What is ppOpen-HPC

ppOpen-HPC is an open source infrastructure for the development and execution of optimized and reliable simulation code on post-peta-scale (pp) parallel computers based on many-core architectures comprising various types of libraries that cover general procedures for scientific computations. Source code developed on a PC with a single processor is linked to these libraries, and the generated parallel code is optimized for post-peta-scale systems. The target post-peta-scale systems are many-core-based supercomputers, such as the Oakforest-PACS with 8,208 Intel Xeon Phi (Knights Landing) operated by Joint Center for Advanced High Performance Computing (JCAHPC). ppOpen-HPC is part of a five-year project (FY.2011-FY.2015) spawned from the “Development of System Software Technologies for Post-Peta Scale High-Performance Computing” project funded by JST-CREST. The framework covers various types of procedures for scientific computations, such as the parallel I/O of distributed datasets, matrix assembly, linear solvers with practical and scalable preconditioners, visualization, adaptive mesh refinement, and dynamic load balancing, in various types of computational models, such as FEM, FDM, FVM, BEM, and DEM. Automatic tuning (AT) technology enables the automatic generation of optimized libraries and applications under various types of environments. We released the most updated version of ppOpen-HPC as an open source software every year in November from 2012 to 2015.

In 2016, the ppOpen-HPC team joined the Equipping Sparse Solvers for Exascale (ESSEX-II) project (led by P.I. Professor Gerhard Wellein of the University of Erlangen-Nuremberg) , which is funded by JST-CREST and the German DFG priority program 1648 Software for Exascale Computing (SPPEXA) under a Japan (JST)-Germany (DFG) collaboration, which continues until FY.2018. In the ESSEX-II project, we are developing pK-Open-HPC (an extended version of ppOpen-HPC, which is a framework for exa-feasible applications), preconditioned iterative solvers for quantum sciences, parallel reordering methods, fault resilient capabilities with checkpoint restarting and a framework for AT with a performance model.

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