TY - GEN
T1 - A customizable auto-tuning scenario with user-defined code transformations
AU - Takizawa, Hiroyuki
AU - Sato, Daichi
AU - Hirasawa, Shoichi
AU - Takahashi, Daisuke
N1 - Funding Information:
This work was partially supported by JST CREST “An Evolutionary Approach to Construction of a Software Development Environment for Massively-Parallel Heterogeneous Systems” and Grant-in-Aid for Scientific Research(B) #16H02822.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - At present, most of real-world HPC applications are being developed without considering any auto-tuning techniques; those applications are not 'gauto-tunable' for several reasons. One reason is that making a code auto-tunable often results in messing up the code and degrading the readability and/or maintainability. In our previous work, we have employed a code transformation framework, Xevolver, for making a code auto-tunable without messing it up. However, there is no standardized way to express the collaboration between code transformation and auto-tuning. In this paper, therefore, we design a standard tuning scenario and some directives to customize the scenario for individual applications. Our case studies show that the scenario can be reusable among different applications and different auto-tuning techniques by only partially customizing it. As a result, in terms of the number of code lines, the proposed approach requires much less programming effort for achieving auto-tuning.
AB - At present, most of real-world HPC applications are being developed without considering any auto-tuning techniques; those applications are not 'gauto-tunable' for several reasons. One reason is that making a code auto-tunable often results in messing up the code and degrading the readability and/or maintainability. In our previous work, we have employed a code transformation framework, Xevolver, for making a code auto-tunable without messing it up. However, there is no standardized way to express the collaboration between code transformation and auto-tuning. In this paper, therefore, we design a standard tuning scenario and some directives to customize the scenario for individual applications. Our case studies show that the scenario can be reusable among different applications and different auto-tuning techniques by only partially customizing it. As a result, in terms of the number of code lines, the proposed approach requires much less programming effort for achieving auto-tuning.
KW - Code transformation
KW - auto-tuning
KW - legacy application
UR - http://www.scopus.com/inward/record.url?scp=85028088134&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028088134&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2017.79
DO - 10.1109/IPDPSW.2017.79
M3 - Conference contribution
AN - SCOPUS:85028088134
T3 - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
SP - 1372
EP - 1378
BT - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
Y2 - 29 May 2017 through 2 June 2017
ER -