MPI

MPI
Speaker：呂宗螢
Date：2007/06/01

Embedded and Parallel Systems Lab 2
Outline

MPI
 MPI is a language-independent communications
protocol used to program parallel computers
 分散式記憶體（Distributed-Memory）
 SPMD(Single Program Multiple Data )
 Fortran , C / C++

MPI需求及支援環境
 Cluster Environment
 Windows
Microsoft AD (Active Directory) server
Microsoft cluster server
 Linux
NFS (Network FileSystem)
NIS (Network Information Services)又稱 yellow pages
SSH
MPICH 2

MPI 安裝
http://www-unix.mcs.anl.gov/mpi/mpich/
下載mpich2-1.0.4p1.tar.gz
[shell]# tar –zxvf mpich2-1.0.4p1.tar.gz
[shell]# mkdir /home/yourhome/mpich2
[shell]# cd mpich2-1.0.4p1
[shell]# ./configure –prefix=/home/yourhome/mpich2 //建議自行建立目錄安裝
[shell]# make
[shell]# make install
再來是
[shell]# cd ~yourhome //到自己home目錄下
[shell]# vi .mpd.conf //建立文件
內容為
secretword=<secretword> (secretword可以依自己喜好打)
Ex:
secretword=abcd1234

MPI 安裝
[shell]# chmod 600 mpd.conf
[shell]# vi .bash_profiles
將PATH=$PATH:$HOME/bin
改成PATH=$HOME/mpich2/bin:$PATH:$HOME/bin
重登server
[shell]# vi mpd.hosts //在自己home目錄下建立hosts list文件
ex：
cluster1
cluster2
cluster3
cluster4

MPI constructs
MPI
Point-to-Point
Communication
Collective
Communication
Process Group Virtual Topology
Blocking
MPI_Send()
MPI_Receive()
Nonblocking
MPI_Isend()
MPI_Irecv()
Synchronization
MPI_Barrier()
Data Exchange
MPI_Bcast()
MPI_Scatter()
MPI_Gather()
Mpi_Alltoall()
Collective
Computation
MPI_Reduce()
MPI_Comm_group()
MPI_Comm_create()
MPI_Group_incl()
MPI_Group_rank()
MPI_Group_size()
MPI_Comm_free()
MPI_Cart_create()
MPI_Cart_coords()
MPI_Cart_shift()

MPI程式基本架構
#include "mpi.h"
MPI_Init();
Do some work or MPI function
example:
MPI_Send() / MPI_Recv()
MPI_Finalize();

MPI Ethernet Control and Data Flow
Source : Douglas M. Pase, “Performance of Voltaire InfiniBand in IBM 64-Bit Commodity HPC Clusters,” IBM White
Papers, 2005

MPI Communicator
0
1
2
3
4
56
7
8
MPI_COMM_WORLD

MPI Function
function int MPI_Init( int *argc, char *argv[])
功能起始MPI執行環境，必須在所有MPI function前使用，並可以將main的指令參
數(argc, argv)傳送到所有process
parameters int argc：參數數目
char* argv[]：參數內容
return value int：如果執行成功回傳MPI_SUCCESS，０
function int MPI_Finzlize()
功能結束MPI執行環境，在所有工作完成後必須呼叫
parameters

MPI Function
function int MPI_Comm_size( MPI_Comm comm, int *size)
功能取得總共有多少process數在該communicator
parameters comm：IN，MPI_COMM_WORLD
size：OUT，總計process數目
function int MPI_Comm_rank ( MPI_Comm comm, int *rank)
功能取得 process自己的process ID
rank：OUT，目前process ID

MPI Function
function int MPI_Send(void* buf, int count, MPI_Datatype datatype, int dest, int tag, MPI_Comm comm)
功能傳資料到指定的Process，使用Standard模式
parameters buf：IN要傳送的資料(變數)
count：IN，傳送多少筆
datatype：IN，設定傳送的資料型態
dest：IN，目標Process ID
tag：IN，設定頻道
comm：IN，MPI_COMM_WORLD
function int MPI_Recv(void* buf, int count, MPI_Datatype datatype, int source, int tag, MPI_Comm comm,
MPI_Status *status)
功能接收來自指定的Process資料
parameters buf：OUT，要接收的資料(變數)
count：IN，接收多少筆
datatype：IN，設定接收的資料型態
source：IN，接收的Process ID
tag：IN，設定頻道
status：OUT，取得MPI_Status

