Basics of Message-passing Mechanics of message-passing –A means of creating separate processes on different computers –A way to send and receive messages Single program multiple data (SPMD) model –Logic for multiple processes merged into one program –Control Statements separate processor blocks of logic –A compiled program is stored on each processor –All executables are started together statically –Example: MPI (Message Passing Interface) Multiple program multiple data (MPMD) model –Each processor has a separate master program –Master program spawns child processes dynamically –Example: PVM (Parallel Virtual Machine)

PVM (Parallel Virtual Machine) Multiple process control: Host process: control environment; Any process can spawn others, Daemon: control message passing PVM System Calls –Control: pvm_mytid(), pvm_spawn(), pvm_parent(), pvm_exit() –Get send buffer: pvm_initsend() –Pack for sending: pvm_pkint(), pvm_pkfloat(), pvm_pkstr() –Blocking/non-blocking transmission: pvm_send(), pvm_recv(), pvm_nrecv() –Unpack after receipt: Pvm_upkint(), pvm_upkfload, pvm_upkstr() –Group definition: pvm_joingroup() –Collective communication: pvm_bcast(), pvm_scatter(), pvm_gather, pvm_reduce(), pvm_mcast() From Oak Ridge National Laboratories, Free distribution

mpij and MpiJava Overview –MpiJava is a wrapper sitting on mpich or lamMpi –mpij is a native Java implementation of mpi Documentation –MpiJava ( –mpij (uses the same API as MpiJava) Java Grande consortium ( –Sponsors conferences & encourages Java for Parallel Programming –Maintains Java based paradigms (mpiJava, HPJava, and mpiJ) Other Java based implementations –JavaMpi is another less popular MPI Java wrapper

SPMD Computation (MPI) main (int argc, char *argv[]) { MPI_Init(&argc, &argv);. MPI_Comm_rank(MPI_COMM_WORLD, &myrank); if (myrank == 0) master(); else slave();. MPI_Finalize(); } The master process executes master() The slave processes execute slave()

A Simple MPI Program #include int main(int argc, char *argv[]) { int rank, size, MAX = , TAG=1; char data[MAX]; MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &myRank); MPI_Comm_size(MPI_COMM_WORLD, &size); if (size!=2) MPI_Abort(MPI_COMM_WORLD, 1); // Terminate all processors if (myRank==0) { sprintf(data, "Sending from %d of %d", rank, size); MPI_Send(data, MAX, MPI_CHAR, 1, TAG, MPI_COMM_WORLD); } else { MPI_Recv( data, MAX, MPI_CHAR, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, MPI_STATUS_IGNORE); printf:%s\n", data); } MPI_Finalize(); }

Start and Finish MPI_Init: Bring up program on all computers, pass command line arguments, establish ranks. MPI_Comm_rank: Determine the rank of the current process MPI_Comm_size: return the number of processors that are running MPI_Finalize: Terminate the program normally MPI_Abort: Terminate with an error code when something bad happens

Standard Send (MPI_Send) int MPI_Send(void *buf, int count, MPI_Datatype type, int dest, int tag, MPI_Comm comm) Input Parameters –buf: initial address of send buffer (choice) –count: integer number of elements in send buffer –type: type of each send buffer element (ex: MPI_CHAR, MPI_INT, MPI_DOUBLE, MPI_BYTE, MPI_PACK, etc.) –dest: rank of destination (integer) –tag: message tag (integer) –comm communicator (handle) Note: MPI_PACK allows different data types to be sent in a single buffer using the MPI_Pack and MPI_Unpack functions. Note: Google MPI_Send, MPI_Recv, etc for more intormation Block until message is received or data copied to a buffer

Matching Message Tags Differentiates between types of messages The message tag is carried within message. Wild card codes allow receipt of any message from any source –MPI_ANY_TAG: matches any message type –MPI_ANY_SOURCE: matches messages from any sender –Sends cannot use wildcards (pull operation, not push) Send message type 5 from buffer x to buffer y in process 2

