Analytical Performance Modeling and Validation of Intel’s Xeon Phi Architecture

Conference · 01 January 2017

DOI:https://doi.org/10.1145/3075564.3075593· OSTI ID:1351827

Chunduri, Sudheer; Balaprakash, Prasanna; Morozov, Vitali; Vishwanath, Venkatram; Kumaran, Kalyan

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 mini-benchmarks 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.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1351827
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
KNL
analytical modeling
benchmark
performance
projection