Design of a simulation model for high performance LINPACK in hybrid CPU-GPU systems

被引:0
|
作者
Yichang Hu
Lu Lu
机构
[1] South China University of Technology,School of Computer Science and Engineering
来源
关键词
High-performance LINPACK; Heterogeneous systems; GPU acceleration; Simulation;
D O I
暂无
中图分类号
学科分类号
摘要
High performance LINPACK (HPL) benchmark is used to evaluate the maximum floating-point performance of a computer cluster. Since the performance of the graphics processing unit (GPU) has been improved rapidly, many researchers start to optimize HPL benchmark through GPU to maximize system utilization. Nevertheless, it is difficult to determine the optimal combination of parameters in this process due to the complexity of the input. Therefore, running HPL on a heterogeneous system is time-consuming and is not flexible under different hardware components. So we propose a simulation model of HPL in this paper. The model is no longer limited by hardware components and able to simulate the execute process of HPL across different computing node in heterogeneous GPU-enhanced clusters at any scale. It can also assist researchers in evaluating the floating-point performance quickly and provide a reference for the hardware investment.
引用
收藏
页码:13739 / 13756
页数:17
相关论文
共 50 条
  • [1] Design of a simulation model for high performance LINPACK in hybrid CPU-GPU systems
    Hu, Yichang
    Lu, Lu
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (12): : 13739 - 13756
  • [2] Revisiting Linpack Algorithm on Large-scale CPU-GPU Heterogeneous Systems
    Shui, Chaoyang
    Yu, Xianzhi
    Yan, Yujin
    Wang, Yinshan
    Meng, Ke
    Tan, Guangming
    PROCEEDINGS OF THE 25TH ACM SIGPLAN SYMPOSIUM ON PRINCIPLES AND PRACTICE OF PARALLEL PROGRAMMING (PPOPP '20), 2020, : 411 - 412
  • [3] High Performance Graph Analytics with Productivity on Hybrid CPU-GPU Platforms
    Yang, Haoduo
    Su, Huayou
    Lan, Qiang
    Wen, Mei
    Zhang, Chunyuan
    2018 2ND INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPILATION, COMPUTING AND COMMUNICATIONS (HP3C 2018), 2018, : 17 - 21
  • [4] Performance Analysis of AES on CPU-GPU Heterogeneous Systems
    Sanz, Victoria
    Pousa, Adrian
    Naiouf, Marcelo
    De Giusti, Armando
    CLOUD COMPUTING, BIG DATA & EMERGING TOPICS, JCC-BD&ET 2022, 2022, 1634 : 31 - 42
  • [5] GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
    Zhu, Zhaocheng
    Xu, Shizhen
    Qu, Meng
    Tang, Jian
    WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), 2019, : 2494 - 2504
  • [6] GSched: An efficient scheduler for hybrid CPU-GPU HPC systems
    Mateos, Mariano Raboso
    Robles, Juan Antonio Cotobal
    1600, Springer Verlag (217): : 179 - 185
  • [7] PARALLEL SOLVER FOR SHIFTED SYSTEMS IN A HYBRID CPU-GPU FRAMEWORK
    Bosnery, Nela
    Bujanovic, Zvonimir
    Drmac, Zlatko
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2018, 40 (04): : C605 - C633
  • [8] Optimizing the LINPACK Algorithm for Large-Scale PCIe-Based CPU-GPU Heterogeneous Systems
    Tan, Guangming
    Shui, Chaoyang
    Wang, Yinshan
    Yu, Xianzhi
    Yan, Yujin
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2021, 32 (09) : 2367 - 2380
  • [9] Hybrid CPU-GPU Simulation of Hierarchical Adaptive Random Boolean Networks
    Kuvshinov, Kirill
    Bochenina, Klavdiya
    Gorski, Piotr J.
    Holyst, Janusz A.
    EURO-PAR 2017: PARALLEL PROCESSING WORKSHOPS, 2018, 10659 : 403 - 414
  • [10] HybridHadoop: CPU-GPU Hybrid Scheduling in Hadoop
    Oh, Chanyoung
    Jung, Hyeonjin
    Yi, Saehanseul
    Yoon, Illo
    Yi, Youngmin
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING IN ASIA-PACIFIC REGION (HPC ASIA 2021), 2020, : 40 - 49