Earticle

초록

영어

With the rapid progress of computational science and computer simulation ability, a lot of properties can be predicted by the powerful ability of parallel computation before the actual research and development. With the development of high performance computer architecture, GPU is more and more widely used in high performance computation field as an emerging architecture, and a growing number of computations use GPU heterogeneous cluster architecture. However, how to partition workload and map to computing resource has always been the focus and difficult point. In the current study of GPU, according to the problems of the computing power provided by each node and the cluster hardware architecture which the application programmers don't understand, some partitioning strategies will result in serious load imbalance problem. Aimed at the complexity brought by the different computing ability of the nodes of GPU clusters, this paper proposes a GPU data partitioning strategy of heterogeneous clusters based on learning. It collects the states of each node in the process of running a program, and then estimates the calculation ability of each node dynamically, so as to guide the data partitioning. Actual testing results show that, this strategy allocates different tasks to nodes based on computing ability to ensure load balancing among nodes, so as to improve the execution performance of CUDA programs on heterogeneous GPU clusters and it laid a solid foundation for efficient computing on heterogeneous GPU clusters.

목차

Abstract
 1. Introduction
 2. The GPU Clusters Architecture and Programming Mode
  2.1. GPU Clusters Architecture
  2.2. The Programming Model on GPU Heterogeneous Clusters
  2.3. The design of programs on GPU heterogeneous clusters
 3. Data Partitioning Strategy of GPU Heterogeneous Clusters Based on Learning
  3.1. The Description of the Learning Process
  3.2. The Implementation of Data Partitioning Strategy
 4. Test and Result Analysis
  4.1. The Implementation of Data Partitioning Strategy
  4.2. The Implementation of Data Partitioning Strategy
 5. Related Work
 6. Conclusion
 Acknowledgments
 References

키워드

저자

• Jianjiang Li [ Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China ]
• Wei Chen [ Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China ]
• Jin Tian [ Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China ]
• Hongyan Zheng [ Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China ]
• Peng Zhang [ Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China ]
• Yajun Liu [ Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605