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Motivation and Overview
Published in Naim A. Kheir, Systems Modeling and Computer Simulation, 2018
The development of the hybrid computer resulted from realizing the potential, advantages, and disadvantages of the analog and digital computers of the 1950s (Howe, 1976). The main advantages of the digital, as mentioned earlier, were high precision and large storage capabilities, but low speed was the principal disadvantage. The analog, on the other hand, offered high speed and good human-machine interaction. The scaling requirements and limited precision were among the disadvantages of the analog computer. The hybrid computer emerged as a combination of analog-digital computers, mainly to solve aerospace problems, based on optimum utilization of analog and digital computing elements. An interface converting analog to digital and digital to analog allowed successful communication between the two types of computers and real-time simulation. Subdivision of the tasks performed on the analog and digital subsystems allowed using the analog for the dynamic part of simulation (including integration), and the digital mainly performed computations that are difficult or impossible on the analog. The hybrid computer is still in use for time critical applications, such as hardware-in-the loop simulation (Grider, 1984). The SIMSTAR system, developed by Electronics Associates, Inc., offers the user the option to select its operation from a parallel, serial (sequential), or parallel-serial combination.
Numerical Methods for ODEs
Published in Daniel Zwillinger, Vladimir Dobrushkin, Handbook of Differential Equations, 2021
Daniel Zwillinger, Vladimir Dobrushkin
A hybrid computer is one that combines both digital and analog computing devices. Generally, in such a configuration, the analog computer is used to perform tasks that are very time consuming on a digital computer. The analog computer is constructed, generally by the user, out of capacitors, operational amplifiers, resistors, and other electronic components. The numbers in an analog computer are represented by electrical quantities such as voltage and amperage.
Distillation
Published in John J. McKetta, Unit Operations Handbook, 2018
Analog and hybrid computers are other choices for simulation studies [26, 30, 32, 57, 76, 97]. Analog computers, as the name implies, are composed of electronic components. Hybrid computers include both analog and digital portions. Both types of computers are limited by the accuracy of the analog components. However, they are useful where fast solutions are desired. An operator-training simulator [8] is one example.
Plasma Waves Around Comets
Published in IETE Technical Review, 2022
The ULF waves in the proton (H+) and water group ion (H2O+) cyclotron frequencies were observed in the magnetic field fluctuations measured onboard ICE near the comet G–Z and Halley. It was also observed that the wave amplitude started growing as the spacecraft approached the comet and the observations were consistent with the one-dimensional electromagnetic hybrid computer simulations for the collisionless plasma environment of a comet [115].
Parallel computing solutions for Markov chain spatial sequential simulation of categorical fields
Published in International Journal of Digital Earth, 2019
Weixing Zhang, Weidong Li, Chuanrong Zhang, Tian Zhao
Parallel computing techniques have created a great opportunity for resolving spatial computational issues (Wang and Armstrong 2009; Ingram and Cornford 2010; Chao et al. 2011; Zhao et al. 2015; Li, Hodgson, and Li 2016; Liu et al. 2016; Du et al. 2017). At present, parallel computing solutions can be divided into three groups in terms of central processing units (CPUs) and graphics processing units (GPUs). The first group is parallelization using multi-core CPU(s) (including both shared memory models and distributed memory models). In geostatistics, for example, a number of researchers parallelized kriging interpolation processes on distributed computing systems (Armstrong and Marciano 1997; Pesquer, Cortés, and Pons 2011; Wei et al. 2015). Peredo, Ortiz, and Herrero (2015) enabled the Geostatistical Software Library (GSL) to improve computation efficiency using both hybrid parallel computing and code optimization. Mariethoz (2010) presented a general parallelization strategy for geostatistical sequential simulation at the path level and tested it using Message Passing Interface (MPI) on a distributed computing system. The second group is parallelization using many-core GPU(s) (e.g. the Compute Unified Device Architecture (CUDA) framework). For example, Srinivasan, Duraiswami, and Murtugudde (2010) parallelized the process of kriging interpolation to approximate real-time atmospheric conditions from scattered data on a GPU device. In order to reduce the execution time of running an ordinary kriging model, De Ravé et al. (2014) parallelized the matrix inverse processes, which were considered the most time-consuming step within the model. Li et al. (2014) developed a two-stage GPU-accelerated method to achieve the speedup of 17× in high-order spatial statistics calculation. Mei (2014) further improved the computational efficiency of Inverse Distance Weighting interpolation on GPU by maximizing the use of fast shared memory. Cheng (2013) and Wu et al. (2016) succeeded in speeding up a kriging algorithm for interpolation with the aid of CUDA-enabled GPU. The third group is heterogeneous computing. For example, in order to accelerate geostatistical simulation, Tahmasebi et al. (2012) developed a hybrid parallel computing method that can avoid conflicts between concurrent computations in simulation. Shi and Ye (2013) conducted a comparative study on parallelization methods of kriging interpolation on hybrid computer architectures and showed that the use of multiple GPU devices can accelerate kriging interpolation over a large data set.