=================================HySoP (Hybrid Simulation with Particles) is a library dedicated to high performance direct numerical simulation of fluid related problems based on semi-lagrangian particle methods, for hybrid architectures providing multiple compute devices including CPUs, GPUs or MICs.
HySoP (Hybrid Simulation with Particles) is a library dedicated to high performance direct numerical simulation of fluid related problems based on semi-lagrangian particle methods, for hybrid architectures providing multiple compute devices including CPUs, GPUs or MICs.
The high level functionalities and the user interface are mainly written in Python using the object oriented programming model. This choice was made because of the large software integration benefits it can provide [Sanner 1999]. Moreover, the object oriented programming model offers a flexible framework to im- plement scientific libraries when compared to the imperative programming model [Arge et al. 1997][Cary et al. 1997]. It is also a good choice for the users as the Python language is easy to use for beginners while experienced programmers can pick it up very quickly [Oliphant 2007a]. The numerical solvers are mostly implemented using compiled languages such as Fortran, C/C++, or OpenCL for obvious performance reasons. It is also possible to implement numerical algorithms using directly Python, which is an interpreted language, hence slower for critical code paths under heavy arithmetic or memory load. The Python language support is however the key for rapid development cycles of experimental features. It also allows to easily implement routines that compute simulation statistics during runtime, relieving most of the user post-processing efforts and enabling live simulation monitoring.
The high level functionalities and the user interface are mainly written in Python using the object oriented programming model. This choice was made because of the large software integration benefits it can provide [Sanner 1999]. Moreover, the object oriented programming model offers a flexible framework to im- plement scientific libraries when compared to the imperative programming model [Arge et al. 1997][Cary et al. 1997]. It is also a good choice for the users as the Python language is easy to use for beginners while experienced programmers can pick it up very quickly [Oliphant 2007a]. The numerical solvers are mostly implemented using compiled languages such as Fortran, C/C++, or OpenCL for obvious performance reasons. It is also possible to implement numerical algorithms using directly Python, which is an interpreted language, hence slower for critical code paths under heavy arithmetic or memory load. The Python language support is however the key for rapid development cycles of experimental features. It also allows to easily implement routines that compute simulation statistics during runtime, relieving most of the user post-processing efforts and enabling live simulation monitoring.