MPI Function
 Status：指出來源的process ID和傳送的tag，在C是使用MPI_Status的資料型態
typedef struct MPI_Status {
int count;
int cancelled;
int MPI_SOURCE; //來源ID
int MPI_TAG; //來源傳送的tag
int MPI_ERROR; //錯誤控制碼
} MPI_Status;
function double MPI_Wtime()
功能傳回一個時間(秒數，浮點數)代表目前時間，通常用來看程式執行的時間
parameters
return value double：傳回時間

MPI Function
function int MPI_Type_commit(MPI_Datatype *datatype);
功能建立datatype
parameters datatype：INOUT，新的datatype
function MPI_Type_free(MPI_Datatype *datatype);
功能釋放datatype
parameters datatype：INOUT，需釋放的datatype

MPI Function
function int MPI_Type_contiguous (int count, MPI_Datatype oldtype, MPI_Datatype *newtype)
功能將現有資料型態(MPI_Datatype)，簡單的重新定大小，形成新的資料型態，就是指將數個
相同型態的資料整合成一個
parameters count：IN，新型態的大小(指有幾個oldtype組成)
oldtype：IN，舊有的資料型態(MPI_Datatype)
newtype：OUT，新的資料型態

撰寫程式和執行的步驟
1. 啟動MPI環境
mpdboot -n 4 -f mpd.hosts //-n為啟動pc數量, mpd.hosts為pc清單
2. 撰寫MPI程式
vi hello.c
3. Compile
mpicc hello.c –o hello.o
4. 執行程式
mpiexec –n 4 ./hello.o //-n為process數量
5. 結束MPI
mpdallexit

MPI example : hello.c
#include "mpi.h"
#include <stdio.h>
#define SIZE 20
int main(int argc,char *argv[])
{
int numtasks, rank, dest, source, rc, count, tag=1;
char inmsg[SIZE];
char outmsg[SIZE];
double starttime, endtime;
MPI_Status Stat;
MPI_Datatype strtype;
MPI_Init(&argc,&argv); //起始MPI環境
MPI_Comm_rank(MPI_COMM_WORLD, &rank); //取得自己的process ID
MPI_Type_contiguous(SIZE, MPI_CHAR, &strtype); //設定新的資料型態string
MPI_Type_commit(&strtype); //建立新的資料型態string
starttune=MPI_Wtime(); //取得目前時間

if (rank == 0) {
dest = 1;
source = 1;
strcpy(outmsg,"Who are you?");
//傳送訊息到process 0
rc = MPI_Send(outmsg, 1, strtype, dest, tag, MPI_COMM_WORLD);
printf("process %d has sended message: %sn",rank, outmsg);
//接收來自process 1 的訊息
rc = MPI_Recv(inmsg, 1, strtype, source, tag, MPI_COMM_WORLD, &Stat);
printf("process %d has received: %sn",rank, inmsg);
}
else if (rank == 1) {
dest = 0;
source = 0;
strcpy(outmsg,"I am process 1");
rc = MPI_Recv(inmsg, 1, strtype, source, tag, MPI_COMM_WORLD, &Stat);
printf("process %d has received: %sn",rank, inmsg);
rc = MPI_Send(outmsg, 1 , strtype, dest, tag, MPI_COMM_WORLD);
printf("process %d has sended message: %sn",rank, outmsg);
}

endtime=MPI_Wtime(); // 取得結束時間
//使用MPI_CHAR來計算實際收到多少資料
rc = MPI_Get_count(&Stat, MPI_CHAR, &count);
printf("Task %d: Received %d char(s) from task %d with tag %d and use
time is %f n", rank, count, Stat.MPI_SOURCE, Stat.MPI_TAG,
endtime-starttime);
MPI_Type_free(&strtype); //釋放string資料型態
MPI_Finalize(); //結束MPI
}
process 0 has sended message: Who are you?
process 1 has received: Who are you?
process 1 has sended message: I am process 1
Task 1: Received 20 char(s) from task 0 with tag 1 and use time is 0.001302
process 0 has received: I am process 1
Task 0: Received 20 char(s) from task 1 with tag 1 and use time is 0.002133

Collective Communication Routines
function int MPI_Barrier(MPI_Comm comm)
功能當程式執行到Barrier便會block，等待所有其他process也執行到Barrier，當所
有Group內的process均執行到Barrier便會取消block繼續往下執行
 Types of Collective Operations:
 Synchronization : processes wait until all members of the group have reached
the synchronization point.
 Data Movement : broadcast, scatter/gather, all to all.
 Collective Computation (reductions) : one member of the group collects data
from the other members and performs an operation (min, max, add, multiply,
etc.) on that data.

