MuPIF is modular, object-oriented integration platform allowing to create complex, distributed, multiphysics simulation workflows across the scales and processing chains by combining existing simulation tools. MuPIF uses Python 3.5 standards and is independent on operating system (Linux, UNIX, Windows, Mac, …).
A reliable multiscale/multiphysics numerical modeling requires including all relevant physical phenomena along the process chain, typically involving multiple scales, and the combination of knowledge from multiple fields. A pragmatic approach lies in combining existing tools, The approach followed in MuPIF is based on an system of distributed, interacting components designed to solve given problem. The individual components represent different entities, including individual simulation packages and simulation workflows, high level data, external databases, etc. The top-level abstract classes are introduced covering all entities in the model space, defining common interfaces that need to be implemented by any derived class, representing particular implementation of specific component. Such interface concept allows using any derived class on a very abstract level, using common services defined by abstract class, without being concerned with the implementation details of an individual software component. This essentially allows to manipulate all models, data, and workflows using standardized interfaces. Moreover, as the complex simulation data are represented by objects as well, the platform is independent on particular data format(s), as they can be manipulated using same abstract interfaces. Therefore, the focus is on services provided by objects (object interfaces) and not on underlying object's data. In addition, the distributed design allows to treat local as well as remote objects using the same interface making the platform naturally distributed.
Even though the platform can be used locally on a single computer orchestrating installed applications, the real strength of the MMP platform is its distributed design, allowing to execute simulation scenarios involving remote applications. MuPIF provides a transparent distributed object system, which takes care of the network communication between the objects when they are distributed over different machines on the network. One can just call a method on a remote object as if it were a local object – the use of remote objects is (almost) transparent. This is achieved by using the concept of proxies representing remote objects, which forward the calls to the remote objects and pass the results back to the calling code. In this way, there is no difference between simulation script for local or distributed case, except for the initialization, where, instead of creating local object, one has to connect to the remote object.
Rather than writing programs, the Python language is extended by modules, representing interfaces to existing codes and data structures with specific functionality. The emphasis is on building infrastructure to facilitate the implementation of multi-physic and multi-level simulations. The high-level language serves as a “glue” to tie the modules or components together, to create specialized application. Python language provides the flexibility, interactivity, and extensibility needed for such an approach, thanks to its concise and pseudocode-like syntax, modularity and object-oriented design, introspection and self documentation capability, and the availability of a Numerics extension allowing the efficient storage and manipulation of large amounts of numerical data. The application interface can be conveniently realized by wrapping application code. The process of wrapping code can be automated to a fair extent using SWIG, Boost, or similar tools, which can generate wrapper code for several languages. This approach also allows a single source version of the component code to be maintained. The simulation workflows are implemented as Python scripts built on top of MuPIF. The graphical workflow editor is available to make the workflow implementation more accessible and convenient.
The easiest installation happens through Python Package Index (pip) which takes care of dependencies and installs/updates missing modules automatically. Run as a command
pip install mupif
Alternatively, you may download and install MuPIF from git repository. You have to take care yourself on depending modules (Pyro4, numpy, scipy, setuptools, enum34, pyvtk). Note that since June, 2017 the git repository has been migrated to GitHub.
MuPIF is available under GNU Library or Lesser General Public License version 3.0 (LGPLv3)
The GitHub issue subsystem to report any bugs or get a support MuPIF GitHub page
We offer intensive, one-day course on multi-scale and multi-physics modeling using MuPIF platform. The course covers following topics:
The cost of the course is 400 EUR/person. Course will be held in Prague at the Czech Technical University. Minimum of 5 participants per course.