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Creation and application of heterogeneous grid environment for seismic modeling

100%

Strzelczyk J. , Danek T.

Geoinformatica Polonica

2009

tom T. 9

35--41

Grid computing is a simultaneous application of several computers to one, single, computationally intensive problem. A computer grid can be created from special, dedicated hardware. but ais o unused CPU cycles of available desktop machines can be exploited. This article report s on a successful effort of creating a heterogeneous grid infrastructure. The installation consisted of two Blade servers and one PC machine that were available at the time. Heterogeneity was present on both levels: hardware platform and operating system. Blade machines ran Linux Fedora Core, while PC ran Windows 2000 SP4. Using only free grid software, the authors created working infrastructure and used it in seismic modeling ca1culations.

Termin grid computing oznacza jednoczesne wykorzystanie kilku komputerów do rozwiązywania jednego, złożonego problemu obliczeniowego. Komputerowy grid może zostać stworzony ze specjalnych urządzeń dedykowanych, przeznaczonych do tego celu, bądź także z nieużywanych procesorów dostępnych w biurowych komputerach Pc. W tym artykule opisano udaną próbę stworzenia heterogenicznej infrastruktury gridowej. System składał się z dwóch serwerów typu IBM Blade oraz z jednej maszyny PC, która była aktualnie dostępna. Heterogeniczność była obecna na obu poziomach: platformy sprzętowej oraz systemu operacyjnego. Na maszynach typu IBM Blade zainstalowany był system Linux Fedora Core, a komputer PC działał pod kontrolą systemu Windows 2000 SP4. Używając jedynie darmowego oprogramowania gridowego, autorzy stworzyli działającą infrastrukturę i użyli jej w procesie modelowania sejsmicznego pola falowego.

An adaptive multi-spline refinement algorithm in simulation based sailboat trajectory optimization using onboard multi-core computer systems

84%

Dębski R.

International Journal of Applied Mathematics and Computer Science

2016

tom Vol. 26, no. 2

351--365

A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of “high-performance sailing”. The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties). Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC) platforms—they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.

High-performance simulation-based algorithms for an alpine ski racer’s trajectory optimization in heterogeneous computer systems

84%

Dębski R.

International Journal of Applied Mathematics and Computer Science

2014

tom Vol. 24, no. 3

551--566

Effective, simulation-based trajectory optimization algorithms adapted to heterogeneous computers are studied with reference to the problem taken from alpine ski racing (the presented solution is probably the most general one published so far). The key idea behind these algorithms is to use a grid-based discretization scheme to transform the continuous optimization problem into a search problem over a specially constructed finite graph, and then to apply dynamic programming to find an approximation of the global solution. In the analyzed example it is the minimum-time ski line, represented as a piecewise-linear function (a method of elimination of unfeasible solutions is proposed). Serial and parallel versions of the basic optimization algorithm are presented in detail (pseudo-code, time and memory complexity). Possible extensions of the basic algorithm are also described. The implementation of these algorithms is based on OpenCL. The included experimental results show that contemporary heterogeneous computers can be treated as μ-HPC platforms—they offer high performance (the best speedup was equal to 128) while remaining energy and cost efficient (which is crucial in embedded systems, e.g., trajectory planners of autonomous robots). The presented algorithms can be applied to many trajectory optimization problems, including those having a black-box represented performance measure.