add discussion of OpenMP parallelization

doc/src/Developer_par_openmp.rst

@@ -11,6 +11,104 @@ and KOKKOS package offer additional options and are more complex since
 they support more features and different hardware like co-processors
 or GPUs.
 
-Avoiding data races
--------------------
+One of the key decisions when implementing the OPENMP package was to
+keep the changes to the source code small, so that it would be easier to
+maintain the code and keep it in sync with the non-threaded standard
+implementation.  this is achieved by a) making the OPENMP version a
+derived class from the regular version (e.g. ``PairLJCutOMP`` from
+``PairLJCut``) and overriding only methods that are multi-threaded or
+need to be modified to support multi-threading (similar to what was done
+in the OPT package), b) keeping the structure in the modified code very
+similar so that side-by-side comparisons are still useful, and c)
+offloading additional functionality and multi-thread support functions
+into three separate classes ``ThrOMP``, ``ThrData``, and ``FixOMP``.
+``ThrOMP`` provides additional, multi-thread aware functionality not
+available in the corresponding base class (e.g. ``Pair`` for
+``PairLJCutOMP``) like multi-thread aware variants of the "tally"
+functions. Those functions are made available through multiple
+inheritance so those new functions have to have unique names to avoid
+ambiguities; typically ``_thr`` is appended to the name of the function.
+``ThrData`` is a classes that manages per-thread data structures.
+It is used instead of extending the corresponding storage to per-thread
+arrays to avoid slowdowns due to "false sharing" when multiple threads
+update adjacent elements in an array and thus force the CPU cache lines
+to be reset and re-fetched.  ``FixOMP`` finally manages the "multi-thread
+state" like settings and access to per-thread storage, it is activated
+by the :doc:`package omp <package>` command.
 
+Avoiding data races
+"""""""""""""""""""
+
+A key problem when implementing thread parallelism in an MD code is
+to avoid data races when updating accumulated properties like forces,
+energies, and stresses.  When interactions are computed, they always
+involve multiple atoms and thus there are race conditions when multiple
+threads want to update per-atom data of the same atoms.  Five possible
+strategies have been considered to avoid this:
+
+1) restructure the code so that there is no overlapping access possible
+   when computing in parallel, e.g. by breaking lists into multiple
+   parts and synchronizing threads in between.
+2) have each thread be "responsible" for a specific group of atoms and
+   compute these interactions multiple times, once on each thread that
+   is responsible for a given atom and then have each thread only update
+   the properties of this atom.
+3) use mutexes around functions and regions of code where the data race
+   could happen
+4) use atomic operations when updating per-atom properties
+5) use replicated per-thread data structures to accumulate data without
+   conflicts and then use a reduction to combine those results into the
+   data structures used by the regular style.
+
+Option 5 was chosen for the OPENMP package because it would retain the
+performance for the case of 1 thread and the code would be more
+maintainable.  Option 1 would require extensive code changes,
+particularly to the neighbor list code; options 2 would have incurred a
+2x or more performance penalty for the serial case; option 3 causes
+significant overhead and would enforce serialization of operations in
+inner loops and thus defeat the purpose of multi-threading; option 4
+slows down the serial case although not quite as bad as option 2.  The
+downside of option 5 is that the overhead of the reduction operations
+grows with the number of threads used, so there would be a crossover
+point where options 2 or 4 would result in faster executing.  That is
+why option 2 for example is used in the GPU package because a GPU is a
+processor with a massive number of threads.  However, since the MPI
+parallelization is generally more effective for typical MD systems, the
+expectation is that thread parallelism is only used for a smaller number
+of threads (2-8).  At the time of its implementation, that number was
+equivalent to the number of CPU cores per CPU socket on high-end
+supercomputers.
+
+Thus arrays like the force array are dimensioned to the number of atoms
+times the number of threads when enabling OpenMP support and inside the
+compute functions a pointer to a different chunk is obtained by each thread.
+Similarly, accumulators like potential energy or virial are kept in
+per-thread instances of the ``ThrData`` class and then only reduced and
+stored in their global counterparts at the end of the force computation.
+
+
+Loop scheduling
+"""""""""""""""
+
+Multi-thread parallelization is applied by distributing (outer) loops
+statically across threads.  Typically this would be the loop over local
+atoms *i* when processing *i,j* pairs of atoms from a neighbor list.
+The design of the neighbor list code results in atoms having a similar
+number of neighbors for homogeneous systems and thus load imbalances
+across threads are not common and typically happen for systems where
+also the MPI parallelization would be unbalanced, which would typically
+have a more pronounced impact on the performance.  This same loop
+scheduling scheme can also be applied to the reduction operations on
+per-atom data to try and reduce the overhead of the reduction operation.
+
+Neighbor list parallelization
+"""""""""""""""""""""""""""""
+
+In addition to the parallelization of force computations, also the
+generation of the neighbor lists is parallelized.  As explained
+previously, neighbor lists are built by looping over "owned" atoms and
+storing the neighbors in "pages".  In the OPENMP variants of the
+neighbor list code, each thread operates on a different chunk of "owned"
+atoms and allocates and fills its own set of pages with neighbor list
+data.  This is achieved by each thread keeping its own instance of the
+:cpp:class:`MyPage <LAMMPS_NS::MyPage>` page allocator class.