MPI_Bcast
function int MPI_Bcast(void* buffer, int count, MPI_Datatype datatype, int source(root), MPI_Comm
comm)
功能將訊息廣播出去，讓所有人接收到相同的訊息
parameters buffer：INOUT，傳送的訊息，也是接收訊息的buff
count：IN，傳送多少個訊息
datatype：IN，傳送的資料型能
source(標準root)：IN，負責傳送訊息的process

MPI_Gather
function int MPI_Gather(void* sendbuf, int sendcount, MPI_Datatype sendtype, void*
recvbuf, int recvcount, MPI_Datatype recvtype, int destine, MPI_Comm
comm)
功能將分散在各個process 所傳送的訊息，整合起來，然後傳送到指定的process接
收
parameters sendbuf：IN，傳送的訊息
sendcount：IN，傳送多少個
sendtype：IN，傳送的型態
recvbuf：OUT，接收訊息的buf
recvcount：IN，接收多少個
recvtype：IN，接收的型態
destine：IN，負責接收訊息的process

MPI_Gather

MPI_Allgather
function int MPI_Allgather(void* sendbuf, int sendcount, MPI_Datatype sendtype, void*
recvbuf, int recvcount, MPI_Datatype recvtype, MPI_Comm comm)
功能將分散在各個process 所傳送的訊息，整合起來，然後廣播到所有process
sendcount：IN，傳送多少個
sendtype：IN，傳送的型態
recvcount：IN，接收多少個
recvtype：IN，接收的型態

MPI_Allgather

MPI_Reduce
function int MPI_Reduce(void* sendbuf, void* recvbuf, int count, MPI_Datatype datatype,
MPI_Op op, int destine, MPI_Comm comm)
功能在傳送時順便做一些Operation(ex：MPI_SUM做加總)，然後將結果送到
destine process
count：IN，傳送接收多少個
datatype：IN，傳送接收的資料型態
op：IN，想要做的動作
destine：IN，接收訊息的process ID

MPI_Reduce
MPI Reduction Operation C Data Types
MPI_MAX maximum integer, float
MPI_MIN minimum integer, float
MPI_SUM sum integer, float
MPI_PROD product integer, float
MPI_LAND logical AND integer
MPI_BAND bit-wise AND integer, MPI_BYTE
MPI_LOR logical OR integer
MPI_BOR bit-wise OR integer, MPI_BYTE
MPI_LXOR logical XOR integer
MPI_BXOR bit-wise XOR integer, MPI_BYTE
MPI_MAXLOC max value and location float, double and long double
MPI_MINLOC min value and location float, double and long double

MPI example : matrix.c(1)
#include <mpi.h>
#include <stdio.h>
#include <stdlib.h>
#define RANDOM_SEED 2882 //random seed
#define MATRIX_SIZE 800 //sequare matrix width the same to height
#define NODES 4//this is numbers of nodes. minimum is 1. don't use < 1
#define TOTAL_SIZE (MATRIX_SIZE * MATRIX_SIZE)//total size of
MATRIX
#define CHECK
int main(int argc, char *argv[]){
int i,j,k;
int node_id;
int AA[MATRIX_SIZE][MATRIX_SIZE];
int BB[MATRIX_SIZE][MATRIX_SIZE];
int CC[MATRIX_SIZE][MATRIX_SIZE];

#ifdef CHECK
int _CC[MATRIX_SIZE][MATRIX_SIZE]; //sequence user, use to check
the parallel result CC
#endif
int check = 1;
int print = 0;
int computing = 0;
double time,seqtime;
int numtasks;
int tag=1;
int node_size;
MPI_Status stat;
MPI_Datatype rowtype;
srand( RANDOM_SEED );

MPI_Init(&argc,&argv);
MPI_Comm_rank(MPI_COMM_WORLD, &node_id);
MPI_Comm_size(MPI_COMM_WORLD, &numtasks);
if (numtasks != NODES){
printf("Must specify %d processors. Terminating.n", NODES);
MPI_Finalize();
return 0;
}
if (MATRIX_SIZE%NODES !=0){
printf("Must MATRIX_SIZE%NODES==0n", NODES);
MPI_Finalize();
return 0;
}
MPI_Type_contiguous(MATRIX_SIZE, MPI_FLOAT, &rowtype);
MPI_Type_commit(&rowtype);

