Some pointers to more advanced features of MPI #

Communicator manipulation #

We saw that we can distinguish point-to-point messages by providing different tags, but that there was no such facility for collective operations. Moreover, a collective operation (by definition) involves all the processes in a communicator.

This raises two questions:

1. How can we have multiple collective operations without them interfering with each other;
2. What if we want a collective operation, but using only a subset of the processes (e.g. only processes with an even rank)?

We might worry that we’re reduced to writing everything by hand using point-to-point messages, but fear not, MPI has us covered.

Duplicating communicators #

To address point 1, collective operations match based on the communicator context, and MPI allows us to duplicate communicators. This provides us with a new communicator that contains exactly the same set of processes with the same ranks, but collectives on one communicator won’t interfere with those on another (and similarly for point-to-point messages).

int MPI_Comm_dup(MPI_Comm incomm, MPI_Comm *outcomm);

This is a very useful thing to use if you are writing a library that uses MPI. Whenever someone calls your library you do

MPI_Comm_dup(user_communicator, &library_communicator);

and then always use the library_communicator inside your library. Now you can guarantee that you will never accidentally match any messages or collectives that the user runs on their user_communicator.

When we are done, we should release the communicator we duplicated (so as not to leak memory) by calling MPI_Comm_free

int MPI_Comm_free(MPI_Comm *comm);

/* To release a communicator: */
MPI_Comm_free(&library_communicator);

Splitting communicators into subgroups #

This is useful if want some collective operation over a subset of all the processes, for example we want to gather along the rows of a distributed matrix. This can be done by calling MPI_Comm_split

int MPI_Comm_split(MPI_Comm incomm, int colour, int key, MPI_Comm *newcomm);

The colour decides which ranks in incomm end up in the same newcomm. Ranks that provide the same colour will be in the same group. The key can be used to provide an ordering of the ranks in the new group, usually we pass the rank from the incomm.

For example, to create a communicator that splits into the processes in MPI_COMM_WORLD into a even and odd processes we can use.

int rank;
MPI_Comm incomm = MPI_COMM_WORLD;
MPI_Comm newcomm;
MPI_Comm_rank(incomm, &rank)
MPI_Comm_split(incomm, rank % 2, rank, &newcomm);
/* Do stuff with newcomm */
/* Release once we are done */
MPI_Comm_free(&newcomm);

Here’s a picture:

We emphasise again that this does not produce new processes, it just provides a communication context that does not contain all processes.

Exercise

code/mpi-snippets/split-comm.c contains a simple example. Have a look at the code and compile and run it.

Do you understand the output?

Do you understand why there is only one splitcomm variable (despite splitting the input communicator into two)?

This splitting facility is useful if we only need a subset of all the processes to participate in a collective operation. This might be something like reducing the number of processes participating in the coarse grids of a multigrid solve.

Further features and details #

In addition to what we’ve seen, MPI provides a number of other features that are useful for writing libraries. We won’t cover them in detail, but just mention some aspects.

File IO #

MPI, via MPI-IO, provides a portable and high-performance way of reading and writing files in parallel. This forms the backbone of higher-level parallel file libraries like HDF5 and NetCDF.

Profiling interface #

All MPI functions (everything called MPI_XXX) are actually just wrappers around internal “profiling” functions whose names start with PMPI_XXX. For example, MPI_Send is implemented in the MPI library as

int MPI_Send(const void *sendbuf, int count, MPI_Datatype datatype, int dest, int tag, MPI_Comm comm)
{
  return PMPI_Send(sendbuf, count, datatype, dest, tag, comm);
}

The public MPI_ functions are exported with weak symbol binding so we can override them. For example, suppose that we want to print a message every time an MPI_Send is called, our code could do:

int MPI_Send(const void *sendbuf, int count, MPI_Datatype datatype, int dest, int tag, MPI_Comm comm)
{
  printf("Sending message to %d\n", dest);
  return PMPI_Send(sendbuf, count, datatype, dest, tag, comm);
}

This facility is used to write tools that can produce timelines of message-passing in a parallel program. These include

• The MPI Parallel Environment
• Score-P
• Scalasca
• Tau

One-sided messaging and Remote Memory Access #

All of the messaging we saw was two-sided, in that we need both a send and a receive. MPI-2 introduced, and MPI-3 extended and improved, support for one-sided messages and direct access to remote (off-process) memory. For details on these features, if you’re interested, I recommend the books Using MPI and Using Advanced MPI. See also Torsten Hoefler’s tutorials.

Language bindings and libraries #

Julia has MPI bindings in MPI.jl, and distributed arrays in the MPIArrays.jl package.

As we’ve seen in this course, Python has wrappers via mpi4py. For distributed array computation, look at dask.

For parallel sparse linear algebra, and PDE solvers, PETSc and Trilinos are robust and well-supported libraries. PETSc has mature Python bindings provided by petsc4py.