VTK/GSoC 2015

Project ideas for the Google Summer of Code 2015

Guidelines

Students

These ideas were contributed by developers and users of VTK and ParaView. If you wish to submit a proposal based on these ideas you should contact the community members identified below to find out more about the idea, get to know the community member that will review your proposal, and receive feedback on your ideas.

The Google Summer of Code program is competitive, and accepted students will usually have thoroughly researched the technologies of their proposed project, been in frequent contact with potential mentors. Ideally students will have submitted a patch or two to their project, [instructions are here] as they will have to soon after being accepted, but it is not a requirement for the proposal. Kitware makes extensive use of mailing lists, and this would be your best point of initial contact to apply for any of the proposed projects. The mailing lists can be found on the project pages linked in the preceding paragraph. Please see GSoC proposal guidelines for further guidelines on writing your proposal.

Adding Ideas

When adding a new idea to this page, please try to include the following information:

• A brief explanation of the idea
• Expected results/feature additions
• Any prerequisites for working on the project
• Links to any further information, discussions, bug reports etc
• Any special mailing lists if not the standard mailing list for VTK
• Your name and email address for contact (if willing to mentor, or nominated mentor)

If you are not a developer for the project concerned, please contact a developer about the idea before adding it here.

Project Ideas

Project page, mailing lists, dashboard.

Shared Memory Parallelism in VTK

Brief explanation: Development of multi-threaded algorithms in VTK. Multiple R&D efforts are leading the creation of an infrastructure to support next generation multi-threaded parallel algorithm development in VTK. These efforts are based on modern parallel libraries such as Intel TBB and Inria KAAPI. The main goal of this project will be the development of algorithms that leverage this infrastructure. The focus will be on upgrading existing core algorithms such as iso-surfacing, clipping, cutting, warping etc. to be parallel. Ideally, this will include modernization of the old multi-threading code in the imaging pipeline. References:

• VTK SMP Wiki page
• VTK SMP paper
• Initial VTK SMP paper
• VTK multi-threaded image algorithm wiki page

Expected results: A number of algorithms that execute in parallel using shared memory. Scalability of new algorithms will have to be measured and documented. Development of regression tests and examples will be also expected.

Prerequisites: Experience in C++ and multi-threaded code development. Understanding of core visualization algorithms and data structures. Some experience in VTK ideally but not necessary.

Mentor: Berk Geveci (berk dot geveci at kitware dot com) and David Gobbi (david dot gobbi at gmail dot com)

Fine-Grained Parallelism in VTK-m

Brief explanation: VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures ( GPU's and Coprocessors's ). VTK-m is designed for fine-grained concurrency and provides abstract data and execution models that can be applied to a variety of algorithms. The goal of the project will be on developing algorithms such as Slice by implicit surface, Gradient, Streamlines, External Faces, Resample, etc. using VTKM-m data and execution model. See [1] for more information on VTK-m.

Expected results: A collection of algorithms that are processor architecture agnostic and execute on GPU's and Coprocessor's using VTK-m's data and execution model. Performance of new algorithms will have to be measured and documented. Development of regression tests and examples will be also expected.

Prerequisites: Experience in templated C++ development, visualization algorithms, data structures and highly parallel architectures such as GPU's. Some experience in VTK or CUDA or OpenCL would be ideal but not necessary.

Mentor: Robert Maynard (robert dot maynard at kitware dot com) and Kenneth Moreland (kmorel at sandia dot gov)

Computational Biology (Molecular Dynamics) In Situ Visualization

Brief explanation: Computational Biology involves using computer simulations to study biological problems using molecular dynamics and other techniques. Of particular interest is [GROMACS], a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles. It is primarily designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions. GROMACS is optimized to run on distributed memory clusters with recent support for GPU and SSE optimization. These GROMACS supercomputing simulations produce enormous (terabytes) file output to be analyzed post process by tools that only read the trajectory (position, velocity, and forces) or coordinate (molecular structure) information, and simply guess at the topology rather than using the simulations topology defined in GROMACS.

This project would provide a baseline implementation of ParaView Catalyst for molecular in situ visualization and data analysis embedded in GROMACS based on GROMACS' computed topology and trajectory information.

Expected results: The result would be ParaView Catalyst adaptors, example python scripts, and new advanced visualization techniques for GROMACS in order to enhance the computational biology workflow.