/*create matrix A and Matrix B*/
if(node_id == 0){
for( i=0 ; i<MATRIX_SIZE ; i++){
for( j=0 ; j<MATRIX_SIZE ; j++){
AA[i][j] = rand()%10;
BB[i][j] = rand()%10;
}
}
}
/*send the matrix A and B to other node */
node_size = MATRIX_SIZE / NODES;

//send AA
if (node_id == 0)
for (i=1; i<NODES; i++)
MPI_Send(&AA[i*node_size][0], node_size, rowtype, i, tag,
MPI_COMM_WORLD);
else
MPI_Recv(&AA[node_id*node_size][0], node_size, rowtype, 0, tag,
MPI_COMM_WORLD, &stat);
//send BB
if (node_id == 0)
for (i=1; i<NODES; i++)
MPI_Send(&BB, MATRIX_SIZE, rowtype, i, tag,
MPI_COMM_WORLD);
else
MPI_Recv(&BB, MATRIX_SIZE, rowtype, 0, tag,
MPI_COMM_WORLD, &stat);

/*computing C = A * B*/
time = -MPI_Wtime();
for( i=node_id*node_size ; i<(node_id*node_size+node_size) ; i++){
computing = 0;
for( k=0 ; k<MATRIX_SIZE ; k++)
computing += AA[i][k] * BB[k][j];
CC[i][j] = computing;
}
}
MPI_Allgather(&CC[node_id*node_size][0], node_size, rowtype, &CC,
node_size, rowtype, MPI_COMM_WORLD);
time += MPI_Wtime();

#ifdef CHECK
seqtime = -MPI_Wtime();
if(node_id == 0){
for( i=0 ; i<MATRIX_SIZE ; i++){
computing = 0;
for( k=0 ; k<MATRIX_SIZE ; k++)
computing += AA[i][k] * BB[k][j];
_CC[i][j] = computing;
}
}
}
seqtime += MPI_Wtime();

/* check result */
if(node_id == 0){
for( i=0 ; i<MATRIX_SIZE; i++){
if( CC[i][j] != _CC[i][j]){
check = 0;
break;
}
}
}
}

/*print result */
#endif
if(node_id ==0){
printf("node_id=%dncheck=%snprocessing
time:%fnn",node_id,(check)?"success!":"failure!", time);
#ifdef CHECK
printf("sequent time:%fn", seqtime);
#endif
}
MPI_Type_free(&rowtype);
MPI_Finalize();
return 0;
}

Reference
 Top 500 http://www.top500.org/
 Maarten Van Steen, Andrew S. Tanenbaum, “Distributed Systems: Principles
and Paradigms ”
 System Threads Reference
http://www.unix.org/version2/whatsnew/threadsref.html
 Semaphone http://www.mkssoftware.com/docs/man3/sem_init.3.asp
 Richard Stones. Neil Matthew, “Beginning Linux Programming”
 W. Richard Stevens, “Networking APIs：Sockets and XTI“
 William W.-Y. Liang , “Linux System Programming”
 Michael J. Quinn, “Parallel Programming in C with MPI and OpenMP”
 Introduction to Parallel Computing
http://www.llnl.gov/computing/tutorials/parallel_comp/

Reference
 Michael J. Quinn, “Parallel Programming in C with MPI and OpenMP”
 Introduction to Parallel Computing
http://www.llnl.gov/computing/tutorials/parallel_comp/
 MPI standard http://www-unix.mcs.anl.gov/mpi/
 MPI http://www.llnl.gov/computing/tutorials/mpi/

books
 Michael J. Quinn , “Parallel Programming in C
with MPI and OpenMP, 1st Edition”
 http://books.google.com.tw/books?id=tDxNyGSXg5I
C&dq=parallel+programming+in+c+with+mpi+and+
openmp&pg=PP1&ots=I0QWyWECXI&sig=YwyUk
g9mKqWyxMnO1Hy7hkDc8dY&prev=http://www.g
oogle.com.tw/search%3Fsource%3Dig%26hl%3Dzh-
TW%26q%3DParallel%2Bprogramming%2Bin%2B
C%2Bwith%2Bmpi%2Band%2BopenMP%26meta%
3D%26btnG%3DGoogle%2B%25E6%2590%259C
%25E5%25B0%258B&sa=X&oi=print&ct=title#PP
A529,M1

MPI

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