Google Summer of Code is a program that offers student developers stipends to write code for various open source projects. Google will be working with several open source, free software, and technology-related groups to identify and fund several projects over a three month period.
- 1 Welcome to Madagascar's Google Summer of Code Page
- 2 Project Ideas
Welcome to Madagascar's Google Summer of Code Page
See also feature request tracker
Madagascar, an open source project, is a leading participant in the Open Research movement. As described on Wikipedia, the central theme of open research is to make clear accounts of the methodology, along with data and results extracted therefrom, freely available via the internet. This permits a massively distributed collaboration.
Its design is based on a few simple and powerful principles.
From the coder's point of view, Madagascar is written in C and in Python. The C library is a very loosely coupled set of unix-style filters, transforming stdin to stdout. The Python is mostly an implementation of a custom build system on top of the rule based build system SCons.
Seismic data processing consists of a sequence of steps. Madagascar's filter-based design allows such sequences to be easily composed and abstracted. A key advantage of the Madagascar system is that the computational pipeline is also construed as a build system. Modifications to intermediate steps automatically reinvoke only necessary computations and skip over up-to-date ones, just as a more conventional build system would recompile modules whose code had been touched while reusing modules which are newer than their source. Madagascar extends this model all the way from raw data to publication.
This strategy is a key to reproducibility. By maintaining scripts which contain all transformations from raw data to final publication quality document, Madagascar supports repeatability and testing of scientific computations, thus advancing the collaborative nature of science in the same way that open source advances the collaborative nature of computing.
Directions in which Madagascar is expanding include visualization, parallelization, and user interfaces.
See also feature request tracker
Graphical User Interface (Mentor: Sergey Fomel)
- Add an option to sfdoc to output spec files in the format defined for TKSU. This should make TKSU immediately applicable. Spec files can be generated automatically at the compile time.
- Rewrite TKSU in Python, possibly using TkInter
- See http://sourceforge.net/forum/forum.php?thread_id=1579059&forum_id=552249 for more discussions.
- Investigate alternative solutions.
Data Visualization (Mentor: Vladimir Bashkardin)
- Migrate 2D rendering OpenGL-based code from GSEGYView to Madagascar and create an interactive viewer with zooming/panning features.
- Migrate 3D rendering GLSL-based code from GSEGYView to Madagascar and create a viewer with the support of pluggable shader programs.
- Finish 3D rays viewer
- Create a set of alternatives to sfgraph, sfgrey, sfcontour programs, that would use PLPLOT library instead of VPlot; also, create "pens", that could read from those programs and generate ps, pdf, png output; analyze flexibility of PLPLOT and the possibility to fully mimic VPlot's output (including animation).
Java API (Mentor: Undefined)
- Add a Java interface to other supported interfaces
- Possibly use JNI
- Investigate possible connections with Mines JTK and JavaSeis
Seismic I/O Library (Mentor: Bert Bril)
- See Seismic Library
Binary Packages (Mentor: Nick Vlad)
- Generate binary packages to simplify installation on multiple platforms.
- Given Madagascar's dependencies, and a standardized way of finding other package's dependencies come up with a way/apply a tool to determine the minimum number of packages that make a self-contained Linux distributions that runs Madagascar. Build such a distribution starting from an existing well-supported distribution. Build a virtual appliance from that distribution.
Geophysics / Numerical Analysis (Mentor: Paul Sava)
- Implement an optimal algorithm for parallel transposes of arrays with 4 or 5 dimensions, up to a few tens of terabytes in volume, on a multi-node Linux cluster
- As a bonus, FFT one of the transposed dimensions
- Implement a hardware-adaptive transpose algorithm for a 1-node, SMP machine of 8 nodes or more. Investigate speed of transfers, size of caches, memory arrangement, etc, and make it hardware-adaptive. Bonus for out-of-core capabilities.
- Implement 3-D seismic data header storage using the fastest open-source database, then compare header I/O times with the classic approach of having a simple table. Which is the fastest way of implementing a large database knowing that the values it will hold are all bools, ints and floats?