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MG-Hybrid: A Strongly Connected Components Detection algorithm Using Multiple GPUs 52

MG-Hybrid: A Strongly Connected Components Detection Algorit...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hou, Junteng Wang, Shupeng Wu, Guangjun Ma, Bingnan Si, Chengxiang Jia, Siyu Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9781728133201

Detection of strongly connected component (SCC) on the GPU has become a fundamental operation to accelerate graph computing. Existing SCC detection methods on multiple GPUs introduce massive unnecessary data transformation between multiple GPUs. In this paper, we propose a novel distributed SCC detection approach using multiple GPUs plus CPU. Our approach includes three key ideas: (1) segmentation and labeling over large-scale datasets;(2) collecting and merging the segmented SCCs;and (3) running tasks assignment over multiples GPUs and CPU. We implement our approach under a hybrid distributed architecture with multiple GPUs plus CPU. Our approach can achieve device-level optimization and can be compatible with the state-of-the-art algorithms. We conduct extensive theoretical and experimental analysis to demonstrate efficiency and accuracy of our approach. The experimental results expose that our approach can achieves 11.2x, 1.2x, 1.2x speedup for SCC detection using NVIDIA K80 compared with Tarjan's, FB-Trim, and FB-Hybrid algorithms respectively.

关键词： graph computing strongly connected components detection multiple GPUs heterogeneous algorithm hybrid scheme

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Scalable Fast Multipole Accelerated Vortex Methods 28

Scalable Fast Multipole Accelerated Vortex Methods

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28th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Hu, Qi Gumerov, Nail A. Yokota, Rio Barba, Lorena Duraiswami, Ramani Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Fantalgo LLC Elkridge MD USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia George Washington Univ Mech & Aerosp Engn Washington DC 20052 USA

ISBN: (纸本)9781479941162

The fast multipole method (FMM) is often used to accelerate the calculation of particle interactions in particle-based methods to simulate incompressible flows. To evaluate the most time-consuming kernels-the Biot-Savart equation and stretching term of the vorticity equation, we mathematically reformulated it so that only two Laplace scalar potentials are used instead of six. This automatically ensuring divergence-free far-field computation. Based on this formulation, we developed a new FMM-based vortex method on heterogeneous architectures, which distributed the work between multicore CPUs and GPUs to best utilize the hardware resources and achieve excellent scalability. The algorithm uses new data structures which can dynamically manage inter-node communication and load balance efficiently, with only a small parallel construction overhead. This algorithm can scale to large-sized clusters showing both strong and weak scalability. Careful error and timing trade-off analysis are also performed for the cutoff functions induced by the vortex particle method. Our implementation can perform one time step of the velocity+stretching calculation for one billion particles on 32 nodes in 55.9 seconds, which yields 49.12 Tflop/s.

关键词： FMM heterogeneous algorithm GPGPU vortex methods

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Scalable Fast Multipole Methods for Vortex Element Methods

Scalable Fast Multipole Methods for Vortex Element Methods

引用

25th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Hu, Qi Gumerov, Nail A. Yokota, Rio Barba, Lorena Duraiswami, Ramani UMIACS College Pk MD 20740 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Fantalgo LLC Elkridge MD 21075 USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Boston Univ Dept Mech Engn Boston MA 02215 USA

ISBN: (纸本)9780769549569;9781467362184

We use a particle-based method to simulate incompressible flows, where the Fast Multipole Method (FMM) is used to accelerate the calculation of particle interactions. The most time-consuming kernelsâ'the Biot-Savart equation and stretching term of the vorticity equationâ'are mathematically reformulated so that only two Laplace scalar potentials are used instead of six, while automatically ensuring divergence-free far-field computation. Based on this formulation, and on our previous work for a scalar heterogeneous FMM algorithm, we develop a new FMM-based vortex method capable of simulating general flows including turbulence on heterogeneous architectures, which distributes the work between multi-core CPUs and GPUs to best utilize the hardware resources and achieve excellent scalability. The algorithm also uses new data structures which can dynamically manage inter-node communication and load balance efficiently but with only a small parallel construction overhead. This algorithm can scale to large-sized clusters showing both strong and weak scalability. Careful error and timing trade-off analysis are also performed for the cutoff functions induced by the vortex particle method. Our implementation can perform one time step of the velocity+stretching for one billion particles on 32 nodes in 55.9 seconds, which yields 49.12 Tflop/s. © 2012 IEEE.

关键词： fast multipole method heterogeneous algorithm vortex methods GPU

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Abstract: Scalable Fast Multipole Methods for Vortex Element Methods

Abstract: Scalable Fast Multipole Methods for Vortex Element...

引用

High Performance Computing, Networking, Storage and Analysis (SCC), SC Companion:

作者： Qi Hu Nail A. Gumerov Rio Yokota Lorena Barba Ramani Duraiswami University of Maryland Institute for Advanced Computer Studies USA King Abdullah University for Science and Technology Saudi Arabia Mechanical Engineering Department Boston University USA

ISBN: (纸本)9781467308052

关键词： fast multipole method heterogeneous algorithm vortex methods GPU

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Poster: Scalable Fast Multipole Methods for Vortex Element Methods

Poster: Scalable Fast Multipole Methods for Vortex Element M...

引用

High Performance Computing, Networking, Storage and Analysis (SCC), SC Companion:

作者： Qi Hu Nail A. Gumerov Rio Yokota Lorena Barba Ramani Duraiswami Institute for Advanced Computer Studies University of Maryland College Park USA Strategic Initiative for Extreme CompuGng King Abdullah University for Science and Technology Saudi Arabia Mechanical Engineering Department Boston University USA

ISBN: (纸本)9781467308052

We use a particle-based method to simulate incompressible flows, where the Fast Multipole Method (FMM) is used to accelerate the calculation of particle interactions. The most time-consuming kernelsâ"the Biot-Savart equation and stretching term of the vorticity equationâ"are mathematically reformulated so that only two Laplace scalar potentials are used instead of six, while automatically ensuring divergence-free far-field computation. Based on this formulation, and on our previous work for a scalar heterogeneous FMM algorithm, we develop a new FMM-based vortex method capable of simulating general flows including turbulence on heterogeneous architectures, which distributes the work between multi-core CPUs and GPUs to best utilize the hardware resources and achieve excellent scalability. The algorithm also uses new data structures which can dynamically manage inter-node communication and load balance efficiently but with only a small parallel construction overhead. This algorithm can scale to large-sized clusters showing both strong and weak scalability. Careful error and timing trade-off analysis are also performed for the cutoff functions induced by the vortex particle method. Our implementation can perform one time step of the velocity+stretching for one billion particles on 32 nodes in 55.9 seconds, which yields 49.12 Tflop/s.

关键词： fast multipole method heterogeneous algorithm vortex methods GPU

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