properties results in a better compression factor with minimal additional computational costs. This gain in compression factor allows for the usage of less sophisticated prediction algorithms to achieve a higher throughput during compression and decompression. In addition, we will introduce a new encoding scheme to achieve an 9% increase in compression factor on average compared to the current state-of-the-art.