Status of Sends and Receives MPI_Status status; MPI_Recv(&result, 1, MPI_DOUBLE, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status); status.MPI_SOURCE/* rank of sender */ status.MPI_TAG/* type of message */ status.MPI_ERROR/* error code */ –MPI_SUCCESS - Successful, MPI_ERR_BUFFER - Invalid buffer pointer –MPI_ERR_COUNT - Invalid count, MPI_ERR_TYPE - Invalid data type –MPI_ERR_TAG - Invalid tag, MPI_ERR_COMM - Invalid communicator –MPI_ERR_RANK - Invalid rank, MPI_ERR_ARG - Invalid argument –MPI_ERR_UNKNOWN - Unknown error, MPI_ERR_INTERN - internal error –MPI_ERR_TRUNCATE - message truncated on receive MPI_Get_count(&status, recv_type, &count)/* number of elements */

Console Input and Output Input –Console input must be initiated at the host process if (rank==0) { printf("Enter some fraction"); scanf("%lf", &value); fflush(stdin); } or gets(data) to read a string Output –Any process can initiate an output –MPI uses internal library functions to route the output to the process initiating the program –Transmission using a library functions initiated before normal application transmissions can arrive after, or visa versa

Groups and Communicators Group: A set of processes ordered by relative rank Communicators: Context required for sends and receives Purpose: Enable collective communication (to subgroups of processors) The default communicator is MPI_COMM_WORLD –A unique rank corresponds to each executing process –The rank is an integer from 0 to p – 1 –The number of processors executing is p Applications can create subset communicators –Each processor has a unique rank in each sub-communicator –The rank is an integer from 0 to g-1 –The number of processors in the group is g

Example

MPI Group Communicator Functions Typical Usage 1.Extract group from communicator: MPI_Comm_group 2.Form new group: MPI_Group_incl or MPI_Group_excl 3.Create new group communicator: MPI_Comm_create 4.Determine group rank: MPI_Comm_rank 5.Communications: MPI message passing functions 6.Destroy created communicators and groups: MPI_Comm_free and MPI_Group_free

Details MPI_Group_excl: –New group without certain processes from an existing group –int MPI_Group_excl(MPI_Group group, int n, int *ranks, MPI_Group *newgroup); MPI_Group_incl: –New group withc selected processes from an existing group –int MPI_Group_incl(MPI_Group group, int n, int *ranks, MPI_Group *newgroup);

Creating and using a sub-group int ranks[4]={1,3,5,7}; MPI_Group original, subgroup; MPI_Comm slave; MPI_Comm_group(MPI_COMM_WORLD, &original); MPI_Group_incl(original, 4, ranks, &subgroup); MPI_Comm_create(MPI_COMM_WORLD, subgroup, &slave); MPI_Send(data,strlen(data)+1,MPI_CHAR,0,0, slave); MPI_Group_free(subgroup); MPI_Group_free(original); MPI_Comm_free(slave);

Point-to-point Communication Pseudo code constructs Send(data, destination, message tag) Receive(data, source, message tag) Synchronous –Send Completes when data safely received –Receive completes when data is available –No copying to/from internal buffers Asynchronous –Copy to internal message buffer –Send completes when transmission begins –Local buffers are free for application use –Receive polls to determine if data is available Process 1Process 2 send(&x, 2); recv(&y, 1); xy Generic syntax (actual formats later)

Synchronized sends and receives (b) recv() occurs before send()

Point to Point MPI calls Buffered Send (receiver gets to it when it can) –Completes after data is copied to a user supplied buffer –Becomes synchronous if no buffers are available Ready Send (guarantee transmission is successful) –A matching receive call must precede the send –Completion occurs when remote processor receives the data Standard Send (starts transmission if possible) –If receive call is posted, completes when transmission starts –If no receive call is posted, completes when data is buffered by MPI, but becomes synchronous if no buffers are available Blocking - Return occurs when the call completes Non-Blocking - Return occurs immediately –Application must periodically poll or wait for completion –Why non-blocking? To allows more parallel processing

