praktikum: verteiltes rechnen und parallelprogrammierung

Praktikum:

Verteiltes Rechnen und Parallelprogrammierung

„Introduction to MPI“

Agenda

1) MPI für Java

2) 2. Übungszettel

3) Projektpräferenzen ?

4) Nächste Woche: 3. Übungszettel, Projektauswahl, Konzepte

MPI – Application Example

Colliding Galaxies

5

Colliding Galaxies with 60,000 Particles

Cluster Formation

7

Structure Formation with two million Particles

MPI Recap

Message-Passing Programming

Paradigm

• Each processor in a message-passing program runs a sub-program

– written in a convential sequential language

– all variables are private

– communicate via special subroutine calls

M

P

M

P

M

P

Memory

Processors

Interconnection Network

Messages

• Messages are packets of data moving between sub-

programs

• The message passing system has to be told the

following information

– Sending processor

– Source location

– Data type

– Data length

– Receiving processor(s)

– Destination location

– Destination size

Messages

• Access:

– Each sub-program needs to be connected to a message

passing system

• Addressing:

– Messages need to have addresses to be sent to

• Reception:

– It is important that the receiving process is capable of

dealing with the messages it is sent

• A message passing system is similar to:

– Post-office, Phone line, Fax, E-mail, etc

Point-to-Point Communication

• Simplest form of message passing

• One process sends a message to another

• Several variations on how sending a

message can interact with execution of the

sub-program

Point-to-Point variations

• Synchronous Sends

– provide information about the completion of the message

– e.g. fax machines

• Asynchronous Sends

– Only know when the message has left

– e.g. post cards

• Blocking operations

– only return from the call when operation has completed

• Non-blocking operations

– return straight away - can test/wait later for completion

Collective Communications

• Collective communication routines are higher level

routines involving several processes at a time

• Can be built out of point-to-point communications

• Barriers

– synchronise processes

• Broadcast

– one-to-many communication

• Reduction operations

– combine data from several processes to produce a single (usually)

result

Introduction

Hint: we are using mpjExpress because it can also be run in

a multi-core environment.

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Two Important Concepts

• Two fundamental concepts of parallel programming are:

– Domain decomposition

– Functional decomposition

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Domain Decomposition

Image taken from https://computing.llnl.gov/tutorials/parallel_comp/

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Functional Decomposition

Image taken from https://computing.llnl.gov/tutorials/parallel_comp/

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)

http://www-unix.mcs.anl.gov/mpi/mpich2/



http://open-mpi.org

http://open-mpi.org

http://open-mpi.org

http://mpj-express.org



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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node6

Allegro

(cluster head node) node1

node3

node2

node4

node5

node7

Distributed Memory Cluster

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Minimal MPI Java Program

import mpi.*

class Hello {

static public void main(String[] args) {

MPI.Init(args) ;

int myrank = MPI.COMM_WORLD.Rank() ;

if(myrank == 0) {

char[] message = “Hello, there”.toCharArray() ;

MPI.COMM_WORLD.Send(message, 0, message.length, MPI.CHAR, 1, 99) ; }

else {

char[] message = new char [20] ;

MPI.COMM_WORLD.Recv(message, 0, 20, MPI.CHAR, 0, 99) ;

System.out.println(“received:” + new String(message) + “:”) ; }

MPI.Finalize() ; } }

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Steps involved in executing the “Hello World!” program

1. Let’s logon to the cluster head node

2. Write the Hello World program

3. Compile the program

4. Write the machines files

5. Start MPJ Express daemons

6. Execute the parallel program

7. Stop MPJ Express daemons

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Step1: Logon to the head node

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Step 2: Write the Hello World Program

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Step 3: Compile the code

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Step 4: Write the machines file

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Step 5: Start MPJ Express daemons

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Step 6: Execute the parallel program

aamir@barq:~/projects/mpj-user> mpjrun.sh -np 6

-headnodeip 10.3.20.120 -dport 11050 HelloWorld

..

Hi from process <3> of total <6>






…

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Step 7: Stop the MPJ Express daemons

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COMM WORLD Communicator

import java.util.*;

import mpi.*;

..

// Initialize MPI

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

// Get total number of processes and rank

size = 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 processes


..

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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 processes


..

