MPI Labs

1. Simulation of an colony of ants Camille Coti coti@lri.fr QosCosGrid Barcelona meeting, 10/25/06 MPI Labs

2. Introduction to ants • How ants find food and how they remember the path • Random walk around the source • Once they find some food : go back to the source • Drop pheromones along this path • When they find some pheromones: • Follow them • Pheromones evaporate, thereby limiting their influence over time

3. Modelising an ant colony • Cellular automata • Grid of cells, reprensented as a matrix • State of a cell: • With an ant on it (or several ones) • Some pheromones can have been dropped on it • It can also be empty • We define a transition rule from time t to time t+1

4. A picture can make things easier The ant-hill (where the ants live) The food Ants spread around the ant-hill Ants that have found the food drop pheromones

5. Update algorithm • every ant seeks around it • if it finds pheromones: • follow it • if it finds some food: • take some • go back to the ant hill dropping pheromones on the path • otherwise: • chose a random direction

6. Parallelisation • Share the grid among the processors • Each processor computes a part of the calculation • Use MPI communication between the processes • This is parallel computing ☺ Proc #0 Proc #1 Proc #2 Proc #3

7. Parallelisation • Each processor can compute the transition rule for almost all the space it is assigned to • BUT problem near the boundaries: need to know what is next • THEN each processor has to send the state of its frontiers to its neighbours • Overlap computation and communications • Non-blocking communication • Computation • Wait for the communications to be finished (usually not necessary)

8. Algorithm of the parallelisation • Initialisation • for n iterations do: • send/receive frontiers • compute the transition rule (excepted near the frontiers) • finish the communications • compute the transition rule near the frontiers • send the result • update the bounds (ants might have walked across the frontiers)

9. What you have to do • We provide you • The basic functions • The update rule • You have to write • The MPI communications • An MPI data type creation and declaration

10. Some “good” practice rules • Initalise your communications • MPI_Init(&argc, &argv); • MPI_Comm_size(MPI_COMM_WORLD, &size); • MPI_Comm_rank(MPI_COMM_WORLD, &rank); • Finalise them • MPI_Finalize();

11. Some “good” practice rules • Use non-blocking communications rather than blocking ones • MPI_Isend() / MPI_Irecv() • Wait for completion with MPI_Waitall() • So that you can overlap communications with computation

12. Creating a new MPI data type • Declare the types that will be contained • MPI_Datatypes types[2] = {MPI_INT, MPI_CHAR} • Declare the offset for the address • MPI_Aint displ[2]={0, 4} • Create your structure and declare its name • MPI_Type_create_struct(...) • And commit it • MPI_Type_commit(...)

13. Create a topology • For example, create a torus • void create_comm_torus_1D(){ • int mpierrno, period, reorder; • period=0; reorder=0; • mpierrno=MPI_Cart_create(MPI_COMM_WORLD, 1, &comm_size, &period, reorder, &comm_torus_1D); • MPI_Cart_shift(comm_torus_1D,0,1,&my_west_rank, &my_east_rank); • } • (you won't have to do this for the labs, this function is provided, but it is for your personal culture)

14. Some collective communications • Reductions: sum, min, max... • Useful for time measurements or to make a global sum of local results, for example • MPI_Reduce(...) • Barriers • All the processes get synchronised • MPI_Barrier(communicator)

15. Misc • Time measurement: • t1 = MPI_Wtime(); • t2 = MPI_Wtime(); • time_elapsed = t2 - t1; • MPI_Wtime() returns the time elapsed on a given processor

16. If you need more • www.lri.fr/~coti/QosCosGrid • Feel free to ask questions☺