Comparison of Three Popular Parallel Programming Models on the Intel Xeon Phi

Author(s):  
Ashkan Tousimojarad ◽  
Wim Vanderbauwhede

2020 ◽  
Vol 23 (4) ◽  
pp. 866-886
Author(s):  
Vladimir Aleksandrovich Bakhtin ◽  
Dmitry Aleksandrovich Zakharov ◽  
Aleksandr Aleksandrovich Ermichev ◽  
Victor Alekseevich Krukov

DVM-system is designed for the development of parallel programs of scientific and technical calculations in the C-DVMH and Fortran-DVMH languages. These languages use a single DVMH-model of parallel programming model and are an extension of the standard C and Fortran languages with parallelism specifications in the form of compiler directives. The DVMH model makes it possible to create efficient parallel programs for heterogeneous computing clusters, in the nodes of which accelerators, graphic processors or Intel Xeon Phi coprocessors can be used as computing devices along with universal multi-core processors. The article describes the method of debugging parallel programs in DVM-system, as well as new features of DVM-debugger.



Author(s):  
С.Е. Киреев

На основе разработанного ранее программного комплекса для моделирования многофазных потоков в деформируемой пористой среде реализован новый параллельный программный комплекс, оптимизированный для запуска на кластере с ускорителями Intel Xeon Phi. Рассмотрены различные способы оптимизации, специфичные для данного ускорителя, и их влияние на время работы программы. Выполнено сравнение различных способов использования ускорителей в составе кластера: симметричного режима и режима offload. Получены оценки ускорения и эффективности при использовании различного числа узлов кластера. On the basis of a previously developed program for modeling the multiphase flows in finite-deformed porous media, a new parallel program optimized for clusters with Intel Xeon Phi accelerators is implemented. Several optimization techniques specific for such accelerators are considered and their effect on the program execution time is discussed. A comparison of the symmetric and offload programming models for the accelerators is performed. The parallelization speedup and efficiency are estimatedwhen using various numbers of cluster's nodes.



2020 ◽  
Vol 23 (3) ◽  
pp. 247-270
Author(s):  
Valery Fedorovich Aleksahin ◽  
Vladimir Aleksandrovich Bakhtin ◽  
Olga Fedorovna Zhukova ◽  
Dmitry Aleksandrovich Zakharov ◽  
Victor Alekseevich Krukov ◽  
...  

DVM-system is designed for the development of parallel programs of scientific and technical calculations in the C-DVMH and Fortran-DVMH languages. These languages use a single DVMH-model of parallel programming model and are an extension of the standard C and Fortran languages with parallelism specifications in the form of compiler directives. The DVMH model makes it possible to create efficient parallel programs for heterogeneous computing clusters, in the nodes of which accelerators, graphic processors or Intel Xeon Phi coprocessors can be used as computing devices along with universal multi-core processors. The article presents new features of DVM-system that have been developed recently.



2016 ◽  
Vol 10 (3) ◽  
pp. 14
Author(s):  
VEERASAMY BALA DHANDAYUTHAPANI ◽  
NASIRA G.M ◽  
◽  


2018 ◽  
Vol 175 ◽  
pp. 02009
Author(s):  
Carleton DeTar ◽  
Steven Gottlieb ◽  
Ruizi Li ◽  
Doug Toussaint

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.





Author(s):  
Arunmoezhi Ramachandran ◽  
Jerome Vienne ◽  
Rob Van Der Wijngaart ◽  
Lars Koesterke ◽  
Ilya Sharapov




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