PARALLEL COMPUTING MODEL WITH CONTINUOUS TIME

The aim of this work is to construct a numerical-analytical method of designing efficient algorithms for solution of tasks having the parabolic type. Using a priori information about the smoothness of solutions, great attention is paid to the construction of solutions of high -order accuracy. Creation of parallel computing systems required the development of mathematical concepts for constructing parallel algorithms, i.e. algorithms adapted for implementation in these systems. As the basis for constructing the parallel algorithm we can take both: a sequential algorithm and the task itself as well. The most sensible at parallelization of sequential algorithm is pragmatic approach; actually sequential algorithms detect common elements which further are transformed to a parallel form. It is shown, that the algorithm of numerical - analytical vectorization has the maximal parallel form and, hence, minimally possible time for realization on parallel computing devices.

MODELING OF MAXIMALLY PARALLEL STRUCTURES OF ALGORITHMS FOR SOLVING THERMAL PROBLEMS

The paper demonstrates the possibility of creating a maximum parallel form of computational algorithms to solve thermal problems and their mapping to the architecture of multiprocessor systems based on solving thermal problems of mathematical physics. It is shown that an effective tool for studying heat and mass transfer problems in metallurgical production could be parallel computing technologies on distributed cluster systems with a relatively low cost and reasonably easily scalable both in the number of processors and in the amount of RAM. Tridiagonal structure systems' parallelization was implemented by a numerical-analytical approach, which predetermined their maximally parallel algorithmic form. That approach is facilitated by the minimum possible implementation time of the developed algorithm on parallel computing systems. Furthermore, during the arithmetic expressions parallel computations, the developed algorithm separates the error in the output data from rounding operations. Thus, the parallelization of tridiagonal systems based on numerical-analytical discretization methods does not impose any restrictions on the topology of the mesh nodes of the computational domain.Furthermore, as applied to the parallel computation of arithmetic expressions, it separates the initial data error from a real PC's rounding operations. That approach eliminates the recurrent structure of computing the sought-for decision vectors, which, as a rule, leads to the round-off errors accumulation. Such a parallel form of the constructed algorithm is maximal and has the shortest possible implementation time of the algorithm on parallel computing systems. The developed approach to parallelizing the mathematical model is stable for various types of input data. It has the most parallel form and is distinguished by the minimum time for solving the problem as applied to multiprocessor computing systems. That is explained as follows. If it is hypothesized that one processor can be assigned to one processor and one processor can be assigned to one node of the computational mesh domain, the computations can be processed in parallel and simultaneously for all nodes of the computational mesh domain. The simulation process was implemented on a PC cluster. It follows from the simulation results analysis that the developed method for solving the heat conduction problem effectively minimizes residuals.

Turkish version of the ‘Three-Factor Eating Questionnaire-51’ for obese individuals: a validity and reliability study

Objectives: Obesity is a serious public health issue. Investigating the eating behaviour of individuals plays an important role in preventing obesity. Therefore, the purpose of the current study is to adapt the long and first version of the ‘Three-Factor Eating Questionnaire’ (TFEQ), a scale that examines the eating behaviour of individuals, to Turkish culture and to carry out its validity and reliability study. Design: The data were collected using data collection forms, and anthropometric measurements of the individuals were made by the researchers. The data collection form included several parameters: socio-demographic characteristics, the TFEQ scale, whose validity and reliability analysis is conducted here, and the Dutch Eating Behaviour Questionnaire (DEBQ) which was used as a parallel form. Setting: The Obesity Clinic at Ege University in Izmir. Participants: The study group consisted of obese adult individuals (n 257). Results: It was seen that constructing the questionnaire with twenty-seven items and four sub-dimensions provides better information about Turkish obese individuals. Factor loadings ranged from 0·421 to 0·846, and item total score correlations ranged from 0·214 to 0·558. Cronbach’s α coefficient was found to be 0·639 for the whole scale. A positive, strong and statistically significant correlation was detected between TFEQ and DEBQ, which was used as a parallel form (r = 0·519, P < 0·001). Conclusion: In Turkey, the long version of the TFEQ scale was found valid and reliable for obese adult individuals. TFEQ can be used by clinicians or researchers to study the eating behaviour of obese individuals.

Erosion by Standardisation

This chapter examines the interplay between the GDPR and parallel private regulation in the form of privacy-related standards adopted by the International Organisation for Standardisation (ISO). Focusing on the understanding of ‘risks' in the GDPR and ISO respective ecosystems, it compares the GDPR requirement for Data Protection Impact Assessments (DPIAs) with ISO/IEC 29134:2017, a private standard on Privacy Impact Assessment explicitly referred to by EU Data Protection Authorities as relevant in the context of DPIA methods. The resulting gap analysis identifies and maps misalignments, critically reflecting on whether the parallel form of ISO regulation, in the context of DPIAs, could support or rather blurs GDPR's objective to protect fundamental rights by embracing a risks-based approach.

Implementing Kalman Filter Algorithm in Parallel Form: Denoising Sound Wave as a Case Study

Introduction: Signal filters were originally seen as circuits or systems with frequency selecting behaviors. The development of filtering techniques went on and more sophisticated filters were introduced, such as e.g. Chebychev and Butterworth filters, which gave means of shaping the frequency characteristics of the filter in a more systematic design procedure. During this stage, the filtering was mainly considered from this frequency. Mehtod: In (SIMD) model, a parallel computer consists of N identical processors, each of the N processors possesses its own local memory where it can store both programs and data, and all processors operate under the control of a single instruction stream issued by a central control unit. Equivalently, the N processors may be assumed to hold identical copies of a single program, each processor's copy being stored in its local memory. There are N data streams, one per processor. Result: It can be seen that the computation time decreases when we increase the number of cores from 2 to 12 cores shows that Kalman Filter can achieve nearly linear speed-up by increasing the number of cores, both results are illustrated consecutively. Conclusion: Parallel multicore implementation of the Kalman filter is studied. The implementation based on SIMD model which we splitting all signal points into large segments of data and applying equations on each segment simultaneously. Discussion: Through implementing the algorithm in parallel form on the noisy sound wave signals as a case study, it is found that the proposed parallel algorithm executes about twice as fast on double cores as the sequential form on a single core, it enhances the execution time to 44.86%. And is capable of achieving linear speedup in the number of cores used.