Running large‐scale CFD applications on Intel‐KNL–based clusters
Abstract
Intel's latest Xeon Phi processor, Knights Landing (KNL), has the potential to provide over 2.6 TFLOPS. However, to obtain maximum performance on the KNL, significant refactoring and optimization of application codes are still required to exploit key architectural innovations that KNL features—wide vector units, many‐core node design, and deep memory hierarchy. The experience and insights gained in porting and running FEFLO (a typical edge‐based finite element code for the solution of compressible and incompressible flows) on the KNL platform are described in this paper. In particular, optimizations used to extract on‐node parallelism via vectorization and multithreading and improve internode communication are considered. These optimizations resulted in a 2.3× performance gain on a 16 node runs of FEFLO, with the potential for larger performance gains as the code is scaled beyond 16 nodes. The impact of the different configurations of KNL's on‐package MCDRAM (Multi‐Channel DRAM) memory on FEFLO's performance is also explored. Finally, the performance of the optimized versions of FEFLO for KNL and Haswell (Intel Xeon) is compared.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 14, 2017
- Source ID
- 10.1002/fld.4474
Entities
People
- Adam Jundt
- Allyson Cauble‐chantrenne
- Ananta Tiwari
- Joseph D. Baum
- Joshua Peraza
- Laura Carrington
- Rainald Löhner
Organizations
- Air Force Office of Scientific Research
- George Mason University