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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1906.04286 |
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| _version_ | 1866912613558910976 |
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| author | Beltre, Angel Zaman, Shehtab Chiu, Kenneth Pamidighantam, Sudhakar Qiao, Xingye Govindaraju, Madhusudhan |
| author_facet | Beltre, Angel Zaman, Shehtab Chiu, Kenneth Pamidighantam, Sudhakar Qiao, Xingye Govindaraju, Madhusudhan |
| contents | Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. Furthermore, it allows users to better plan and schedule their research, streamlining workflows and improving the overall productivity of cyberinfrastructure. Predicting run time is challenging, however. The inputs to scientific codes can be complex and high dimensional. Their relationship to the run time may be highly non-linear, and, in the most general case is completely arbitrary and thus unpredictable (i.e., simply a random mapping from inputs to run time). Most codes are not so arbitrary, however, and there has been significant prior research on predicting the run time of applications and workloads. Such predictions are generally application-specific, however. In this paper, we focus on the Gaussian computational chemistry code. We characterize a data set of runs from the SEAGrid science gateway with a number of different studies. We also explore a number of different potential regression methods and present promising future directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1906_04286 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway Beltre, Angel Zaman, Shehtab Chiu, Kenneth Pamidighantam, Sudhakar Qiao, Xingye Govindaraju, Madhusudhan Computational Physics Performance Machine Learning Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. Furthermore, it allows users to better plan and schedule their research, streamlining workflows and improving the overall productivity of cyberinfrastructure. Predicting run time is challenging, however. The inputs to scientific codes can be complex and high dimensional. Their relationship to the run time may be highly non-linear, and, in the most general case is completely arbitrary and thus unpredictable (i.e., simply a random mapping from inputs to run time). Most codes are not so arbitrary, however, and there has been significant prior research on predicting the run time of applications and workloads. Such predictions are generally application-specific, however. In this paper, we focus on the Gaussian computational chemistry code. We characterize a data set of runs from the SEAGrid science gateway with a number of different studies. We also explore a number of different potential regression methods and present promising future directions. |
| title | Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway |
| topic | Computational Physics Performance Machine Learning |
| url | https://arxiv.org/abs/1906.04286 |