triadabucks.blogg.se

1. #Data and model coupling pianoteq software#
2. #Data and model coupling pianoteq code#
3. #Data and model coupling pianoteq Pc#

We report preliminary performance results for the MCT interpolation benchmark. We describe the C++ and Python versions of the MCT programming model and provide short examples. This approach enables an object-based programming model for implementing parallel couplings in non-Fortran coupled systems and in systems with language hetero-geneity. We describe the method used to generate the interlanguage bindings. This programming model's capabilities reach beyond MCT's native Fortran implementation to include bindings for the C++ and Python programming languages. We have created a multilingual parallel coupling pro-gramming model based on a successful open-source parallel coupling library, the Model Coupling Toolkit (MCT). An additional coupling problem in these systems is language interoperability between their constituent codes.

Parallel coupling is emerging as a new grand challenge in computational science as scientists attempt to build multiscale and multiphysics systems on parallel platforms. On distributed-memory parallel platforms, the coupling problem is complicated by the need to describe, transfer, and transform distributed data-known as the parallel coupling problem.

#Data and model coupling pianoteq software#

Multiphysics and multiscale simulation systems share a common software requirement-infrastructure to implement data exchanges between their constituent parts-often called the coupling problem. We evaluate the performance and scalability of the coupling framework using a specific coupling scenario, on the Jaguar Cray XT5 system at Oak Ridge National Laboratory. The framework enables flexible coupling behaviors that are asynchronous in time and space, and it supports dynamic coupling between heterogeneous simulation processes without enforcing any synchronization constraints. The framework is driven by the coupling requirements of the Center for Plasma Edge Simulation, and it provides simple coupling abstractions as well as efficient asynchronous (RDMA-based) memory-to-memory data transport mechanisms that complement existing parallel programming systems and data sharing frameworks. In this paper, we present the design, implementation and evaluation of a memory-to-memory coupling framework for coupled scientific simulations on high-performance parallel computing platforms. Efficient and scalable parallel implementations of these coupled simulations present challenging interaction and coordination requirements, especially when the coupled physical processes are computationally heterogeneous and progress at different speeds. Scientific applications are striving to accurately simulate multiple interacting physical processes that comprise complex phenomena being modeled. Some first experiments validate our approach. These components are set up in an XML file processed by FvSciViz's automatic tools in order to build the application. The application template is based on generic components handling interaction, visualization and simulation.

#Data and model coupling pianoteq code#

FVmoduleAPI encapsulates FlowVR technicalities in few simple functions inserted in the original code written in C, C++ or Fortran, to make it compatible with the programming model of the FlowVR middleware. FvSciViz provides an API, called FVmoduleAPI, to build FlowVR components, a template of an interactive scientific visualization application and automatic tools to generate the application defined by the user.

In this paper, we present FvSciViz, a generic framework based on FlowVR, to build interactive scientific visualization from heterogeneous components.

#Data and model coupling pianoteq Pc#

FlowVR is a component-based middleware providing the means to develop and run high performance VR or Scientific Visualization applications on PC cluster or grid architecture.