Introduction to TDDC78 Lab Series Lu Li Linköping University Parts of Slides developed by Usman Dastgeer
Goals Shared- and Distributed-memory systems Programming parallelism (typical problems)
Goals Shared- and Distributed-memory systems Programming parallelism (typical problems) Approach and solve opartitioning Domain decomposition Functional decomposition ocommunication oagglomeration omapping o
TDDC78 Labs: Memory-based Taxonomy Memory Distributed Shared Labs 1 2&3 Distributed 5 Use MPI POSIX threads & OpenMP MPI LAB 4 (tools). May saves your time for LAB 5.
Information sources Compendium oyour primary source of information http://www.ida.liu.se/~tddc78/labs/ ocomprehensive Environment description Lab specification Step-by-step instructions Others Triolith: http://www.nsc.liu.se/systems/triolith/ MPI: http://www.mpi-forum.org/docs/
TDDC 78 Labs: Memory-based Taxonomy Memory Distributed Shared Labs 1 2&3 Distributed 5 Use MPI POSIX threads & OpenMP MPI LAB 5 (tools) at every stage. Saves your time.
Learn about MPI Define MPI types Send / Receive Broadcast Scatter / Gather LAB 1 Use virtual topologies MPI_Issend / MPI_Probe / MPI_Reduce Sending larger pieces of data LAB 5 Synchronize / MPI_Barrier
Lab-1 TDDC78: Image Filters with MPI Blur & Threshold o See compendium for details Your goal is to understand: Define types Send / Receive Broadcast Scatter / Gather For syntax and examples refer to the MPI lecture slides Decompose domains Apply filter in parallel
MPI Types Example typedef struct { int id; double data[10]; } buf_t; // Composite type buf_t item; // Element of the type MPI_Datatype buf_t_mpi; // MPI type to commit int block_lengths [] = { 1, 10 }; // Lengths of type elements MPI_Datatype block_types [] = { MPI_INT, MPI_DOUBLE }; //Set types MPI_Aint start, displ[2]; MPI_Address( &item, &start ); MPI_Address( &item.id, &displ[0] ); MPI_Address( &item.data[0], &displ[1] ); displ[0] -= start; // Displacement relative to address of start displ[1] -= start; // Displacement relative to address of start MPI_Type_struct( 2, block_lengths, displ, block_types, &buf_t_mpi ); MPI_Type_commit( &buf_t_mpi );
Send-Receive... int s_data, r_data;... MPI_Request request; MPI_ISend( &s_data, sizeof(int), MPI_INT, (my_id == 0)?1:0, 0, MPI_COMM_WORLD, &request); MPI_Status status; MPI_Recv( &r_data, sizeof(int), MPI_INT, (my_id == 0)?1:0, 0, MPI_COMM_WORLD, &status ); MPI_Wait(&request, &status);... P0 SendTo(P1) program execution P1 SendTo(P0) RecvFrom(P1) RecvFrom(P0)
Send-Receive Modes (1) SEND BLOCKING Standard Synchronous Buffered Ready MPI_Send MPI_Ssend MPI_Bsend MPI_Rsend RECEIVE BLOCKING MPI_Recv NONBLOCKING MPI_Isend MPI_Issend MPI_Ibsend MPI_Irsend NONBLOCKING MPI_Irecv
Lab-4 Lab 5: Particles Moving particles Moving Validate particlesthe pressure law ValidateDynamic the pressure law: pv=nrt interaction patterns Dynamic interaction patterns # of particles that fly across borders is n o# of particles that fly across borders is not static You need advanced domain decomp You need advanced Motivate yourdomain choice! decomposition omotivate your choice!
Process Topologies (1) Process Topologies (0) By default processors are arranged into 1-dimensional arraysinto 1By default processors are arranged dimensional arrays Processor ranks are computed! accordingly Processor ranks are computed accordingly What if processors need! to communicate in 2 What if processors need dimensions or more? to communicate in 2 dimensions or more? Use virtual topologies achieving 2D! Use virtual topologies achieving 2D instead of 1D instead ofof1d arrangement of arrangement processors with convenient ranking schemes processors with convenient ranking
Process Topologies (1) int dims[2]; // 2D matrix / grid dims[0]= 2; // 2 rows dims[1]= 3; // 3 columns MPI_Dims_create( nproc, 2, dims); int periods[2]; periods[0]= 1; // Row-periodic periods[1]= 0; // Column-non-periodic int reorder = 1; // Re-order allowed MPI_Comm grid_comm; MPI_Cart_create( MPI_COMM_WORLD, 2, dims, periods, reorder, &grid_comm);
Process Topologies (2) int int int int my_coords[2]; // Cartesian Process coordinates my_rank; // Process rank right_nbr[2]; right_nbr_rank; MPI_Cart_get( grid_comm, 2, dims, periods, my_coords); MPI_Cart_rank( grid_comm, my_coords, &my_rank); right_nbr[0] = my_coords[0]+1; right_nbr[1] = my_coords[1]; MPI_Cart_rank( grid_comm, right_nbr, & right_nbr_rank);
Collective Communication (CC)... // One processor for(int j=1; j < nproc; j++) { MPI_Send(&message, sizeof(message_t),...); }... // All the others MPI_Recv(&message,sizeof(message_t),...);
CC: Scatter / Gather Distributing (unevenly sized) chunks of data sendbuf = (int *) malloc( nproc * stride * sizeof(int)); displs = (int *) malloc( nproc * sizeof( int)); scounts = (int *) malloc( nproc * sizeof( int)); for (i = 0; i < nproc; ++i) { displs[i] =... scounts[i] =... } MPI_Scatterv( sendbuf, scounts, displs, MPI_INT, rbuf, 100, MPI_INT, root, comm);
Summary Learning goals opoint-to-point communication oprobing / Non-blocking send (choose) obarriers & Wait = Synchronization oderived data types ocollective communications ovirtual topologies Send/Receive modes ouse with care to keep your code portable, e.g. MPI_Bsend o It works there but not here!
MPI Labs at home? No problem www.open-mpi.org Simple to install Simple to use