@inproceedings{58ef935cc3c04be98cd56b4752f06309,
title = "Assessing representativeness of kernels using descriptive statistics",
abstract = "A kernel or mini-App is a self-contained small application that retains certain characteristics of the original application [7]. Working on a kernel or mini-App in the place of the original application can dramatically reduce the resources and effort required for performing software tasks such as performance optimization and porting to new platforms. However, using kernel as a proxy is based on the assumption that it represents the original application in the context of how it is being used. In this paper, we introduce an extension to the Fortran Kernel Generator (KGen) which is an automated kernel extraction tool [1]. The extension allows comparison of the execution characteristics between the original application and the generated kernel using descriptive statistics. From the comparison, the user is provided with statistics that provide information on the degree and context of representativeness of the kernel. KGen also utilizes the information generated to help it to automatically improve representativeness of the kernels whilst reducing the size of the workload generated. We applied this extension to three kernels. One is generated from a Fortran scientific library and the remaining two are generated from an earth system model. We have demonstrated that the descriptive statistics provided in the enhancement provide not only quantitative metrics and context of representativeness but also a way to improve the quality of representativeness of the kernels generated.",
keywords = "Automated kernel extraction, Codesign, Descriptive statistics, Kernel, Python, Representativeness",
author = "Youngsung Kim and Dennis, \{John M.\} and Christopher Kerr",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 ; Conference date: 05-09-2017 Through 08-09-2017",
year = "2017",
month = sep,
day = "22",
doi = "10.1109/CLUSTER.2017.117",
language = "English",
series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "818--825",
booktitle = "Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017",
address = "United States",
}