Buffered Send Example Applications supply a data buffer area using MPI_Buffer_attach() to hold the data during transmission Note: transmission is between sender/receiver MPI buffers Note: copying in and out of buffers can be expensive

Point-to-point Message Transfer MPI_Comm_rank(MPI_COMM_WORLD,&myrank); int x; MPI_Status *stat; if (myrank == 0) { MPI_Send(&x,1,MPI_INT,1,99,MPI_COMM_WORLD); } else if (myrank == 1) { MPI_Recv(&x,1,MPI_INT,0,99,MPI_COMM_WORLD,stat); }

Non-blocking Point-to-point Transfer MPI_Comm_rank(MPI_COMM_WORLD, &myrank); int x; MPI_Request *io; MPI_STATUS *stat; if (myrank == 0) { MPI_Isend(&x,1,MPI_INT,1,99,MPI_COMM_WORLD,io); doSomeProcessing(); MPI_Wait(io, stat); } else if (myrank == 1) { MPI_Recv(&x,1,MPI_INT,0,99,MPI_COMM_WORLD,stat); } MPI_Isend() and MPI_Irecv() return immediately MPI_Rsend returns when received by remote computer, MPI_Bsend Buffered send, MPI_Send Standard send MPI_Wait() returns after transmission, MPI_Test() returns non-zero after transmission, returns zero otherwise

Message Passing Order Note : Messages originating from a processor will always be received in order. Messages from different processors can be received out of order.

Collective Communication MPI_Bcast()): Broadcast or Multicast data to processors in a group Scatter (MPI_Scatter()): Send parts of an array to separate processes Gather (MPI_Gather()): Collect array elements from separate processes AlltoAll (MPI_Alltoall()): A combination of gather and scatter. All processes send; then sections of the combined data are gathered MPI_Reduce(): Combine values from all processes to a single value using some operation (function call). MPI_Reduce_scatter(): First reduce and then scatter result MPI_Scan(): Reduce values received from processors of lower rank in the group. (Note: this is a prefix reduction) MPI_Barrier(): Pause until all processors reach the barrier call MPI operations on groups of processes Advantages MPI can use the processor hierarchy to improve efficiency Although, we can implement collective communication using standard send and receive calls, collective operations require less programming and debugging

Reduce, BroadCast, All Reduce Reduce, then broadcast Butterfly Allreduce

Predefined Collective Operations MPI_MAX, MPI_MIN: maximum, minimum MPI_MAXLOC, MPI_MINLOC: –If the output buffer is out –For each index, out[i].val and out[i].rank contains the max (or min) value and the processor rank containing it MPI_SUM, MPI_PROD: sum, product MPI_LAND, MPI_LOR, MPI_LXOR: logical &, |, ^ MPI_BAND, MPI_BOR, MPI_BXOR: bitwise &, |, ^

Derived MPI Data Types /* Goal: send items, each containing a double, integer, and a string */ int lengths[3] = {1, 1, 100}; MPI_Datatype types[3] = {MPI_DOUBLE, MPI_INT, MPI_CHAR, }; int displacements[3] = {0, sizeof(double), sizeof(double)+sizeof(int)}; MPI_Datatype* myType; /* Derive a data type */ MPI_TYPE_create_struct(3, lengths, displacements, types, &myType); MPI_Type_commit(myType);/* Commit it for use */ /* count data items broadcast from source to processors in communicator */ MPI_Bcast(&data, count, myType, source, comm); MPI_Type_free(myType);/* Don't need it anymore */ Note: Broadcasts can be fifty to a hundred times faster than doing individual sends using for loops

