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GSoC Report: Trajectory New Generation: the trajectory format for the future of simulation

02 Sep 2020

This is the end?

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.

Why TNG?

Trajectory storage has proved problematic for the molecular simulation community, due to large file sizes, poor portability and low metadata incorporation. As hardware and software advances enable the creation of larger and more complex datasets, shortcomings in trajectory formats have been highlighted. The Trajectory New Generation (or TNG) format 1,2 designed by GROMACS aims to remove these shortcomings, enabling flexible compression, metadata incorporation small file sizes combined with a lightweight API.

Despite its many advantages, the TNG format has not seen widespread adoption, as tooling to support the format is lacking. Creation of new TNG tooling has been hindered by the current design and implementation of the TNG library in older style C code. My project centered around improving implementation and tooling for the TNG format. Our primary aims were 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 so that the format can be read into MDAnalysis.

TNG time!

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.

Using GoogleTest, a typical regression test looked a lot like this:

TEST_F(ArgonCompressedTest, BoxShapeValues)
{
    int64_t             stride_length, i;
    tng_function_status read_stat;
    read_stat = tng_util_box_shape_read(traj, &box_shape, &stride_length);
    EXPECT_EQ(read_stat, TNG_SUCCESS);
    // box_shape frame 0
    const std::vector<float> frame_0  = {
        3.60140, 0.00000, 0.000000, 0.000000, 3.60140, 0.000000, 0.000000, 0.000000, 3.60140,
    };
    ASSERT_EQ(frame_0.size(), 9);
    // compare element by element
    for (i = 0; i < 9; i++)
    {
        EXPECT_FLOAT_EQ(box_shape[i], frame_0[i]);
    }

}

Now you can just make test to ensure your TNG times will be fun times!

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 achieved full C++ compilation in a combination of !26, !27, !29, !33 and !34.

Moving the project to C++ appeared simple at the top level:

set_source_properties(SOURCE ... LANGUAGES CXX)

Where all .c files should be renamed to .cpp (primarily so clang will read them as C++ correctly). Then the hard part! Non ISO C then had to be made ISO C++. An example of this is given below:

void DECLSPECDLLEXPORT* Ptngc_warnmalloc_x(size_t size, char* file, int line);
#define warnmalloc(size) Ptngc_warnmalloc_x(size, __FILE__, __LINE__)

Use of this macro performs a void to T implicit cast which has deprecated in C++ as being unsafe. In C++ this requires an explicit cast with static_cast<T>():

//explicitly cast
unsigned int*  dict = static_cast<unsigned int*>(warnmalloc(0x20004 * sizeof *dict));

Also important, although technically ISO compliant, was turning C style casts into explicit (explicitly ugly) C++ casts:

//this
md5_append(md5_state, (md5_byte_t*)str, len);
// to this (wow disgusting)
md5_append(md5_state, reinterpret_cast<md5_byte_t*>(const_cast<char*>(str)), len);

There were loads and loads of these throughout the library, which hopefully look a lot more like C++ now.

From here I started on modernisation 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.

An example of a modernisation change to the TNG API involves the use of templates to simplify the definition of TNG data blocks. A complicated struct that carried around a void* array for the data and a tng_datatypes enum to indicate the data type contained in the block could instead be a templated class as is shown below:

template<typename T> // type for data array
TngDataBlock<T>::TngDataBlock(const int64_t&     id,
                            // other stuff
                              T*                 values) :

Template specialization is then used to do the correct operations for each datatype, reducing a lot of very repetitive code paths into a few simpler statements.

Refactoring the TNG library was a huge task, much more so than initially anticipated. Due to this and some changing circumstances, the aforementioned modernisation 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.

All the work I have done on TNG itself can be found in my TNG fork as well as open PRs on the TNG library itself.

PyTNG time!

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 achieved 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.

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 tng[0:len(tng):tng.block_strides["TNG_TRAJ_POSITIONS"]):
         # read the integrator timestep
         # then, get the data from the requested block by
         # supplying NumPy array which is updated in-place
         ts.get_pos(positions)

What can we do now?

Looking forward

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. I also aim to keep working on TNG itself time permitting.

What have I learnt?

Things I have learned in GSoC include

My experience

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!

— @hmacdope

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