Desktop Grid Computing (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)

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Change Language. English Arabic. Important Links. Follow Us. App Download. US UK. Thank you for subscribing! Please check your email to confirm your subscription. Our Stores. Results for - desktop. All the latest offers delivered right to your inbox! We Accept. Table 4 Algorithm performance for various platforms and floatingpoint precisions formats. The most computationally intensive part of our algorithm is the bisection sub-algorithm at level no. We implemented it as described in [ 13 ] and [ 12 ], producing single- and double-precision versions, algorithmically identical on both GPU and CPU platforms.

For reference, we provide the double-precision version based on the AlgLib library [ 6 ]. This library includes a state-of-the-art implementation of the bisection algorithm, thoroughly tuned to produce the most accurate results possible with the modern CPUs floating point units FPUs. Our CPU-based implementation of bisection algorithm can not boast such accuracy. However, it is notably faster than AlgLib due to its simplicity. The discrepancy between the outputs residue of the same algorithm on the CPU and GPU is the result of the different implementations of floating-point units on these platforms.

However, recomputation of this final point in FP64 gives the result 0.

Parallel and distributed computation: numerical methods

Table 5 Mixed-mode algorithm performance. The performance of this algorithm is presented in Table 5. The total runtime of this mixed-mode algorithm is seconds. This is faster than runtime of the pure GPU-based FP64 algorithm seconds , and its final residue 0.

However, according to [ 12 ] this performance drop should be at least 4x times for the GTX GPU used in our experiments. The higher GPU performance we perceived in our previous experiments with the parallelization scheme based on the simultaneous computing of residues for many search space points supports this theory. This means that a thorough optimization of the GPU code could increase the GPU performance of our geoacoustic inversion algorithm at least times.

The computing clusters are often used in the practical applications of geoacoustic inversion algorithms see, e. While sometimes one can compromise by using heuristic optimization techniques in end-user applications, the problems of development and validation of inversion algorithms anyway set very strong demand for the high-performance computational tools. In the previous works [ 26 , 27 ] we solved on a computing cluster the problems similar to that, considered in the present paper. The restrictions on available computational resources forced us to launch Acoustics home.

But we will continue to use the cluster in order to solve simple scenarios, or to test new versions of the computing application. In [ 18 ] general purpose GPUs were used to accelerate underwater acoustic propagation modeling. In the present study we describe the volunteer computing project Acoustics home that was set up to handle computationally intense geoacoustic inversion problems. With the help of our volunteers we conducted a series of experiments aiming to investigate the capabilities of the dispersion-based inversion method in producing the estimate of the sound speed profile in shallow-water environment.

In our view, it seems interesting to find out, how many grid points are required to resolve the function c z and how accurate the inversion result can be. In [ 25 ] a similar question was studied by using a sophisticated trans-D Bayesian inversion approach. Our results are obtained in a much more simple and straightforward way, and they are also probably easier to understand. The accuracy of the sound speed profile inversion is quite encouraging, and it seems that in principle function c z can be estimated from the data almost exactly.

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At least, it is clear that there is no limitation neither on the side of the warping-based mode filtering algorithm, nor on the side of the global minimization problem i. It is also important to note that increasing the number of nodes in sound speed profile approximation does not necessarily improve the inversion accuracy. At some point adding new dimensions of the search space results in the larger deviation of the inverted sound speed profile from the true one. The present study highlights great opportunities that volunteer computing can bring into the field of computational underwater acoustics.

In the future, we are aiming at developing a powerful computational framework that can be used on demand by any member of ocean acoustics research community who needs to conduct some very intense calculations in order to solve certain direct or inverse problems. To this end, we are planning to expand our code by adding some modules capable of solving propagation problems in inhomogeneous 2D and 3D waveguides and also implement other inversion algorithm not necessarily relying on single-hydrophone data. We thank all Acoustics home volunteers, whose computers took part in the experiment.

David P. Konstantin Barkalov and Victor Gergel. Parallel global optimization on GPU. Journal of Global Optimization, 66 1 :3—20, Sep Academic Press, Oxford, Sergey Bochkanov and Vladimir Bystritsky. Alglib - a cross-platform numerical analysis and data processing library. Novgorod, Russia, Bonnel, C. Gervaise, B. Nicolas, and J. Single-receiver geoacoustic inversion using modal reversal. The Journal of the Acoustical Society of America, 1 —, Julien Bonnel and N. Ross Chapman. Geoacoustic inversion in a dispersive waveguide using warping operators.

Julien Bonnel, Stan E. Dosso, and N.

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Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis. Christophe Cerin and Gilles Fedak. Desktop Grid Computing. Nvidia Corporation. Cuda c programming guide, James W Demmel. Applied numerical linear algebra. SIAM, Automated geoacoustic inversion and uncertainty: Meso-scale seabed variability in shallow water environments. Report, The Office of Naval Research, Evtushenko, S. Lurie, M. Posypkin, and Yu. Application of optimization methods for finding equilibrium states of two-dimensional crystals.

Computational Mathematics and Mathematical Physics, 56 12 —, Global Connections. Robert Hooke and T. ACM, 8 2 —, April Hursky and M.

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Accelerating underwater acoustic propagation modeling using general purpose graphic processing units. Jensen, W. Kuperman, M. Porter, and H. Computational ocean acoustics. Springer, New-York et al, Katsnelson, V. Petnikov, and J. Fundamentals of Shallow Water Acoustics. Springer US, New-York et al, Vladimir V. Mazalov, Natalia N. Nikitina, and Evgeny E. Task scheduling in a desktop grid to minimize the server load.

Springer-Verlag New York, Inc. A method for single-hydrophone geoacoustic inversion based on the modal group velocities estimation: Application to a waveguide with inhomogeneous bottom relief. Petrov, A. Zakharenko, and M. The wave equation with viscoelastic attenuation and its application in problems of shallow-sea acoustics. Acoustical Physics, 58 6 —, Graham A.

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