Python library for ODE integration via Taylor's method and LLVM
MPL-2.0 License
Published by bluescarni over 2 years ago
This new version of heyoka.py comes with a big new feature and several fixes.
heyoka.py is now capable of automatically performing multithreaded fine-grained parallelisation within an individual integration step. Parallel mode is easily enabled, and it can lead to substantial speed-ups, especially for large ODE systems and/or extended precision computations.
A tutorial exploring this new feature is available here:
https://bluescarni.github.io/heyoka.py/notebooks/parallel_mode.html
propagate_grid()
were identified and fixed.The full changelog, as usual, is available here:
Published by bluescarni over 2 years ago
This new version of heyoka.py comes with a big new feature, several quality-of-life improvements and a few fixes.
The big new feature is support for ensemble propagations. In ensemble mode, multiple distinct instances of the same ODE system are integrated in parallel, typically using different sets of initial conditions and/or runtime parameters. Monte Carlo simulations and parameter searches are two typical examples of tasks in which ensemble mode is particularly useful.
The combination of ensemble mode with batch integrators can lead to substantial speedups (e.g., a floating-point throughput increase of 23x was observed on a modern desktop computer using 8 threads of execution, see here).
The full changelog, as usual, is available here:
Published by bluescarni almost 3 years ago
This is another big release for heyoka.py, featuring 2 major new features and substantial performance improvements.
Event detection is now available in the batch mode Taylor integrator. As a result, the batch mode integrator has now feature parity with the scalar mode integrator.
The batch mode event detection API is very similar to scalar mode. A tutorial describing the new feature is available here:
https://bluescarni.github.io/heyoka.py/notebooks/Batch%20mode%20overview.html#event-detection
Debuting in this release is support for continuous output for the propagate_for/until()
methods of the scalar and batch integrators.
Continuous output allows to compute the value of the solution of the ODE system at any time within the integration time interval covered by propagate_for/until()
. Tutorials are available here:
https://bluescarni.github.io/heyoka.py/notebooks/Dense%20output.html#continuous-output
https://bluescarni.github.io/heyoka.py/notebooks/Batch%20mode%20overview.html#continuous-output
This feature has been inspired by a similar feature available in the DifferentialEquations.jl package.
As a result of various micro-optimisations, performance for large ODE systems in compact mode has improved by up to 15%.
Additionally, fast event exclusion checking is now implemented as a JIT-compiled function, which leads to a ~30% reduction in the event detection overhead.
The full changelog, as usual, is available here:
Published by bluescarni about 3 years ago
This is a quick bugfix 🐛 release that fixes an issue in the conversion of SymPy rationals to the heyoka.py expression system.
The full changelog is available here:
Published by bluescarni about 3 years ago
This is one of the biggest releases of heyoka.py to date, featuring various new capabilities, a major change in the expression system and several fixes.
func
sA fundamental change debuting in heyoka.py 0.15.0 is that function nodes in the expression system now use reference semantics, instead of value semantics. This change is motivated by various use cases involving large symbolic expressions with a high degree of internal repetition (including, e.g., artificial neural networks), which can now be handled by the expression system orders of magnitude more efficiently (from the point of view of both CPU and memory utilisation).
atan2()
has been added to the expression system.An implementation of the VSOP2013 analytical solution for the motion of the planets of the Solar System has been added to the expression system. This means that it is now possible to formulate differential equations containing the positions/velocities of the planets of the Solar System as functions of time.
A tutorial introducing this new feature is available here:
https://bluescarni.github.io/heyoka.py/notebooks/vsop2013.html
Thanks to the generosity of OSU's Open Source Lab, who provided remote access to a PowerPC workstation, heyoka.py now features much better support for 64-bit PowerPC processors. In particular, heyoka.py is now able to take advantage of the hardware-accelerated quadruple-precision arithmetic capabilities of recent PowerPC processors.
kepE()
function.As usual, the full changelog is available here:
Published by bluescarni about 3 years ago
This new release of heyoka.py implements an important improvement in the automatic deduction of the cooldown value for terminal events, which should now be more reliable than before.
The full changelog, as usual, is available here:
Published by bluescarni over 3 years ago
The 0.12.0 release of heyoka.py features two important additions:
Serialisation allows to save/load heyoka.py objects via Python's pickle
module. A tutorial showcasing this new feature is available here:
https://bluescarni.github.io/heyoka.py/notebooks/pickling.html
Please pay attention to the very important CAVEATS highlighted at the beginning of the tutorial!
This is the first version of heyoka.py officially supporting 64-bit ARM processors. ARM builds have been added to the continuous integration pipeline, courtesy of CircleCI.
Published by bluescarni over 3 years ago
This is a minor release to keep the release number of heyoka.py in sync with the release number of the heyoka C++ library. Apart from a new tutorial notebook and a few doc fixes, there are no functional changes with respect to version 0.10.0.
The full changelog, as usual, is available here:
Published by bluescarni over 3 years ago
The latest version of heyoka.py comes with an important new feature, the ability to convert heyoka.py expressions to/from SymPy expressions. A tutorial showcasing this new feature is available here.
Additionally, this release introduces a change in the API of the callbacks that can (optionally) be passed to the propagate_*()
methods. Also, this release features a new pairwise product primitive and various improvements to the automatic simplification capabilities of the expression system.
As usual, the detailed changelog is available here:
Published by bluescarni over 3 years ago
This release includes two new functions in the expression system: the inverse of Kepler's elliptic equation and time polynomials.
Additionally, this release features also performance improvements and various internal cleanups.
The full changelog, as usual, is available here:
Published by bluescarni over 3 years ago
This new release of heyoka.py features a couple of breaking changes in the event detection API. The changes are explained in detail here:
https://bluescarni.github.io/heyoka.py/breaking_changes.html#bchanges-0-8-0
The release contains also a couple of new features for the propagate_*()
functions. As usual, the full changelog is available here:
Published by bluescarni over 3 years ago
This new release of heyoka.py comes with several feature additions and a couple of fixes.
One important improvement is that the time coordinate in the adaptive integrators is now represented internally in double-length format, which greatly increases time accuracy.
As usual, the full changelog is available here:
Published by bluescarni over 3 years ago
This is a small incremental release that bumps up the minimum required version of the heyoka C++ library to 0.6.1.
The full changelog, as usual, is available here:
Published by bluescarni over 3 years ago
This new version of heyoka.py comes with a big new feature, event detection. Various tutorials exploring this new feature are available:
https://bluescarni.github.io/heyoka.py/advanced_tutorials.html#event-detection
Another important new feature is that propagate_grid()
can now be used with the batch integrator.
The full changelog is available here:
Published by bluescarni over 3 years ago
This is a minor release that features additions and improvements to the expression system. In particular, the symbolic differentiation capabilities of heyoka's expression system are now available also in Python.
The full changelog, as usual, is available here:
Published by bluescarni over 3 years ago
This new release of heyoka.py comes with several new features:
powi()
for exponentiation with natural exponents.There is also an important bugfix regarding a division by zero in certain corner cases when using pow()
with small natural exponents.
As usual, the full changelog is available here:
Published by bluescarni over 3 years ago
This is the initial release of heyoka.py.