A Study on Scalability of Profiling Method Based on Hardware Performance Counter for Optimal Execution of Supercomputer


KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 10, pp. 221-230, Oct. 2020
https://doi.org/10.3745/KTCCS.2020.9.10.221,   PDF Download:
Keywords: Profiling, scalability, Supercomputer, Hardware Performance Counter, job scheduling
Abstract

Supercomputer that shares limited resources to multiple users needs a way to optimize the execution of application. For this, it is useful for system administrators to get prior information and hint about the applications to be executed. In most high-performance computing system operations, system administrators strive to increase system productivity by receiving information about execution duration and resource requirements from users when executing tasks. They are also using profiling techniques that generates the necessary information using statistics such as system usage to increase system utilization. In a previous study, we have proposed a scheduling optimization technique by developing a hardware performance counter-based profiling technique that enables characterization of applications without further understanding of the source code. In this paper, we constructed a profiling testbed cluster to support optimal execution of the supercomputer and experimented with the scalability of the profiling method to analyze application characteristics in the built cluster environment. Also, we experimented that the profiling method can be utilized in actual scheduling optimization with scalability even if the application class is reduced or the number of nodes for profiling is minimized. Even though the number of nodes used for profiling was reduced to 1/4, the execution time of the application increased by 1.08% compared to profiling using all nodes, and the scheduling optimization performance improved by up to 37% compared to sequential execution. In addition, profiling by reducing the size of the problem resulted in a quarter of the cost of collecting profiling data and a performance improvement of up to 35%.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
J. Choi, G. Park, S. Rho and C. Park, "A Study on Scalability of Profiling Method Based on Hardware Performance Counter for Optimal Execution of Supercomputer," KIPS Transactions on Computer and Communication Systems, vol. 9, no. 10, pp. 221-230, 2020. DOI: https://doi.org/10.3745/KTCCS.2020.9.10.221.

[ACM Style]
Jieun Choi, Guenchul Park, Seungwoo Rho, and Chan-Yeol Park. 2020. A Study on Scalability of Profiling Method Based on Hardware Performance Counter for Optimal Execution of Supercomputer. KIPS Transactions on Computer and Communication Systems, 9, 10, (2020), 221-230. DOI: https://doi.org/10.3745/KTCCS.2020.9.10.221.