Setting an integrated soil monitoring system for Malta : strategy, feasibility and recommendations

Chapter 6Since 2010, MEPA has embarked on a project (which attracted co-funded ERDF assistance) (1) to develop a multi-thematic environment strategy that would lead to updating of its data/ information monitoring capabilities for a number of environmental sectors. The monitoring and continuous evaluation of soil properties is one important sector within this project. Essentially, a multi-criterion assessment of existing available information has been carried out with a view to objectively chart the most appropriate process to carry out a pilot field sampling by testing a pre-agreed set of indicators. The latter were established after taking into consideration all degradation pressures threatening the continued sustainability of this resource. Multi-criterion analysis was carried out by means of a limited set of soil-related datasets published in past editions of Malta’s State of the Environment Report in order to support a number of objectives stipulated within the Project’s ambitious Terms of Reference. Information was derived from earlier attempts to establish a soil information system for Malta. All soil degradation threats, officially determined by the European Commission’s Technical Working Groups, have been taken into consideration within the aforementioned project and its research methodology with a view of establishing a shared GIS environment in accordance with state-of-the-art information dissemination standards.peer-reviewe

An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling

We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination, and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factorization leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK -- STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices