1 parallel computing—introduction to message passing interface (mpi)

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Parallel Computing—Introduction to Message Passing Interface

(MPI)

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Two Important Concepts• Two fundamental concepts of parallel

programming are: • Domain decomposition• Functional decomposition

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Domain Decomposition

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Functional Decomposition

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Message Passing Interface (MPI)• MPI is a standard (an interface or an API):

• It defines a set of methods that are used by application developers to write their applications

• MPI library implement these methods

• MPI itself is not a library—it is a specification document that is followed!

• MPI-1.2 is the most popular specification version

• Reasons for popularity:• Software and hardware vendors were involved

• Significant contribution from academia

• MPICH served as an early reference implementation

• MPI compilers are simply wrappers to widely used C and Fortran compilers

• History: • The first draft specification was produced in 1993

• MPI-2.0, introduced in 1999, adds many new features to MPI

• Bindings available to C, C++, and Fortran

• MPI is a success story:• It is the mostly adopted programming paradigm of IBM Blue Gene systems

• At least two production-quality MPI libraries:• MPICH2 (http://www-unix.mcs.anl.gov/mpi/mpich2/)

• OpenMPI (http://open-mpi.org)

• There’s even a Java library: • MPJ Express (http://mpj-express.org)

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Message Passing Model• Message passing model allows processors to

communicate by passing messages: • Processors do not share memory

• Data transfer between processors required cooperative operations to be performed by each processor:• One processor sends the message while other receives the

message

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Proc 6

Proc 0

Proc 1

Proc 3

Proc 2

Proc 4

Proc 5

Proc 7

message

CPU

Memory LANEthernetMyrinet

Infiniband etc

Distributed Memory Cluster

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Writing “Hello World” MPI Program

• MPI is very simple: • Initialize MPI environment:

• MPI_Init(&argc,&argv); // C Code • MPI.Init(args); // Java Code

• Send or receive message:• MPI_Send(..); // C Code • MPI.COMM_WORLD.Send(); // Java Code

• Finalize MPI environment• MPI_Finalize(); // C Code • MPI.Finalize(); // Java Code

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Hello World in C#include <stdio.h>#include <string.h>#include “mpi.h”

..

// Initialize MPI MPI_Init(&argsc,&&argsv);

// Find out the `id’ or `rank’ of current processMPI_Comm_Rank(MPI_COMM_WORLD,&my_rank); //get the rank

// Get total number of processesMPI_Comm_Size(MPI_COMM_WORLD,&p); //get total processor

// Print the rank of the processprintf(“Hello World from process no %d”,my_rank);

MPI_Finalize();

..

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Hello World in Java

import java.util.*;import mpi.*;

.. // Initialize MPI MPI.Init(args); // start up MPI

// Get total number of processes and ranksize = MPI.COMM_WORLD.Size(); rank = MPI.COMM_WORLD.Rank();

System.out.println(“Hello World <”+rank+”>”);

MPI_Finalize();

..

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After Initializationimport java.util.*;import mpi.*;

.. // Initialize MPI MPI.Init(args); // start up MPI

// Get total number of processes and ranksize = MPI.COMM_WORLD.Size(); rank = MPI.COMM_WORLD.Rank();

..

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What is size?

• Total number of processes in a communicator:• The size of MPI.COMM_WORLD is 6

import java.util.*;import mpi.*;

..

// Get total number of processessize = MPI.COMM_WORLD.Size();

..

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What is rank?

• The “unique” identify (id) of a process in a communicator:• Each of the six processes in MPI.COMM_WORLD has a distinct rank

or id

import java.util.*;import mpi.*;

..

// Get total number of processesrank = MPI.COMM_WORLD.Rank();

..

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Running “HelloWorld” in C• Write parallel code• Start MPICH2 daemon• Write machines file• Start the parallel job

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Running “Hello World” in Java• The code is executed on a cluster called

“Starbug”: • One head-node “holly” and eight compute-nodes

• Steps: • Write machines files• Bootstrap MPJ Express (or any MPI library) runtime• Write parallel application• Compile and execute

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QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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Write machines files

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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Bootstrap MPJ Express runtime

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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Write Parallel Program

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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Compile and Execute

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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Single Program Multiple Data (SPMD) Model

import java.util.*;import mpi.*;

public class HelloWorld { MPI.Init(args); // start up MPI

size = MPI.COMM_WORLD.Size(); rank = MPI.COMM_WORLD.Rank();

if (rank == 0) { System.out.println(“I am Process 0”); } else if (rank == 1) { System.out.println(“I am Process 1”); }

MPI.Finalize();}

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Single Program Multiple Data (SPMD) Model

import java.util.*;import mpi.*;

public class HelloWorld { MPI.Init(args); // start up MPI

size = MPI.COMM_WORLD.Size(); rank = MPI.COMM_WORLD.Rank();

if (rank%2 == 0) { System.out.println(“I am an even process”); } else if (rank%2 == 1) { System.out.println(“I am an odd process”); }

MPI.Finalize();}

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Point to Point Communication• The most fundamental facility provided by MPI• Basically “exchange messages between two

processes”: • One process (source) sends message• The other process (destination) receives message

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Point to Point Communication• It is possible to send message for each basic

datatype:• Floats, Integers, Doubles …

• Each message contains a “tag”—an identifier

Tag1

Tag2

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Process 6

Process 0

Process 1

Process 3

Process 2

Process 4

Process 5

Process 7

message

Integers Process 4 Tag COMM_WORLD

Point to Point Communication

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Blocking and Non-blocking • There are blocking and non-blocking version of send

and receive methods• Blocking versions:

• A process calls send() or recv(), these methods return when the message has been physically sent or received

• Non-blocking versions: • A process calls isend() or irecv(), these methods return

immediately • The user can check the status of message by calling test() or

wait()

• Note the “i” in isend() and irecv()• Non-blocking versions provide overlapping of

computation and communication: • It also depends on the “quality” of the implementation

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CPU waits

“Blocking”

send() recv()

Sender Receiver

time CPU waits

“Non Blocking”

isend() irecv()

Sender Receiver

time CPU

perform task

iwait()

CPU waitsiwait()

CPU waits

CPU perform task

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Modes of Send

• The MPI standard defines four modes of send:• Standard• Synchronous• Buffered• Ready

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Standard Mode (Eager send protocol used for small messages)

time ->control message to receiver

actual data sent

sender receiver

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Synchronous Mode (Rendezvous Protocol used for large messages)

time ->

control message to receiver

actual data sent

acknowledgement

sender receiver

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Performance Evaluation of Point to Point Communication

• Normally ping pong benchmarks are used to calculate: • Latency: How long it takes to send N bytes from

sender to receiver?• Throughput: How much bandwidth is achieved?

• Latency is a useful measure for studying the performance of “small” messages

• Throughput is a useful measure for studying the performance of “large” messages

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Latency on Fast Ethernet

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Throughput on Fast Ethernet

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Latency on Gigabit Ethernet

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Throughput on GigE

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Latency on Myrinet

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Throughput on Myrinet