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-Systems , 10 (1), 37–40. 11. MPI A Message-Passing Internet Standard, from http://www.mpi-forum.org/docs/mpi-3.0/mpi30-report.pdf . 12. OpenMP Open MultiProcessing, from http://openmp.org/wp/ . 13. Nickolls, J., Buck, I., Garland, M., & Skadron, K. (2008). Scalable Parallel Programming with CUDA. ACM Queue , 6 (2). 14. Grama, A., Gupta, A., Karypis, G., & Kumar, V. (2003). Introduction to Parallel Computing. Adison-Wesley. 15. Pałka, M., Bednarski, T., Białas, P., Czerwiński, E., Kapłon, Ł., Kochanowski, A., Korcyl, G., Kowal, J., Kowalski, P., Kozik, T., Krzemień, W

Abstract

The main objective of the presented work was to explore the possibilities of parallel computing utilization in chemical engineering. Parallel computers and principles of parallel computing are in brief described in Introduction. The next part exposes the possibilities of parallel programming in Matlab and C# programming language environment. The next three parts provide case studies of parallel computing in chemical engineering. Each example of the benefits of HPC involves a comparison with its serial equivalents.

(1): 107-113. González-Vélez, H. (2006). Self-adaptive skeletal task farm for computational grids, Parallel Computing 32 (7-8): 479-490. González-Vélez, H. and Cole, M. (2010a). Adaptive statistical scheduling of divisible workloads in heterogeneous systems, Journal of Scheduling 13 (4): 427-441. González-Vélez, H. and Cole, M. (2010b). Adaptive structured parallelism for distributed heterogeneous architectures: A methodological approach with pipelines and farms, Concurrency and Computation: Practice and Experience 22 (15): 2073-2094. González-Vélez, H. and

, 2013. [6] S. R. Wu, L. Gu: Introduction to the Explicit Finite Element Method for Nonlinear Transient Dynamics. Wiley, 2012. [7] V. Rek, I. Němec: Parallel Computing Procedure for Dynamic Relaxation Method on GPU Using NVIDIA’s CUDA. Switzerland, Trans Tech Publications. Applied Mechanics and Materials, 2016, 821, 331-337. [8] S. Prata. C Primer Plus, Fifth Edition. Sams Publishing, 2004. [9] J. Har, K. K. Tamma. Advances in Computational Dynamics of Particles, Materials and Structures. Wiley. 2012. [10] E. WV Chaves. Notes on Continuum Mechanics (Lecture Notes on

Castillo N., Castellanos L. and Solano J. (2003). The parallel tunneling method, Parallel Computing 29 (4): 523-533. Hussein I. I. and Demetriou M. A. (2007). Estimation of distributed processes using mobile spatially distributed sensors, Proceedings of the 2007 American Control Conference , New York, NY, USA. Published on CD-ROM. Jeremić A. and Nehorai A. (1998). Design of chemical sensor arrays for monitoring disposal sites on the ocean floor, IEEE Transactions on Oceanic Engineering 23 (4): 334-343. Jeremić A. and Nehorai A. (2000). Landmine detection and

[20] R. Forster, A. Fülöp, Yang-Mills lattice on CUDA, Acta Univ. Sapientiae, Inf., 5, 2 (2013) 184-211. ⇒196 [21] S. Fortunato, Community detection in graphs, Phys. Rep. 486, 35 (2010) 75-174, http://dx.doi.org/10.1016/j.physrep.2009.11.002. ⇒204 [22] B. Hendrickson, T. G. Kolda, Graph partitioning models for parallel computing, Parallel Comput. 26, 12 (2000) 1519-1534. ⇒204 [23] M. Hodgkinson, Missing ET performance in ATLAS, in Proc. 34th Interna- tional Conference in High Energy Physics (ICHEP08), Philadelphia, 2008, eConf C080730 [arXiv:hep-ex/0810.0181] ⇒201

Abstract

The Yang-Mills fields have an important role in the non- Abelian gauge field theory which describes the properties of the quarkgluon plasma. The real time evolution of the classical fields is given by the equations of motion which are derived from the Hamiltonians to contain the term of the SU(2) gauge field tensor. The dynamics of the classical lattice Yang-Mills equations are studied on a 3 dimensional regular lattice. During the solution of this system we keep the total energy on constant values and it satisfies the Gauss law. The physical quantities are desired to be calculated in the thermodynamic limit. The broadly available computers can handle only a small amount of values, while the GPUs provide enough performance to reach out for higher volumes of lattice vertices which approximates the aforementioned limit.

Abstract

The problem of reads mapping to a reference genome is one of the most essential problems in modern computational biology. The most popular algorithms used to solve this problem are based on the Burrows-Wheeler transform and the FM-index. However, this causes some issues with highly mutated sequences due to a limited number of mutations allowed. G-MAPSEQ is a novel, hybrid algorithm combining two interesting methods: alignment-free sequence comparison and an ultra fast sequence alignment. The former is a fast heuristic algorithm which uses k-mer characteristics of nucleotide sequences to find potential mapping places. The latter is a very fast GPU implementation of sequence alignment used to verify the correctness of these mapping positions. The source code of G-MAPSEQ along with other bioinformatic software is available at: http://gpualign.cs.put.poznan.pl.

Abstract

With the advent of the new and continuously improving technologies, in a couple of years DNA sequencing can be as commonplace as a simple blood test. The growth of sequencing efficiency has a larger exponent than the Moore’s law of standard processors, hence alignment and further processing of sequenced data is the bottleneck. The usage of FPGA (Field Programmable Gate Arrays) technology may provide an efficient alternative. We propose a simple algorithm for DNA sequence alignment, which can be realized efficiently by nucleotic principal agents of Non.Neumann nature. The prototype FPGA implementation runs on a small Terasic DE1-SoC demo board with a Cyclone V chip. We present test results and furthermore analyse the theoretical scalability of this system, showing that the execution time is independent of the length of reference genome sequences. A special advantage of this parallel algorithm is that it performs exhaustive search producing all match variants up to a predetermined number of point (mutation) errors.

Abstract

The article presents the conception of an intelligent system for monitoring and managing the municipal waste disposal in metropolises. Applying advanced IT solutions using intelligent computational techniques enables the passage from the passive position of self-government units (JST) in managing the waste disposal to the active position, especially in decision making during the problem solving of planning systems associated with the organisation management of the complex infrastructure of the waste disposal. The aim of using ICT systems is an increase in the reliability of the economy of systemic waste, monitoring in real time, the stabilization of the work of the system and the optimization of logistic and technological processes in the context of the raw material, energy application and simultaneously limiting the influence on all components of the environment.