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Single Program Multiple Data (SPMD) Model

import java.util.*;

import mpi.*;

public class HelloWorld {




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 {




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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• It is possible to send message for each basic datatype:

– Floats (MPI.FLOAT), Integers (MPI.INT), Doubles (MPI.DOUBLE)

…

– Java Objects (MPI.OBJECT)

• Each message contains a “tag”—an identifier

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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


39

Blocking Send() and Recv() Methods

public void Send(Object buf, int offset, int count,

Datatype datatype, int dest, int tag) throws MPIException

public Status Recv(Object buf, int offset, int count,

Datatype datatype, int src, int tag) throws MPIException

mpjrun.sh -np 5 ToyExample MPJ Express (0.38) is started in the multicore configuration 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4

41

Blocking and Non-blocking Point-to-Point Comm

• 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()

• Non-blocking versions provide overlapping of

computation and communication:

– Asynchronous communication

• Non-blocking methods return a Request object: – Wait() //waits until communication completes

– Test() //test if the communication has finished

Standard Synchronous Ready Buffered

Blocking Send()

Recv()

Ssend() Rsend() Bsend()

Non-blocking Isend()

Irecv()

Issend() Irsend() Ibsend()

Non-blocking Point-to-Point Comm

43

Blocking vs. non-blocking

44

Non-blocking Point-to-Point Comm

public Request Isend(Object buf, int offset, int count,

Datatype datatype, int dest, int tag) throws MPIException

public Request Irecv(Object buf, int offset, int count,

Datatype datatype, int src, int tag) throws MPIException

public Status Wait() throws MPIException

public Status Test() throws MPIException

45

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

46

Latency on MultiCore

47

Throughput on MultiCore

48

Latency on GigE

49

Throughput on GigE

50

Collective communications

• Provided as a convenience for application developers:

– Save significant development time

– Efficient algorithms may be used

– Stable (tested)

• Built on top of point-to-point communications

• These operations include:

– Broadcast, Barrier, Reduce, Allreduce, Alltoall,

Scatter, Scan, Allscatter

– Versions that allows displacements between the data

Broadcast, scatter, gather, reduction

52

Broadcast, scatter, gather, allgather, alltoall

Scatter Toy-Example

Scatter Example: distribute file

55

Broadcast, scatter, gather, allgather, alltoall

public void Bcast(Object buf, int offset, int count,

Datatype type, int root) throws MPIException

public void Scatter(Object sendbuf, int sendoffset, int sendcount, Datatype sendtype,

Object recvbuf, int recvoffset, int recvcount, Datatype recvtype,

int root) throws MPIException

public void Gather(Object sendbuf, int sendoffset, int sendcount, Datatype sendtype,

Object recvbuf, int recvoffset, int recvcount, Datatype recvtype,

int root) throws MPIException

public void Allgather(Object sendbuf, int sendoffset int sendcount, Datatype sendtype, Object recvbuf, int recvoffset, int recvcount, Datatype recvtype) throws MPIException

public void Alltoall(Object sendbuf, int sendoffset, int sendcount, Datatype sendtype,

Object recvbuf, int recvoffset, int recvcount, Datatype recvtype)

throws MPIException

56

Reduce collective operations

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reduce

allreduce

Pro

ce

sse

s

Data MPI.PROD MPI.SUM MPI.MIN MPI.MAX MPI.LAND MPI.BAND MPI.LOR MPI.BOR MPI.LXOR MPI.BXOR MPI.MINLOC MPI.MAXLOC

Pro

cesses

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1

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reduce

allreduce

Pro

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sse

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Data MPI.PROD MPI.SUM MPI.MIN MPI.MAX MPI.LAND MPI.BAND MPI.LOR MPI.BOR MPI.LXOR MPI.BXOR MPI.MINLOC MPI.MAXLOC

Pro

cesses

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public void Reduce(Object sendbuf, int sendoffset,

Object recvbuf, int recvoffset, int count,

Datatype datatype, Op op, int root)

throws MPIException

public void Allreduce(Object sendbuf, int sendoffset,

Object recvbuf, int recvoffset, int count,

Datatype datatype, Op op)

throws MPIException

59

Collective Communication Performance

60

MPJ Design

MPJ point to point communications (Base level)

mpjdev (MPJ Device level)

MPJ collective Communications (High level)

Hardware (NIC, Memory etc)

MPJ API

JNI Java NIO

Java Virtual Machine (JVM)

JNI

Native MPI

gmdev

Threads

API

smpdev

xdev

niodev

61

Summary

• MPJ Express is a Java messaging system that can be

used to write parallel applications:

– MPJ/Ibis and mpiJava are other similar software

• MPJ Express provides point-to-point communication

methods like Send() and Recv():

– Blocking and non-blocking versions

• Collective communications is also supported

praktikum: verteiltes rechnen und parallelprogrammierung

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