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pro vyhledávání: '"Ibeid, Huda"'
Autor:
Ibeid, Huda
Exascale systems are predicted to have approximately one billion cores, assuming Gigahertz cores. Limitations on affordable network topologies for distributed memory systems of such massive scale bring new challenges to the currently dominant paralle
Externí odkaz:
http://hdl.handle.net/10754/621993
We propose a simulation-based approach for performance modeling of parallel applications on high-performance computing platforms. Our approach enables full-system performance modeling: (1) the hardware platform is represented by an abstract yet high-
Externí odkaz:
http://arxiv.org/abs/2011.02617
To understand and predict the performance of scientific applications, several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages. In this paper, we propose and validate a hybrid approach for pe
Externí odkaz:
http://arxiv.org/abs/1810.11772
FFT, FMM, and multigrid methods are widely used fast and highly scalable solvers for elliptic PDEs. However, emerging large-scale computing systems are introducing challenges in comparison to current petascale computers. Recent efforts (Dongarra et a
Externí odkaz:
http://arxiv.org/abs/1810.11883
Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical $N$-Body algorithms like FMM. In the present work, we propose four independent strategies to improve partitioning and reduce communication.
Externí odkaz:
http://arxiv.org/abs/1702.05459
Fast multipole methods (FMM) were originally developed for accelerating $N$-body problems for particle-based methods. FMM is more than an $N$-body solver, however. Recent efforts to view the FMM as an elliptic Partial Differential Equation (PDE) solv
Externí odkaz:
http://arxiv.org/abs/1608.02461
There has been a large increase in the amount of work on hierarchical low-rank approximation methods, where the interest is shared by multiple communities that previously did not intersect. This objective of this article is two-fold; to provide a tho
Externí odkaz:
http://arxiv.org/abs/1602.02244
Autor:
Ibeid, Huda
The current trend in high performance computing is pushing towards exascale computing. To achieve this exascale performance, future systems will have between 100 million and 1 billion cores assuming gigahertz cores. Currently, there are many efforts
Externí odkaz:
http://hdl.handle.net/10754/267452
http://repository.kaust.edu.sa/kaust/handle/10754/267452
http://repository.kaust.edu.sa/kaust/handle/10754/267452
Publikováno v:
In Journal of Parallel and Distributed Computing February 2020 136:63-74
Exascale systems are predicted to have approximately one billion cores, assuming Gigahertz cores. Limitations on affordable network topologies for distributed memory systems of such massive scale bring new challenges to the current parallel programin
Externí odkaz:
http://arxiv.org/abs/1405.6362