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Smart Scheduler for CUDA Programming in Heterogeneous CPU/GPU Environment

Published: 16 January 2019 Publication History

Abstract

The demand for high performance has driven the technology to grow exponentially requiring the computer systems to work as effectively as possible for a valuable output. Substantial innovation in technology with time has made the use of GPUs working together with CPUs in order to make the system more efficient in performing computations optimally. This paper presents design of a scheduler for heterogeneous CUDA environment which ensures that while the task is fetched into the system, all the nodes participate fully in scheduling, thereby completing the task in less span of time as compared to normal schedulers resulting in efficient results. Tasks have been divided amongst the computing nodes according to the availability of the nodes giving maximum possible throughput of the system according to the workload. The scheduler has the potential of running the GPU code in parallel on different computing nodes within the High Performance Computing environment improving the overall performance of the applications. As a result, it turned out that the Smart scheduler gives better throughput in comparison to SLURM's existing schedulers which indicates that there was a room in SLURM's existing schedulers to increase the number of jobs within less span of time. The overall improvement in the throughput was observed to be up to 70 percent which is also shown in Figure 4.

References

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Cited By

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  • (2022)Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT EnvironmentsACM Transactions on Management Information Systems10.1145/354385913:4(1-23)Online publication date: 10-Aug-2022
  • (2020)A load balance multi-scheduling model for OpenCL kernel tasks in an integrated clusterSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05152-825:1(407-420)Online publication date: 15-Jul-2020

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  1. Smart Scheduler for CUDA Programming in Heterogeneous CPU/GPU Environment

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      cover image ACM Other conferences
      ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation
      January 2019
      253 pages
      ISBN:9781450366199
      DOI:10.1145/3307363
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • University of Wollongong, Australia
      • College of Technology Management, National Tsing Hua University, Taiwan
      • Swinburne University of Technology
      • University of Technology Sydney

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 16 January 2019

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      Author Tags

      1. GPU Computing
      2. High Performance Computing
      3. Parallel Computing
      4. Scheduling Algorithms

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      ICCMS 2019
      ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation
      January 16 - 19, 2019
      QLD, North Rockhampton, Australia

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      View all
      • (2022)Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT EnvironmentsACM Transactions on Management Information Systems10.1145/354385913:4(1-23)Online publication date: 10-Aug-2022
      • (2020)A load balance multi-scheduling model for OpenCL kernel tasks in an integrated clusterSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05152-825:1(407-420)Online publication date: 15-Jul-2020

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