Performance Evaluation and Scalability Analysis of NPB-MZ on Intel Xeon Phi Coprocessor

2013 ◽  
pp. 143-152 ◽  
Author(s):  
Yuqian Li ◽  
Yonggang Che ◽  
Zhenghua Wang
Keyword(s):  
Performance Evaluation ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Scalability Analysis ◽  
Intel Xeon

Performance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi

2013 ◽  
Author(s):  
Arunmoezhi Ramachandran ◽  
Jerome Vienne ◽  
Rob Van Der Wijngaart ◽  
Lars Koesterke ◽  
Ilya Sharapov
Keyword(s):  
Performance Evaluation ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Intel Xeon

scholarly journals Performance Evaluation of Multithreaded Geant4 Simulations Using an Intel Xeon Phi Cluster

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
P. Schweitzer ◽  
S. Cipière ◽  
A. Dufaure ◽  
H. Payno ◽  
Y. Perrot ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Computing Time ◽  
Hardware Accelerators ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Electron Dose ◽  
Distributed Execution ◽  
Intel Xeon

The objective of this study is to evaluate the performances of Intel Xeon Phi hardware accelerators for Geant4 simulations, especially for multithreaded applications. We present the complete methodology to guide users for the compilation of their Geant4 applications on Phi processors. Then, we propose series of benchmarks to compare the performance of Xeon CPUs and Phi processors for a Geant4 example dedicated to the simulation of electron dose point kernels, the TestEm12 example. First, we compare a distributed execution of a sequential version of the Geant4 example on both architectures before evaluating the multithreaded version of the Geant4 example. If Phi processors demonstrated their ability to accelerate computing time (till a factor 3.83) when distributing sequential Geant4 simulations, we do not reach the same level of speedup when considering the multithreaded version of the Geant4 example.

scholarly journals A performance evaluation of CCS QCD Benchmark on Intel Xeon Phi (KNC) systems

2016 ◽  
Author(s):  
Ken-Ichi Ishikawa ◽  
Taisuke Boku ◽  
Yoshinobu Kuramashia ◽  
Lawrence Meadows ◽  
Michael D‘Mello ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Intel Xeon ◽  
A Performance

Performance evaluation of R with Intel Xeon Phi coprocessor

2013 ◽  
Author(s):  
Yaakoub El-Khamra ◽  
Niall Gaffney ◽  
David Walling ◽  
Eric Wernert ◽  
Weijia Xu ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Intel Xeon

scholarly journals MILC Code Performance on High End CPU and GPU Supercomputer Clusters

2018 ◽  
Vol 175 ◽  
pp. 02009
Author(s):  
Carleton DeTar ◽  
Steven Gottlieb ◽  
Ruizi Li ◽  
Doug Toussaint
Keyword(s):  
Conjugate Gradient ◽  
Memory Hierarchy ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Code Performance ◽  
Recent Developments ◽  
Knights Landing ◽  
Many Core ◽  
Intel Xeon

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

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