Prerequisites: C++ and python experience required, some experience with VTK and ParaView ideally, but not required.

Mentor: Marcus D. Hanwell (mhanwell at kitware dot com).

Templated Input Generator for VTK

Brief explanation: Build up an infrastructure that makes it trivial to bring new scientific data formats into VTK and ParaView. The infrastructure will handle the complexities of temporal support, parallel processing, composite data structures, ghost levels and the like, and provide easy to use entry points that bring data from the file or other source and populate VTK arrays.

Expected Results: A set of classes that can take an input specification and produce vtk data objects correctly and relatively efficiently. The input specification should be sufficiently abstracted from VTKs data types that users who understand the input format well won't have to understand VTK's complexities in order to use it.

Prerequisites: C++ and probably a scripting language such as Python or Lua.

References: http://www.paraview.org/Wiki/Writing_ParaView_Readers

Mentor(s): Robert Maynard (robert dot maynard at kitware dot com) and/or David DeMarle (dave dot demarle at kitware dot com)

Improvements to Earth and Space Science Visualization

Brief explanation: Add new capabilities to the existing VTK GeoVIS framework. In GSoC 2014 we built vtkMap that can support 2D visualization using OpenStreetMap and other data sources. This time we plan on adding 3D support for it. Also, we need to add support for overlay and clipping using shapefiles or other geographically references data, improved support for data types such as LAS, improve support rendering points, lines and polygons for Geovisualizations, and filters to performs clustering and classification etc are amongst few of them.

Expected results:: New VTK filters and mappers, few demos to showcase the new features.

Prerequisites: C/C++ experience, knowledge of earth science / GIS and preferably some experience with VTK

Mentor(s): Aashish Chaudhary (aashish dot chaudhary at kitware dot com)

CAD Model and Simulation Spline Visualization

Brief explanation: While spline curves and surfaces have been used for many years to describe CAD models, these flexible representations have often been discarded during the meshing and simulation process that is used to estimate the behavior of CAD-modeled parts in a particular environment or during a particular event. Recently, a group of techniques called IsoGeometric Analysis (IGA) has evolved in order to reduce the amount of work required to prepare simulations and to improve their accuracy. This project would develop support for arbitrary-dimensional, rational spline patches in VTK so that these simulations can be visualized properly. This may involve conversion to handle the variety of spline formats (T-Splines, NURBS, Catmull-Clark surfaces, etc.).

Expected results: The result would be a new mesh representation class, a reader, and a suite of 2-3 filters for contouring, cutting, and rendering these meshes.

Prerequisites: C/C++ experience, knowledge of rational splines and techniques for processing them (degree elevation, knot insertion/removal), and preferably some experience with VTK

Mentor(s): David Thompson (david dot thompson ta kitware dot com) and/or Bob O'Bara (bob dot obara at kitware dot com)

Supporting Solid Model Geometry in VTK

Brief explanation: Traditionally VTK has addressed the visualization needs of post-processed simulation information. Typically in these cases a tessellated mesh represents the geometric domain. This project will extend VTK's role in the simulation lifecycle by investigating approaches that will enable VTK to visualize the parametric boundary representation information used in solid modeling kernels such as CGM and OpenCASCADE (http://www.opencascade.org), which is typical pre-processing description of the geometric domain.

Expected results: A VTK module that interfaces with one or more solid modeling kernels.

Prerequisites: Experience in C++, and data structures. Some experience in VTK, parametric surfaces and solid modeling kernels ideal but not necessary.

Mentor: Bob O'Bara (bob dot obara at kitware dot com).

KiwiViewer on VTK

Brief explanation: KiwiViewer (http://www.kiwiviewer.org) is a model viewer for VTK datasets that runs on iOS and Android devices. It is built from a cross compiled version of an older release of VTK coupled with VES (http://www.vtk.org/Wiki/VES), a lightweight rendering library that runs on OpenGL ES. The most recent release of VTK supports iOS and Android directly, so bringing KiwiViewer up to date with full featured rendering would open up many visualization capabilities.

Expected results: A new version of KiwiViewer.

Prerequisites: Experience developing for mobile platforms and C++.

Mentor: Brad Davis (brad dot davis at kitware dot com).

