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This is a miniapp for the Energy Banding Monte Carlo (EBMC) neutron transportation
simulation code.  It is adapted from a similar miniapp provided by Andrew Siegel,
whose algorithm is described in [1], where only one process in a compute node
is used, and the compute nodes are divided into memory nodes and tracking nodes.
Memory nodes do not participate in particle tracking. Obviously, there is a lot
of resource waste in this design.

To improve problems stated above, we developed this miniapp with optimizations
made possible by MPI-3.0. In this miniapp, there is no distinction between
memory nodes and tracking nodes. All cores on all nodes are tracking particles.
In the code, r nodes comprise a memory group, such that there are np/(r*ppn) memory
groups in total. Here np is total number of processes and ppn is the number
processes  per node. Cross section data  is distributed on r nodes per memory group.

Within a compute node, we use MPI-3.0 shared memory windows to shared band data
between MPI processes. Between compute nodes, we use non-blocking calls to fetch
band data on remote nodes. With non-blocking calls, we can start fetching data for
band k+1 when we begin to track particles in band k, in effect achieving computation
and communication overlapping.

We have two methods to fetch remote band data. In the first version of the
miniapp (ebmc-iallgather), we use MPI_Iallgather, the nonblocking version of MPI_Allgather.
In ebmc-iallgather, nodes of a memory group work in lockstep from the first band to
the last band. Working in lockstep is a constraint. However, the benefit is that
MPI runtimes have  global information of the communication pattern and might do
 optimized communication scheduling.

In the second version of the miniapp (ebmc-rget), we use MPI_Rget, a
one-sided RMA operation newly defined by MPI-3.0. In ebmc-rget, nodes of a
memory group work independently. The drawback is that the communication scheduling
might be suboptimal.

In our experiments on Blues of LCRC at ANL, ebmc-iallgather had the best
performance, giving 15x speedup over the miniapp in [1].

Questions: Please contact Junchao Zhang <>

[1] Felker, Kyle G., Andrew R. Siegel, Kord S. Smith, Paul K. Romano, and Benoit Forget.
"The energy band memory server algorithm for parallel Monte Carlo transport calculations."
In SNA+ MC 2013-Joint International Conference on Supercomputing in Nuclear
Applications+ Monte Carlo, p. 04207. EDP Sciences, 2014.


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