The article presents a mathematical model of biofilm growth for aerobic biodegradation of a toxic carbonaceous substrate. Modelling of biofilm growth has fundamental significance in numerous processes of biotechnology...
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The article presents a mathematical model of biofilm growth for aerobic biodegradation of a toxic carbonaceous substrate. Modelling of biofilm growth has fundamental significance in numerous processes of biotechnology and mathematical modelling of bioreactors. The process following double-substrate kinetics with substrate inhibition proceeding in a biofilm has not been modelled so far by means of cellular automata. Each process in the model proposed, i.e. diffusion of substrates, uptake of substrates, growth and decay of microorganisms and biofilm detachment, is simulated in a discrete manner. It was shown that for flat biofilm of constant thickness, the results of the presented model agree with those of a continuous model. The primary outcome of the study was to propose a mathematical model of biofilm growth;however a considerable amount of focus was also placed on the development of efficient algorithms for its solution. Two parallel algorithms were created, differing in the way computations are distributed. Computer programs were created using OpenMP Application Programming Interface for C++ programming language. Simulations of biofilm growth were performed on three high-performance computers. Speed-up coefficients of computer programs were compared. Both algorithms enabled a significant reduction of computation time. It is important, inter alia, in modelling and simulation of bioreactor dynamics.
The n-dimensional locally twisted cube LTQn is a variant of the hypercube, which possesses some properties superior to the hypercube. This paper investigates the conditional edge fault-tolerant Hamiltonian-connected o...
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ISBN:
(纸本)9781467388399
The n-dimensional locally twisted cube LTQn is a variant of the hypercube, which possesses some properties superior to the hypercube. This paper investigates the conditional edge fault-tolerant Hamiltonian-connected of LTQn, and shows that for any n-dimensional locally twisted cube LTQn (n≥5) with faulty edges up to 2n-8 in which each vertex is incident to at least three fault-free edges, there exists a fault-free Hamiltonian path connecting any two vertices.
The advancement of the engine control increases the amount of *** production ECU(Electronic Control Unit),which is made of single-core architecture,cannot have a higher clock *** multi- / many-core architecture is the...
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The advancement of the engine control increases the amount of *** production ECU(Electronic Control Unit),which is made of single-core architecture,cannot have a higher clock *** multi- / many-core architecture is the only way to decrease execution ***,when implementing the engine control software,various problems occur in utilization of the multi- / many-core *** of the biggest problems is sequential structure of control software because the software can only execute with one core on the multi- / many-core *** purpose of this paper is to evaluate numerical error caused by parallelizing complicated models using the proposed parallelized control design method[1],which has decomposed sequential structure and decreases execution time in the embedded multi- / many-core production ECU,and evaluate the performance of each parallelized term.
In this paper, we propose a mapping algorithm called particle filter mapping (PFMAP);PFMAP is able to map task nodes onto the cores of tile-based network-on-chip (NoC) architectures, such as regular, irregular, and cu...
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In this paper, we propose a mapping algorithm called particle filter mapping (PFMAP);PFMAP is able to map task nodes onto the cores of tile-based network-on-chip (NoC) architectures, such as regular, irregular, and custom 2-D or 3-D topologies. PFMAP is inspired from systematic resampling algorithm for particle filters, in which all particles can run parallel and independently from each other. Based upon the experimental results from applying PFMAP for various real life and synthetic applications onto the different topologies and architectures, the performance of the 2-D mesh architectures in terms of communication cost increased by up to 51% for irregular topologies, and by up to 31% for custom architectures. Similarly, total travel distance obtained by PFMAP is reduced by up to 45% for custom 2-D mesh architectures. In addition to these, average clock cycles per flit and total network power are reduced by up to 17% and 15% for regular 2-D mesh architectures, respectively. Finally, communication cost is diminished by up to 34% for 3-D regular NoC architectures.
Fast and accurate determination of the 3D structure of molecules is essential for better understanding their physical, chemical, and biological properties. We focus on an existing method for molecular structure determ...
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Fast and accurate determination of the 3D structure of molecules is essential for better understanding their physical, chemical, and biological properties. We focus on an existing method for molecular structure determination: restrained molecular dynamics with simulated annealing. In this method a hybrid function, composed by a physical model and experimental restraints, is minimized by simulated annealing. Our goal is to accelerate computation time using commodity multi-core CPUs and GPUs in a heterogeneous computing model. We present a parallel and portable OpenCL implementation of this method. Experimental results are discussed in terms of accuracy, execution time, and parallel scalability. With respect to the XPLOR-NIH professional software package, compared to the single CPU core implementation, we obtain speedups of three to five times (increasing with problem size) on commodity GPUs. We achieve these performances by writing specialized kernels for different problem sizes and hardware architectures.
