Abstract The slope calculation algorithm is one of the most widely used geospatial algorithms employing the 3x3 moving window technique (along with calculation of aspect, curvature and flow direction). This work presents an approach consisting of transforming a slope algorithm from a sequential form into a version that can exploit vector and parallel traits of multicore architectures with vector instructions. This approach allows us to take advantage of the potential of the modern multicore processors. The basic idea for optimizing the 3x3 moving window computation is to split the equation used to calculate the result into parts that operate on data that are known to exist in adjacent memory locations. The research was conducted on two multicore architectures without the change in the code — the older architecture was Sandy Bridge and the newer one was Haswell (with more cores). The efficiency of the developed slope algorithm was verified in practice with the use of DEM files of the same resolution but of different sizes. We showed through the numerical experiments that our approach gives better time performance than the original algorithm (and other tools) — and with no loss of accuracy.

Algebraic Constructions of Extremal Graph Theory were efficiently used for the construction of Low Density Parity Check Codes for satellite communication, constructions of stream ciphers and Postquantum Protocols of Noncommutative cryptography and corresponding El Gamal type cryptosystems.We shortly observe some results in these applications and present idea of the usage of algebraic graphs for the development of Multivariate Public Keys (MPK).Some MPK schemes are presented at theoretical level, implementation of one of them is discussed.Extended version of this article is available online at [31].

Calculation of land-surface parameters (e.g.slope, aspect, curvature) is an important part of many geospatial analyses.Current research trends are aimed at developing new software techniques to achieve the best performance and energy trade-off.In our work, we concentrate on the vectorization and parallelization to improve overall energy efficiency and performance of the neighborhood raster algorithms for the computation of land-surface parameters.We chose the slope calculation algorithm as the basis for our investigation.The parallelization was achieved through redesigning the the original sequential code with OpenMP SIMD vectorization hints for compiler, OpenMP loop parallelization, and the hybrid of these techniques.To evaluate both performance and energy savings, we tested our vector-parallel implementations on a multi-core computer for various data sizes.RAPL interface was used to measure energy consumption.The results showed that optimization towards high performance can also be an effective strategy for improving energy efficiency.