We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse matrix-vector operations often suffer from an unfavorable communication to computation ratio. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. We compare our approach to pure MPI and the widely used "vector-like" hybrid programming strategy.
Integrating High Performance In-Memory Data Streaming and In-Situ Visualization in Hybrid MPI+OpenMP PIC MC Simulations Towards Exascale
KTH Royal Institute of Technology（皇家理工学院）；Faculty of Mechanical Engineering, University of Ljubljana（卢布尔雅那大学机械工程学院）；Max Planck Institute for Plasma Physics（马普等离子体物理研究所）；Institute of Plasma Physics of the CAS（捷克科学院等离子体物理研究所）；University of Oregon（俄勒冈大学）；Max Planck Computing and Data Facility（马普计算与数据中心）
Integrating State of the Art Compute, Communication, and Autotuning Strategies to Multiply the Performance of the Application Programm CPMD for Ab Initio Molecular Dynamics Simulations
Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation
Erlangen Regional Computing Center（埃尔朗根区域计算中心）；Ernst-Moritz-Arndt University of Greifswald（格赖夫斯瓦尔德恩斯特·莫里茨·阿恩特大学）；German Aerospace Center (DLR)（德国航空航天中心）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
