Big data, modeling, simulation, computational platform and holistic approaches for the fourth industrial revolution

Naturally, the mathematical process starts from proving the existence and uniqueness of the solution by the using the theorem, corollary, lemma, proposition, dealing with the simple and non-complex model. Proving the existence and uniqueness solution are guaranteed by governing the infinite amount of solutions and limited to the implementation of a small-scale simulation on a single desktop CPU. Accuracy, consistency and stability were easily controlled by a small data scale. However, the fourth industrial can be described the mathematical process as the advent of cyber-physical systems involving entirely new capabilities for researcher and machines (Xing, 2017). In numerical perspective, the fourth industrial revolution (4iR) required the transition from a uncomplex model and small scale simulation to complex model and big data for visualizing the real-world application in digital dialectical and exciting opportunity. Thus, a big data analytics and its classification are a problem solving for these limitations. Some applications of 4iR will highlight the extension version in terms of models, derivative and discretization, dimension of space and time, behavior of initial and boundary conditions, grid generation, data extraction, numerical method and image processing with high resolution feature in numerical perspective. In statistics, a big data depends on data growth however, from numerical perspective, a few classification strategies will be investigated deals with the specific classifier tool. This paper will investigate the conceptual framework for a big data classification, governing the mathematical modeling, selecting the superior numerical method, handling the large sparse simulation and investigating the parallel computing on high performance computing (HPC) platform. The conceptual framework will benefit to the big data provider, algorithm provider and system analyzer to classify and recommend the specific strategy for generating, handling and analyzing the big data. All the perspectives take a holistic view of technology. Current research, the particular conceptual framework will be described in holistic terms. 4iR has ability to take a holistic approach to explain an important of big data, complex modeling, large sparse simulation and high performance computing platform. Numerical analysis and parallel performance evaluation are the indicators for performance investigation of the classification strategy. This research will benefit to obtain an accurate decision, predictions and trending practice on how to obtain the approximation solution for science and engineering applications. As a conclusion, classification strategies for generating a fine granular mesh, identifying the root causes of failures and issues in real time solution. Furthermore, the big data-driven and data transfer evolution towards high speed of technology transfer to boost the economic and social development for the 4iR (Xing, 2017; Marwala et al., 2017)

Efficient Implicit Parallel Patterns for Geographic Information System

International audienceWith the data growth, the need to parallelize treatments become crucial in numerous domains. But for non-specialists it is still difficult to tackle parallelism technicalities as data distribution, communications or load balancing. For the geoscience domain we propose a solution based on implicit parallel patterns. These patterns are abstract models for a class of algorithms which can be customized and automatically transformed in a parallel execution. In this paper, we describe a pattern for stencil computation and a novel pattern dealing with computation following a pre-defined order. They are particularly used in geosciences and we illustrate them with the flow direction and the flow accumulation computations

The SIPSim implicit parallelism model and the SkelGIS library

International audienceScientific simulations give rise to complex codes where data size and computation time become very important issues, and sometimes a scientific barrier. Thus, parallelization of scientific simulations becomes a significant work. Many time and human efforts are deployed to produce efficient parallel programs. But still, many simulations could not be parallelized because of lack of time to learn parallel programming or lack of human resources. Therefore, aiding parallelization through abstracted parallelism or implicit parallelism has become a main topic in computer science. Many implicit parallelism solutions have been proposed such as algorithmic skeletons libraries, domain-specific languages or specific libraries. In this paper is introduced a new type of solution to give a totally transparent access to parallel programming for non-computer scientists of the domain of numerical simulations. This solution is an implicit parallelism model, called Structured Implicit Parallelism on scientific Simulations (SIPSim). After a description of the SIPSim model, this paper presents the implementation of the model, as a C++ templated library called SkelGIS, for two different cases of simulations: simulations on Cartesian meshes and simulations of two physical phenomena linked througha network. For each case, the implementation of the SIPSim components are described, and a simple simulation example is given. SkelGIS is then evaluated on two real cases, one for each case, first on the resolution of shallow water equations and second on an arterial blood flow simulation. To clearly state on SkelGIS performance and its ease of programming, different experiments on both cases are evaluated

A methodology for developing scientific software applications in science gateways

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDistributed Computing Infrastructures (DCIs) have emerged as a viable and affordable solution to the computing needs of communities of practice that may require the need to improve system performance or enhance the availability of their scientific applications. According to the literature, the ease of access and several other issues which relate to the interoperability among different resources are the biggest challenges surrounding the use of these infrastructures. The traditional method of using a Command Line Interface (CLI) to access these resources is difficult and can make the learning curve quite steep. This approach can result in the low uptake of DCIs as it prevents potential users of the infrastructures from adopting the technology. Science Gateways have emerged as a viable option that are used to realise the high-level scientific domain-specific user interfaces that hide all the details of the underlying infrastructures and expose only the science-specific aspects of the scientific applications to be executed in the various DCIs. A Science Gateway is a digital interface to advanced technologies which is used to provide adequate support for science and engineering research and education. The focus of this study therefore is to propose and implement a Methodology for dEveloping Scientific Software Applications in science GatEways (MESSAGE). This will be achieved by testing an approach which is considered to be appropriate for developing applications in Science Gateways. In the course of this study, several Science Gateway functionalities obtained from the review of literature which may be utilised to provide services for different communities of practice are highlighted. To implement the identified functionalities, this study utilises the methodology for developing scientific software applications in Science Gateways. In order to achieve this purpose, this research therefore adopts the Catania Science Gateway Framework (CSGF) and the Future Gateway approach to implement the methods and ideas described in the proposed methodology, as well the essential services of Science Gateways discussed throughout the thesis. In addition, three different set of scientific software applications are utilised for the implementation of the proposed methodology. While the first application primarily serves as the case study for implementing the methodology discussed in this thesis, a second application is used to evaluate the entire process. Furthermore, several other real-life scientific applications developed (using two distinctly different Science Gateway frameworks) are also utilised for the purpose of evaluation. Subsequently, a revised MESSAGE methodology for developing scientific software applications in Science Gateways is discussed in the latter Chapter of this thesis. Following from the implementation of both scientific software applications which sees the use of portlets to execute single experiments, a study was also conducted to investigate ways in which Science Gateways may be utilised for the execution of multiple experiments in a distributed environment. Finally, similar to making different scientific software applications accessible and available (worldwide) to the communities that need them, the processes involved in making their associated research outputs (such as data, software and results) easily accessible and readily available are also discussed. The main contribution of this thesis is the MESSAGE methodology for developing scientific software applications in Science Gateways. Other contributions which are also made in different aspects of this research include a framework of the essential services required in generic Science Gateways and an approach to developing and executing multiple experiments (via Science Gateway interfaces) within a distributed environment. To a lesser extent, this study also utilises the Open Access Document Repository (OADR) (and other related technologies) to demonstrate accessibility and availability of research outputs associated with specific scientific software applications, thereby introducing the concept (and thus laying the foundation) of an Open Science research