OpenFOAM Catalyst adaptor

Brief explanation: OpenFOAM (http://www.openfoam.org) is a premier open source Computational Fluid Dynamics (CFD) simulation package. ParaView/Catalyst (http://www.paraview.org/Wiki/ParaView/Catalyst/Overview) is a VTK based in-situ visualization framework that tightly couples visualization capabilities to arbitrary simulation code. Updates to the data import path between OpenFOAM and VTK would give extreme scalability to OpenFOAM because data products would never need to be written to disk. It would also facilitate live data and computational steering connections that let the scientist see new results while they are being generated.

Expected results: A Catalyst adaptor contributed to either the OpenFOAM or ParaView communities. Two feasible starting points to begin the work are the existing vtkOpenFOAM readers and and the vtkFOAM FOAM-to-VTK exporter.

Prerequisites: Experience developing in C++, experience with CFD.

Mentor: Andy Bauer (andy dot bauer at kitware dot com) and Takuya Oshima (oshima at eng dot niigata-u dot ac dot jp)

Eulerian Magnification for Revealing Subtle Changes

Brief explanation: This project is based on the SIGGRAPH 2012 paper described at http://people.csail.mit.edu/mrub/evm/. The idea would be to develop a filter that would extract out the subtle changes in a time-dependent data set and amplify them. As a follow on, specialized views to show the changes and a feedback path from some of the existing views that show time varying data into the filter may be created.

Expected results: A filter in VTK to analyze the time-dependent data set.

Prerequisites: Experience developing in C++.

Mentor: David DeMarle (dave dot demarle at kitware dot com) and/or Andy Bauer (andy dot bauer at kitware dot com).

Parallel dataset partitioning

Brief explanation: Writing parallel applications using vtk requires the distribution of datasets between processes in such a way that map/reduce style operations can be performed with the minimal amount of communication between those processes. Source objects generally load pieces of data which can be processed independently by downstream filters, but sometimes the data loading produces overlapping pieces which must be repartitioned into spatially distinct regions. VTK contains a DistributedDataFilter (http://www.vtk.org/doc/release/5.2/html/a00336.html) to perform this operation, but it is not the most efficient implementation. Another more efficient implementation based on the Zoltan library from the Trilinos package is available (https://github.com/biddisco/pv-zoltan) but uses an outdated version of the Zoltan library and has high memory consumption during the partitioning phase. The newer Zoltan2 library allows generic programming techniques to be used and geometric data can be partitioned using zero copy methods. The objective of this project is to produce a data partitioning filter that can operate on all vtk dataset types using Zoltan2 and produce spatially distinct data as well as ghost regions when requested.

Expected results: A filter in VTK to partition unstructured and polygonal datasets in parallel.

Prerequisites: Experience developing in C++, MPI.

Mentor: John Biddiscombe (biddisco at cscs dot ch).

Direct mapped Polyhedral input cells from OpenFOAM

Brief explanation: OpenFOAM is an Open Source Computational Fluid Dynamics (CFD) package. OpenFOAM runs on unstructured meshes that are composed of polyhedral cells. Polyhedral support is now provided with VTK although this is not supported by all filters. The default option within the OpenFOAM reader is to decompose polyhedral cells into the other VTK primitive types. The OpenFOAM reader also lacks support for ghost cells when reading in parallel.

Expected results: An updated OpenFOAM reader with support for ghost cells when reading in parallel where the default output is a polyhedral cells. Test cases should be created for many of the common filters and polyhedral related bugs should be fixed.

Prerequisites: Experience developing in C++.

Mentor: Paul Edwards (paul dot m dot edwards at intel dot com)

Half Baked Ideas

(contact Dave DeMarle if you would like to work on one of these or an idea of your own and I will find you a good mentor to work out a solid GSoC proposal with)

• make concave polydata "just work" (i.e. render correctly) with minimal impact on common case speed

• an add on framework to help VTK using applications keep track of units

• anything from vtk user voice http://vtk.uservoice.com/forums/31508-general, except documentation (unfortunately) since docs effort is explicitly ruled out of GSoC

• anything from paraview user voice http://paraview.uservoice.com/forums/11350-general

• lua wrapping, lua programmable filters

• advanced rendering algorithms with OpenGL2 back end - Ambient occlusion, Shadows, Reflection, etc etc.

• interface to high quality rendering engines