Well well well, here we are at the end of GSOC! What a ride it has been, giving me a chance to improve my skills and contribute in ways I never thought would be possible. Thanks to all my mentors @acpmnv (Paul), @orbeckst (Oliver) and @richardjgowers (Richard) for all the help and guidance throughout.
Trajectory storage has proved problematic for the molecular simulation community, due to large file sizes, poor portability and low metadata incorporation. The TNG format designed by GROMACS aims to remove these shortcomings, however it has not seen wide adoption as tooling to support the format is lacking. Creation of new TNG tooling has been hindered by its current design and implementation in older style C code. My project centered around improving implementation and tooling for the TNG format. This was two fold, with the first half of my project working on converting the original library to C++ and the second half on developing some Python bindings.
I started GSOC working on the TNG library with the aim of adding tests, understanding the API and converting the older C style API it to C++. I worked on this for the majority of the bonding period as well as the first and some of the second coding period.
First I added tests in !3 which then revealed problems in the base API that were fixed in !6, !12 and !17. I then made these additional tests into a formal test suite employing googletest in !16, !20 and !21. This revealed some more API elements that could be improved which were touched up in !22, !23 and !25.
Once we had a test suite that could be used to check library correctness on the major routines we started to work on the TNG library itself. Key goals were C++ compilation and in turn modernisation of the library routines to modern C++. I acheived full C++ compilation in a combination of !26, !27, !29, !33 and !34.
From here I started on modernization of the library classes and constructs to modern C++. These progressed in !28 which outlined the structure we were aiming for, !32 which defined a specific API for blocks, !35 that updated the trajectory API and !36 which progressed on the IO elements. These were huge tasks and due to this and some changing circumstances, these MRs are all still open with more work required. I plan on working on these into the future but progress will be slow as I do not have solid blocks of time to dedicate.
From here I switched focus to PyTNG, a set of python bindings designed for use by MDAnalysis although technically a separate library. I worked on PyTNG for most of the second half of GSOC. This required changing gears a little bit as well as learning Cython, which was initially a bit of a learning curve for me.
Firstly I changed the TNG libraries exported with PyTNG itself to include the bugfixes obtained as part of the earlier improvements to the TNG library. This was incorporated as part of #28. I then improved the TNG library calls in PyTNG in #29.
Following some design discussions, we then moved towards a newer design for the bindings, so as to be able to read all the blocks available in a TNG file with maximal speed. This was acheived in merging #32 and ongoing work in #38, which are a total redesign of the whole PyTNG API.
The end result of this is a working implementation that can read any TNG block as of #32 with improvements close in #38. I also added docs and examples as part of #38. Profiling and timings indicated high performance of the bindings, with the library largely IO bound at the TNG API (pure C) level.
What can we do now?
The TNG library now has a regression-test suite and can be compiled as C++ 14 with a modern compiler. The groundwork is also laid for further improvements of the library.
There are now also a set of functional Python bindings for the TNG format that can read any TNG block.
An example of how to use PyTNG to read a TNG file and extract positions is shown below:
import pytng import numpy as np with pytng.TNGFileIterator("traj.tng", 'r') as tng: # make a numpy array to hold the data using helper function # this array will then be updated in-place positions = tng.make_ndarray_for_block_from_name("TNG_TRAJ_POSITIONS") # the TNG API uses regular strides for data deposition, here we stride # over the whole trajectory for the frames that have position data # where len(tng) is the total number of steps in the file. for ts in range(0, len(tng), tng.block_strides["TNG_TRAJ_POSITIONS"]): # read the integrator timestep, modifying the current_integrator_step tng.read_step(ts) # get the data from the requested block by supplying NumPy array which # is updated in-place tng.current_integrator_step.get_pos(positions)
I plan on extending PyTNG to TNG writing as well as integrating PyTNG into MDAnalysis following GSOC. I have raised issues in PyTNG to make sure things that still need to be completed are apparent to people following on.
What have I learnt?
Things I have learned in GSOC include
- C++ class design
- C++ for binary IO
- CMake, GoogleTest and how to refactor a large codebase
- Cython and Python/C integration
- Working collaboratively on a diverse global team
GSOC was a fantastic experience for me, as I felt very welcome amongst two communities (MDAnalysis and GROMACS). I hope I was able to give back! I was constantly challenged throughout and this helped me learn and grow with big improvements both in my technical skills and approach to computational problem solving. Thanks all for having me along!