- Implementation of symplectic solvers
- Implementation of implicit solvers.
- Implementation of global and diffusional coupling of identical systems.
- Support for delayed differential euqations
Version 2.1 (October 10, 2019)
- With Template Metaprogramming, the code is fully templatized to generate highly specialised solver code during compile time (as a function of the algorithm and the necessity of event handling and dense output). Accordingly, the file system is reworked.
- Small extension: possibility to use integers shared parameters and integer accessories to be able to achieve complex indexing techniques efficiently for complicated systems.
Version 2.0 (August 13, 2019)
- Dense output is now supported with few limitations, see the manual. This is a prerequisit e.g. for solving delay differential equations.
- The code and its interface is greatly simplified and cleared. For instance, the Problem Pool is completely omitted from the code (it was kept for historical reason), and many possible options are now bound to the Solver Object that can be setup all with a single member function.
- The manual is also restructured and simplified according to the feedbacks.
Version 1.1 (April 9, 2019)
- A device (GPU) can be associated to each Solver Object. Thus, device selection is now handled automatically.
- A CUDA stream is automatically created for each Solver Object.
- New set of member functions to overlap CPU-GPU computations, and to easily distribute workload to different GPUs in a single node. This includes asynchronous memory and kernel operations, and synchronisation possibilities between CPU threads and GPU streams.
- An active number of threads variable can be specified in each integration phase to handle the tailing effect comfortably.
- Two new tutorial examples are added: a) overlapping CPU and GPU computations using multiple Solver Objects b) using multiple GPUs available in a single machine/node.
Version 1.0 (February 14, 2019)
- The code is designed to solve a huge number of independent but identical (the parameter sets and the initial conditions can be different) ODE systems on GPUs.
- Improved user-friendliness. Even those who are new to C++ programming, only a short course is more than enough to use the program package.
- There is a detailed manual with tutorial examples. Therefore, the user can easily build-up its own project by copy-paste code blocks.
- Possibility to distribute tasks on multiple GPUs.
- Efficient and robust event handling.
- User-defined action after every time step for flexibility.
- User-defined "interactions" after every successful time step or event handling (very useful e.g. for impact dynamics, see the tutorial examples in the manual).
- Possibility to utilize the GPU's memory hierarchy without explicit knowledge on the details.
- User-programmable parameter for flexible implementations and storing special property of a trajectory.
- Only explicit solvers: 4th order Runge-Kutta with a fixed time step, and 4th order Runge-Kutta-Cash-Karp method with 5th order embedded error estimation. (due to the complex control flow of implicit solvers, explicit solver sometimes performs better than the implicit ones even for stiff problems).
- Only double precision arithmetic operations are supported.
- Storing only the endpoints of each integration phase (in order to improve speed). However, this is rarely a problem, as the user-programmable parameters and the aforementioned user-defined interactions allow to store the most complex properties of a trajectory, see the documentation.