Scalable multi-GPU implementation of the MAGFLOW simulator

被引:3
|
作者
Rustico, Eugenio [1 ]
Bilotta, Giuseppe [1 ,2 ]
Herault, Alexis [2 ,3 ]
Del Negro, Ciro [2 ]
Gallo, Giovanni [1 ]
机构
[1] Univ Catania, Dipartimento Matemat & Informat, Catania, Italy
[2] Ist Nazl Geofis & Vulcanol, Sez Catania, Osservatorio Etneo, Catania, Italy
[3] Conservatoire Arts & Metiers, Dept Ingn Math, Paris, France
关键词
D O I
10.4401/ag-5342
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We have developed a robust and scalable multi-GPU (Graphics Processing Unit) version of the cellular-automaton-based MAGFLOW lava simulator. The cellular automaton is partitioned into strips that are assigned to different GPUs, with minimal overlapping. For each GPU, a host thread is launched to manage allocation, deallocation, data transfer and kernel launches; the main host thread coordinates all of the GPUs, to ensure temporal coherence and data integrity. The overlapping borders and maximum temporal step need to be exchanged among the GPUs at the beginning of every evolution of the cellular automaton; data transfers are asynchronous with respect to the computations, to cover the introduced overhead. It is not required to have GPUs of the same speed or capacity; the system runs flawlessly on homogeneous and heterogeneous hardware. The speed-up factor differs from that which is ideal (#GPUsx) only for a constant overhead loss of about 4E(-2) . T . #GPUs, with T as the total simulation time.
引用
收藏
页码:592 / 599
页数:8
相关论文
共 50 条
  • [1] Scalable hybrid implementation of the Schur complement method for multi-GPU systems
    Kopysov, Sergey
    Kuzmin, Igor
    Nedozhogin, Nikita
    Novikov, Alexander
    Sagdeeva, Yulia
    [J]. JOURNAL OF SUPERCOMPUTING, 2014, 69 (01): : 81 - 88
  • [2] Scalable hybrid implementation of the Schur complement method for multi-GPU systems
    Sergey Kopysov
    Igor Kuzmin
    Nikita Nedozhogin
    Alexander Novikov
    Yulia Sagdeeva
    [J]. The Journal of Supercomputing, 2014, 69 : 81 - 88
  • [3] MAPREDUCE IMPLEMENTATION WITH MULTI-GPU
    Chen, Yi
    Chen, Su
    Jiang, Hai
    [J]. INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE & TECHNOLOGY: PROCEEDINGS, 2012, : 21 - 25
  • [4] Multi-GPU Implementation of LU Factorization
    Jia, Yulu
    Luszczek, Piotr
    Dongarra, Jack
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2012, 2012, 9 : 106 - 115
  • [5] Scalable multi-gpu cloud raytracing with OpenGL
    Chochlik, Matus
    [J]. 2014 10TH INTERNATIONAL CONFERENCE ON DIGITAL TECHNOLOGIES (DT), 2014, : 87 - 95
  • [6] Scalable Betweenness Centrality on Multi-GPU systems
    Bernaschi, Massimo
    Carbone, Giancarlo
    Vella, Flavio
    [J]. PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS (CF'16), 2016, : 29 - 36
  • [7] Efficient Implementation of MrBayes on Multi-GPU
    Bao, Jie
    Xia, Hongju
    Zhou, Jianfu
    Liu, Xiaoguang
    Wang, Gang
    [J]. MOLECULAR BIOLOGY AND EVOLUTION, 2013, 30 (06) : 1471 - 1479
  • [8] Towards a Multi-GPU Implementation of a Seismic Application
    Rigon, Pedro H. C.
    Schussler, Brenda S.
    Padoin, Edson L.
    Lorenzon, Arthur F.
    Carissimi, Alexandre
    Navaux, Philippe O. A.
    [J]. HIGH PERFORMANCE COMPUTING, CARLA 2023, 2024, 1887 : 146 - 159
  • [9] Multi-GPU Implementation of the NICAM Atmospheric Model
    Demeshko, Irina
    Maruyama, Naoya
    Tomita, Hirofumi
    Matsuoka, Satoshi
    [J]. EURO-PAR 2012: PARALLEL PROCESSING WORKSHOPS, 2013, 7640 : 175 - 184
  • [10] Multi-GPU implementation of the lattice Boltzmann method
    Obrecht, Christian
    Kuznik, Frederic
    Tourancheau, Bernard
    Roux, Jean-Jacques
    [J]. COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2013, 65 (02) : 252 - 261