SoC Project: Distributed Physics

There was one project application in this area that is summarised here. This page contains the depersonalised content, which can be consolidated as necessary. The depersonalisation is for privacy reasons, credit is here due to those who spent the time writing these descriptions.

Project Title: Distributed physics

Benefits to Kamaelia:
By distributing the physical calculations across distributed components there will be no direct benefit to the BBC audience, but for BBC and everyone else from the community using Kamaelia. The reason for this is the introduction of dual- and quad-core processors into the home user market last or next year respectively. Software designed to run on one core only is expected to obtain serious performance problems or at least waste most of the available computation power. With a distributed physical component Kamaelia will be ready for this forthcoming challenge since this new component will speed up working with every software which uses the current physics component.

Synopsis:
The physics component in Kamaelia is used by different software, e.g. the Axon Visualiser. The goal of this project is to enhance the current physics component by parallelization. This will expose the computational power of the next generation processors decisively.

The Kamaelis physics component is (as a lot of other physics systems) a N-body simulation. The parallelisation of such systems has been an active area of research for a long time. Therefore a lot of different approaches have been published and some of them will be tested as part of this project. Furthermore it is very interesting to optimize the physics component not just by using distributed components but also optimizing the component itself. This could be done e.g. with the help of modern graphic processing units (GPUs) because physical calculations seem to fit to GPU architectures quite well [1].

Deliverables:
The deliverables are divided into two categories. The first category contains everything that will be delivered at the end of the project. The second category consists of optional components which will not all be delivered. The decision regarding the optional component will be made by weighing up the general usefulness for the Kamaelia project and the available time.

will be delivered:
- a distributed physics component
- unit tests proving the correct functionality of the distributed physics component compared to the current physics component
- a complex test case showing the possible speedup of the new component (maybe part of the unit tests)
- documentation describing the final parallelization approach and how the new component should to be used

optional:
- implementation of different parallelization approaches to see which of them fits best to Kamaelia
- optimizing the component itself e.g. with the use of GPUs
- - back porting the optimizations to the old physic component if there is still need for that component

Project Details:
At the beginning of the project I will improve my knowledge of Python/PyUnit and have a closer look at the Kamaelia source code to see how the current physics component and Kamaelia in general are implemented. After completing this first step I will write test cases (unit tests) based on the current physics component. These tests will later be used to verify that the new distributed physics component produces the same results as the old one. The tests itself will be written by using PyUnit.

If the unit tests have covered all relevant parts of the physics component, the development of the new distributed physics component will start. As almost every part of Kamaelia is written in Python, the distributed physics component will be written in Python, too. The communication between the components will be handled using the message system of Kamaelia. This will help keeping the Kamaelia design clear and easy to maintain.

When different parallelization ideas will be implemented, these will result in multiple new components for the time being. But at the end of development time, there will be only one of these coponents being chosen to be released as a part of Kamaelia. Releasing multiple components differing just in the algorithm normally results in more confusion for the developers using the Kamaelia library than it would help choosing the best fitting algorithm for the current situation.

Whereas the first implementation of the distributed physics component will be based on the idea of just dividing the world into N parts (with N being the number of components available) and distribute them across the multiple physics components, the optimized components could incorporate a lot of different algorithms like the methods by Barnes and Hut [2] or the fast multipole method [3].

When the physical calculations will be optimized by using a GPU this could be by the help of PyGPU [4] PyGPU seems to be in an early stage of development and needs to be evaluated first, but after all it looks promising.

Project Schedule:
After the brief outline of the schedule above, I give a more detailed schedule now:

      24.05.06 - Project start
until 07.06.06 - Improving my Python/PyUnit knowledge and have a close look at the Kamaelia source
until 17.06.06 - Writing unit tests for the physics component
      26.06.06 - Mid-term evaluation - There should be an early working distributed physics component be available, but it will not pass all test cases and may still contain some major bugs ("alpha" stage)
until 08.07.06 - Completion of the first distributed physics component; completing of all test cases
until 15.07.06 - Writing of a test case to be used for performance testing and comparing the speed of the old and the new versions of the physics component
until 07.08.06 - Working on the optional part - exact timetable depends on which optional deliverable will be worked on
until 14.08.06 - Writing the final documentation
until 21.08.06 - Polish both the code and the documentation.
      21.08.06 - Project end

References:
[1] http://developer.nvidia.com/object/havok-fx-gdc-2006.html
[2] 'A Hierarchical O(N log N) Force Calculation Algorithm', Josh Barnes & Piet Hut, 1986. Nature 324, 446.
[3] 'A Practical Comparison of N-Body Algorithms', Guy Blelloch and Girija Narlikar, 1997, Parallel Algorithms. Series in Discrete Mathematics and Theoretical Computer Science, Volume 30
[4] http://www.cs.lth.se/home/Calle_Lejdfors/pygpu/




 

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