The use of new technologies, such as GPU boosters, have led to a dramatic
increase in the computing power of High-Performance Computing (HPC)
centres. This development, coupled with new climate models that can better
utilise this computing power thanks to software development and internal
design, led to the bottleneck moving from solving the differential equations
describing Earth’s atmospheric interactions to actually storing the variables.
The current approach to solving the storage problem is inadequate: either
the number of variables to be stored is limited or the temporal resolution
of the output is reduced. If it is subsequently determined that another vari-
able is required which has not been saved, the simulation must run again.
This thesis deals with the development of novel compression algorithms
for structured floating-point data such as climate data so that they can be
stored in full resolution.
Compression is performed by decorrelation and subsequent coding of
the data. The decorrelation step eliminates redundant information in the
data. During coding, the actual compression takes place and the data is
written to disk. A lossy compression algorithm additionally has an approx-
imation step to unify the data for better coding. The approximation step
reduces the complexity of the data for the subsequent coding, e.g. by using
quantification. This work makes a new scientific contribution to each of the
three steps described above.
This thesis presents a novel lossy compression method for time-series
data using an Auto Regressive Integrated Moving Average (ARIMA) model
to decorrelate the data. In addition, the concept of information spaces and
contexts is presented to use information across dimensions for decorrela-
tion. Furthermore, a new coding scheme is described which reduces the
weaknesses of the eXclusive-OR (XOR) difference calculation and achieves
a better compression factor than current lossless compression methods for
floating-point numbers. Finally, a modular framework is introduced that
allows the creation of user-defined compression algorithms.
The experiments presented in this thesis show that it is possible to in-
crease the information content of lossily compressed time-series data by
applying an adaptive compression technique which preserves selected data
with higher precision. An analysis for lossless compression of these time-
series has shown no success. However, the lossy ARIMA compression model
proposed here is able to capture all relevant information. The reconstructed
data can reproduce the time-series to such an extent that statistically rele-
vant information for the description of climate dynamics is preserved.
Experiments indicate that there is a significant dependence of the com-
pression factor on the selected traversal sequence and the underlying data
model. The influence of these structural dependencies on prediction-based
compression methods is investigated in this thesis. For this purpose, the
concept of Information Spaces (IS) is introduced. IS contributes to improv-
ing the predictions of the individual predictors by nearly 10% on average.
Perhaps more importantly, the standard deviation of compression results is
on average 20% lower. Using IS provides better predictions and consistent
compression results.
Furthermore, it is shown that shifting the prediction and true value leads
to a better compression factor with minimal additional computational costs.
This allows the use of more resource-efficient prediction algorithms to
achieve the same or better compression factor or higher throughput during
compression or decompression. The coding scheme proposed here achieves
a better compression factor than current state-of-the-art methods.
Finally, this paper presents a modular framework for the development
of compression algorithms. The framework supports the creation of user-
defined predictors and offers functionalities such as the execution of bench-
marks, the random subdivision of n-dimensional data, the quality evalua-
tion of predictors, the creation of ensemble predictors and the execution of
validity tests for sequential and parallel compression algorithms.
This research was initiated because of the needs of climate science, but
the application of its contributions is not limited to it. The results of this the-
sis are of major benefit to develop and improve any compression algorithm
for structured floating-point data
