TY - GEN
T1 - Analytical performance modeling and validation of Intel's Xeon Phi architecture
AU - Chunduri, Sudheer
AU - Balaprakash, Prasanna
AU - Morozov, Vitali
AU - Vishwanath, Venkatram
AU - Kumaran, Kalyan
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
PY - 2017/5/15
Y1 - 2017/5/15
N2 - Modeling the performance of scientific applications on emerging hardware plays a central role in achieving extreme-scale computing goals. Analytical models that capture the interaction between applications and hardware characteristics are attractive because even a reasonably accurate model can be useful for performance tuning before the hardware is made available. In this paper, we develop a hardware model for Intel's second-generation Xeon Phi architecture code-named Knights Landing (KNL) for the SKOPE framework. We validate the KNL hardware model by projecting the performance of minibenchmarks and application kernels. The results show that our KNL model can project the performance with prediction errors of 10% to 20%. The hardware model also provides informative recommendations for code transformations and tuning.
AB - Modeling the performance of scientific applications on emerging hardware plays a central role in achieving extreme-scale computing goals. Analytical models that capture the interaction between applications and hardware characteristics are attractive because even a reasonably accurate model can be useful for performance tuning before the hardware is made available. In this paper, we develop a hardware model for Intel's second-generation Xeon Phi architecture code-named Knights Landing (KNL) for the SKOPE framework. We validate the KNL hardware model by projecting the performance of minibenchmarks and application kernels. The results show that our KNL model can project the performance with prediction errors of 10% to 20%. The hardware model also provides informative recommendations for code transformations and tuning.
KW - Analytical modeling
KW - Benchmark
KW - KNL
KW - Performance
KW - Projection
UR - http://www.scopus.com/inward/record.url?scp=85027039326&partnerID=8YFLogxK
U2 - 10.1145/3075564.3075593
DO - 10.1145/3075564.3075593
M3 - Conference contribution
AN - SCOPUS:85027039326
T3 - ACM International Conference on Computing Frontiers 2017, CF 2017
SP - 247
EP - 250
BT - ACM International Conference on Computing Frontiers 2017, CF 2017
PB - Association for Computing Machinery, Inc
T2 - 14th ACM International Conference on Computing Frontiers, CF 2017
Y2 - 15 May 2017 through 17 May 2017
ER -