Parallel MATLAB Using Standard MPI Implementations

Raeth, Peter G.; Chaves, Juan C.

doi:10.1109/hpcmp-ugc.2010.52

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The next design of a mathematical model of motion in a flying simulator is conditioned by identification of its stability. Roots of the characteristic equation, denominator Equations (13), (14), (16), respectively, Equation (17), see [11].…”

Section: The Angle Of Attack Dependencementioning

confidence: 99%

“…More information is shown in [12]. The MPICH2 implementation is portable, high-performance implementation of the entire MPI-2 standard and consists of a library of routines that can be called from the program [16]. In such a system, there are n nodes, which consist of a processor P and a local memory M. The program of a mathematical model of motion of aircraft is divided into concurrent processes, each is executed in a separate processor, see Figure 4.…”

Section: Parallel Application Of Mathematical Models On a Distributedmentioning

confidence: 99%

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Comparing simulation results of a structure defined mathematical model of aircraft

Kvasnica

2017

Archives of Electrical Engineering

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This paper is focused on creation, accuracy and simulation of 2-parameter control of a mathematical model for motion of aircraft in a flying simulator. We are discussing many of important advances in applied aircraft modeling. Modelling on various computer architectures (central, parallel, distributed) has an impact on a structure of a simulator system of aircraft. The way of description of a numerical method and its accuracy, a shared memory system and a distributed memory system, is an important part. Necessary accuracy of implemented simulation methods, an analytical approach to definition of mathematical models, and corresponding simulation implementation architecttures are presented in the article.

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Section: The Angle Of Attack Dependencementioning

confidence: 99%

Section: Parallel Application Of Mathematical Models On a Distributedmentioning

confidence: 99%

Comparing simulation results of a structure defined mathematical model of aircraft

Kvasnica

2017

Archives of Electrical Engineering

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“…The mathematical model of motion of aircraft can be simulated using central computer architecture, which can be based on single-processor systems. The simulation of structured mathematical model of an aircraft can be implemented using central computer architecture, a distributed memory system (DMS) using a message interface (MPI) architecture; they are identified as node computers or shared memory system (SMS) and graphics processing unit (GPU) architectures [3]. The parallel computer architecture may be based on multiprocessors; each processor has a multicore architecture.…”

Section: Introductionmentioning

confidence: 99%

Simulation of a Mathematical Model of an Aircraft Using Parallel Techniques (MPI and GPU)

Kvasnica¹

2022

Aeronautics - New Advances

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This chapter is focused on creation, accuracy, and simulation of two-parameter control of a mathematical model of motion of aircraft in a flying simulator. We are discussing many of the important advances in applied aircraft modeling. Modeling on various computer architectures (central, distributed, parallel) has an impact on a structure of a mathematical model of aircraft. An important part is the way of description of a numerical method and its accuracy, use of distributed memory system, and shared memory system are presented in the chapter. Motivation of this research is implementation of the general-purpose message passing interface and graphics processing units as inexpensive arithmetic-processing units bring a relevant amount of computing power to desktop personal computers. The chapter is focused on exploitation of parallel techniques of simulation features, computation time of parallel methods of implementation, and improved simulation of a continuous mathematical model of aircraft motion in a flying simulator. The use and application of modeling methods and parallel simulation techniques determine the structure of the mathematical model used in the flying simulator. The effectiveness of our solution is confirmed by providing simulation results obtained by two-parameter control of the mathematical model of aircraft motion.

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“…This is demonstrable by the fact that even in a high-level language environment like MAT-LAB, the computational load increases almost polynomially on increasing the dimensionality of a system, leading to longer run times. MATLAB is one of the preferred languages of development for scientific computing due to the ease with which algorithms can be developed and prototyped, which in turn is enabled by its array-based semantics, powerful visualization capabilities, and subject-specific toolboxes, all encased in an integrated framework [19]. While MATLAB excels on the "ease of programmability" and "portability" fronts, it has been found to be lacking in the "performance" department [20].…”

Section: Introductionmentioning

confidence: 99%

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Prakash

Chaudhury

Ramachandran

2013

Modelling and Simulation in Engineering

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Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB's Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes.

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Parallel MATLAB Using Standard MPI Implementations

Cited by 4 publications

References 3 publications

Comparing simulation results of a structure defined mathematical model of aircraft

Comparing simulation results of a structure defined mathematical model of aircraft

Simulation of a Mathematical Model of an Aircraft Using Parallel Techniques (MPI and GPU)

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

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