Collective Communication Example Master: Allocate memory to hold all of the date and then gather items from a group of processes Remotes: Fill an array with data and send them to the master Note: All processors execute the MPI_Gather() function int data[10]; /*data to gather from processes*/ MPI_Comm_rank(MPI_COMM_WORLD, &myrank); if (myrank == 0) { MPI_Comm_size(MPI_COMM_WORLD, &grp_size); buf = (int *)malloc(grp_size*10*sizeof (int)); } else { for (i=0; i<10; i++) data[i] = myrank; } MPI_Gather(data, 10, MPI_INT, buf, grp_size*10, MPI_INT, 0 /* gatherer rank */,MPI_COMM_WORLD);

User Defined Collective Operation /* User-defined function to add complex numbers (dest = src + dest) */ void compSum(Complex *src, Complex *dest, int *len, MPI_Datatype *ptr) { int i; Complex c; for (i=0; i< *len; ++i) { dest->real += src->real; dest->imag += src->imag; src++; dest++; } Complex in[100], out[100]; MPI_Op operation; MPI_Datatype complexType; MPI_Type_contiguous(2, MPI_DOUBLE, &complextype); // Define type MPI_Type_commit(&complexType); // Record for possible use MPI_Op_create( compSum, True, &operation); // Define the operation MPI_Reduce( in, out, 100, complexType, operation, root, communicator );

Collective Communication Rules All of the processors in the communicator call the same collective function The arguments must specify the same host, input data array length, data type, operation, and communicator The destination process is the only one that needs to specify an output array There is no message tag. Matching is done by the calling order and the communicator The input and output buffers must be different and should not overlap

Broadcast Broadcast - Sending the same message to all processes Multicast - Sending the same message to a defined group of processes. bcast(); buf bcast(); data bcast(); data Process 0Processp  1Process 1 Action Code MPI form

Scatter Distributing each element of an array to separate processes Contents of the i th location of the array transmits to process i scatter(); buf scatter(); data scatter(); data Process 0Processp  1Process 1 Action Code MPI form

Gather One process collects individual values from set of processes. gather(); buf gather(); data gather(); data Process 0Processp  1Process 1 Action Code MPI form

Reduce Perform a distributed calculation Example: Perform addition over a distributed array reduce(); buf reduce(); data reduce(); data Process 0Processp  1Process 1 + Action Code MPI form

Avoiding MPI Deadlocks MPI_Send doesn't always work the same way –Can copy to a buffer and then return before the transmission is received –Can block until the matching MPI_Recv starts –MPI uses thresholds to switch from buffered to blocking sends –Some implementations buffer small messages and block large messages Deadlock Possibilities (MPI_Send followed by MPI_Recv) –If all of the sends block, none of the receives can start –Small messages may succeed, while larger messages may lead to deadlock Possible Solutions: –Some processors send before receive; others receive before send –Use MPI_Sendrecv or Sendrecv_replace so that MPI will automatically handle the order of calls and guarantee no deadlock. An MPI_Recv without a matching send will block forever

Timing Parallel Programs What should not be timed? –Time to type input –Time to print or display output What should be timed? –The actual algorithm's computation –Communication blocks How? Answer: Either use MPI or C time.h functions double start = MPI_Wtime(); /* Do stuff */ double time = (MPI_Wtime() – start)*MPI_Wtick(); OR C (but doesn't include idle time) clock_t start = clock(); /* Do stuff */ float time = ((double) (clock()-start)) / CLOCKS_PER_SEC;

Maximum Time over Processors double start, localElapsed, elapsed; // Start all processors together MPI_Barrier(MPI_COMM_WORLD); start = MPI_Wtime(); // Start time /** do code here */ // Get processor elapsed time localElapsed = MPI_Wtime() – start; // Get the maximum elapsed processor time MPI_Reduce(&localElapsed, &elapsed, 1, MPI_DOUBLE, MPI_MAX, 0, comm); if (rank == 0) // Master processor outputs result printf("Elapsed time = %f seconds\n", elapsed); Note: Another way is to code another barrier at the end in order to avoid needing a reduce operation