4.3. N-body benchmark (TAMPI, Blocking MPI Functions)

An N-body simulation numerically approximates the evolution of a system of bodies in which each body continuously interacts with every other body. A familiar example is an astrophysical simulation in which each body represents a galaxy or an individual star, and the bodies attract each other through the gravitational force.

N-body simulation arises in many other computational science problems as well. For example, protein folding is studied using N-body simulation to calculate electrostatic and van der Waals forces. Turbulent fluid flow simulation and global illumination computation in computer graphics are other examples of problems that use N-body simulation.

This application has been parallelized using MPI and OmpSs-2. The MPI part requires an MPI implementation supporting the multi-threading mode. It mainly targets the Intel MPI implementation, however, it should work with other libraries when adding the needed implementation-specific flags in the Makefile.

A second level of parallelism is exploited following the OmpSs-2 approach. The program uses tasks to express the parallelism among the different blocks. Communication services have also been taskified but their execution has been serialized by means of a sentinel dependence (in the code serial) that guarantees the proper order of execution. The following function centralizes all the communication operations to exchange data among processes:

#pragma oss task label(send_particles_block) in(*sendbuf) inout(serial)
void send_particles_block(const particles_block_t *sendbuf, int block_id, int dst)
   MPI_Send(sendbuf, sizeof(particles_block_t), MPI_BYTE, dst, block_id+10, MPI_COMM_WORLD);

#pragma oss task label(recv_particles_block) out(*recvbuf) inout(serial)
void recv_particles_block(particles_block_t *recvbuf, int block_id, int src)
   MPI_Recv(recvbuf, sizeof(particles_block_t), MPI_BYTE, src, block_id+10, MPI_COMM_WORLD, MPI_STATUS_IGNORE);

Taking as a baseline this version of the program we want to exploit the TAMPI interoperability layer capabilities. The Makefile system already includes the compilation and linkage against TAMPI (then, MPI services can be captured by this supporting library). We will need to make the proper changes in the code to inform the program about the usage of a different level of thread support (where communication services can potentially become task switching points). Once we have this thread support level we can relax task execution order by removing artificial dependences.

4.3.1. Goals of this exercise

  • Transform the code in order to exploit TAMPI capabilities for blocking MPI communications. Look for TODO comments.

  • Compare performance results between the initial version and your candidate version.

  • Check several runtime options when executing the program (versions, schedulers, etc.).

  • Check scalability. Execute the program using different numbers of nodes and cpus and compute the speed-up.

  • Change program arguments that may have an impact on task granularity (block size, tile size, etc.).

  • Change program arguments that may have an impact on the number of tasks (matrix sizes and/or block/tile sizes).

  • Get different paraver traces using different MPI configurations (i.e., MPI parameters), runtime options and/or program arguments and compare them.

4.3.2. Execution instructions

The binaries accept several options. The most relevant options are the number of total particles with -p, and the number of timesteps with -t. More options can be seen passing the -h option. An example of execution could be:

$ mpiexec.hydra -n 4 ./nbody -t 100 -p 8192

in which the application will perform 100 timesteps in 4 MPI processes, where each process will have the same number of cores (# of available cores / 4). The total number of particles will be 8192, this means that each process will have 2048 particles (2 blocks per process).