We present an optimal deterministic O(n)-work parallel algorithm for finding a minimum spanning tree on an n-vertex planar graph. The algorithm runs in O(log n) time on a CRCW PRAM and in O(log n log∗ n) time on an ER...
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Runtime verification is a monitoring technique to gain assurance about well-being of a program at run time. Most existing approaches use sequential monitors;i.e., when the state of the program with respect to an event...
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Runtime verification is a monitoring technique to gain assurance about well-being of a program at run time. Most existing approaches use sequential monitors;i.e., when the state of the program with respect to an event of interest changes, the monitor interrupts the program execution, evaluates a set of logical properties, and finally resumes the program execution. In this paper, we propose a GPU-based method for design and implementation of monitors that enjoy two levels of parallelism: the monitor (1) works along with the program in parallel, and (2) evaluates a set of properties in a parallel fashion as well. Our parallel monitoring algorithms effectively exploit the many-core platform available in the GPU. In addition to parallel processing, our approach benefits from a true separation of monitoring and functional concerns, as it isolates the monitor in the GPU. Thus, our monitoring approach incurs minimal intrusion, as executing monitoring tasks take place in a different computing hardware from execution of the program under inspection. Our method is fully implemented for parametric and non-parametric 3-valued linear temporal logic. Our experimental results show significant reduction in monitoring overhead, monitoring interference, and power consumption due to leveraging the GPU technology. In particular, we observe that our parallel verification algorithms are indeed scalable.
We study effective parallelization of approximation algorithms for the one-dimensional bin packing problem on a multicore platform. Bin packing is a classic combinatorial optimization problem that aims to pack a given...
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ISBN:
(纸本)9781509054121
We study effective parallelization of approximation algorithms for the one-dimensional bin packing problem on a multicore platform. Bin packing is a classic combinatorial optimization problem that aims to pack a given sequence of items into a minimum number of equal-sized bins. The problem potentially serves as a model for a wide variety of applications. Examples include: packing data into chunks in a memory hierarchy in a given system to increase application performance, loading vehicles subject to weight limitations, and packing TV commercials into station breaks. Bin packing has long served as a proving ground for the analysis of approximation algorithms and played a crucial role in the development of much of the theory of approximation algorithms. Its parallelization, however, has received comparatively much less attention. In this work, we develop multiple parallel versions of an effective approximation algorithm (First Fit Decreasing) for the problem and investigate the trade-off between solution quality and execution time. We use OpenMP and Cilk Plus as mechanisms for achieving the parallelization. The new parallel algorithms obtain a speedup of more than 10× (on 32 cores) for moderate to large input sequences without sacrificing much on the quality of solution produced by the sequential algorithm - in particular, we see only about 3 to 30% increase in the number of bins compared to the sequential version. In turn, the solution obtained by the sequential First Fit Decreasing algorithm is provably almost optimal (the approximation ratio is less than 1.3).
Summary form only given. We advocate the usage of mathematical models and abstractions in practical high-performance computing. For this, we show a series of examples and use-cases where the abstractions introduced by...
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ISBN:
(纸本)9781509036547
Summary form only given. We advocate the usage of mathematical models and abstractions in practical high-performance computing. For this, we show a series of examples and use-cases where the abstractions introduced by performance models can lead to clearer pictures of the core problems and often provide non-obvious insights. We start with models of parallel algorithms leading to close-to-optimal practical implementations. We continue our tour with distributed-memory programming models that provide various abstractions to application developers. A short digression on how to measure parallel systems shows common pitfalls of practical performance modeling. Application performance models based on such accurate measurements support insight into the resource consumption and scalability of parallel programs on particular architectures. We close with a demonstration of how mathematical models can be used to derive practical network topologies and routing algorithms. In each of these areas, we demonstrate newest developments but also point to open problems. All these examples testify to the value of modeling in practical high-performance computing. We assume that a broader use of these techniques and the development of a solid theory for parallel performance will lead to deep insights at many fronts.
Bioinformatics is a cross subject of biological information processing. DNA sequence splicing is one of its research content. At present, most parallel algorithms are based on the operating environment of MapReduce. T...
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ISBN:
(纸本)9781509035762
Bioinformatics is a cross subject of biological information processing. DNA sequence splicing is one of its research content. At present, most parallel algorithms are based on the operating environment of MapReduce. There is a complex process for reading and writing to hard disk, which lead to inferiority that the speed of the algorithm will be slow. In this paper, Spark calculation model based on memory is proposed to solve the problem. At the same time, a new method of matching K-2 bit will be also used by us. Results of experiment show that the running environment based on Spark and the method can ensure accuracy of stitching results and make the algorithm more efficient.
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