Hi, after getting some good amount of positive feedback on our talk at boostcon we are now asking for a formal review process of the odeint library, currently hosted on github: https://github.com/headmyshoulder/odeint-v2 odeint provides routines for numerically solving Ordinary Differential Equations, a task heavily required when approaching scientific problems numerically. In odeint we have implemented the most common algorithms in a modern, generic way which provides several advantages over existing implementations. The main focus of odeint is to separate algorithms from the underlying basic computation unit. This allows for example to easily use the power of modern GPUs via CUDA or OpenCL to speed up computational heavy simulations. Features: - provides most of the standard algorithms, including step size control, dense ouput and extrapolation routines - symplectic routines for Hamiltonian systems - implicit routines based on boost::ublas (MTL4 in progress) - high level integrate functions - interchangeable computation backend with existing bindings to CUDA and OpenCL - native support of ODEs defined for complex values - support of Boost.Units in most of the algorithms - support of arbitrary precision types The code is hosted on github: https://github.com/headmyshoulder/odeint-v2 Documentation/Tutorial: http://headmyshoulder.github.com/odeint-v2/doc/index.html We are, of course, looking for a review manager as well. Thanks! Karsten and Mario
Hello Mario, I have added ODEint to the review schedule. Thanks, Ron On Jul 2, 2012, at 7:05 AM, Mario Mulansky wrote:
Hi,
after getting some good amount of positive feedback on our talk at boostcon we are now asking for a formal review process of the odeint library, currently hosted on github: https://github.com/headmyshoulder/odeint-v2
odeint provides routines for numerically solving Ordinary Differential Equations, a task heavily required when approaching scientific problems numerically. In odeint we have implemented the most common algorithms in a modern, generic way which provides several advantages over existing implementations. The main focus of odeint is to separate algorithms from the underlying basic computation unit. This allows for example to easily use the power of modern GPUs via CUDA or OpenCL to speed up computational heavy simulations.
Features: - provides most of the standard algorithms, including step size control, dense ouput and extrapolation routines - symplectic routines for Hamiltonian systems - implicit routines based on boost::ublas (MTL4 in progress) - high level integrate functions - interchangeable computation backend with existing bindings to CUDA and OpenCL - native support of ODEs defined for complex values - support of Boost.Units in most of the algorithms - support of arbitrary precision types
The code is hosted on github: https://github.com/headmyshoulder/odeint-v2
Documentation/Tutorial: http://headmyshoulder.github.com/odeint-v2/doc/index.html
We are, of course, looking for a review manager as well.
Thanks!
Karsten and Mario
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On Mon, 2 Jul 2012, Mario Mulansky wrote:
after getting some good amount of positive feedback on our talk at boostcon we are now asking for a formal review process of the odeint library, currently hosted on github: https://github.com/headmyshoulder/odeint-v2
I found your page comparing performance with other Runge-Kutta implementations. http://headmyshoulder.github.com/odeint-v2/performance.html The documentation indicates you have support for various types of stepper. http://headmyshoulder.github.com/odeint-v2/doc/boost_numeric_odeint/odeint_i... Have you considered whether the odeint API could act as an interface to CVODE or CVODES in Sundials? https://computation.llnl.gov/casc/sundials/main.html If that works, then it validates the odeint API. If there are gaps or inefficiencies, then they may indicate improvements for odeint. Thanks, Daniel
On 08/29/2012 03:21 PM, Daniel Herring wrote:
On Mon, 2 Jul 2012, Mario Mulansky wrote:
after getting some good amount of positive feedback on our talk at boostcon we are now asking for a formal review process of the odeint library, currently hosted on github: https://github.com/headmyshoulder/odeint-v2
I found your page comparing performance with other Runge-Kutta implementations.
http://headmyshoulder.github.com/odeint-v2/performance.html
The documentation indicates you have support for various types of stepper.
http://headmyshoulder.github.com/odeint-v2/doc/boost_numeric_odeint/odeint_i...
Have you considered whether the odeint API could act as an interface to CVODE or CVODES in Sundials?
Good question. We have not checked this. But I don't see any point why one can not implement a odeint-compatible stepper wrapping CVODE or CVODES. Of course, there might be some subtle pitfalls with these packages which make the wrapper very complicated or inefficient. It is an interesting (and open) question.
https://computation.llnl.gov/casc/sundials/main.html
If that works, then it validates the odeint API. If there are gaps or inefficiencies, then they may indicate improvements for odeint.
Thanks, Daniel
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participants (4)
-
Daniel Herring -
Karsten Ahnert -
Mario Mulansky -
Ronald Garcia