Introduction to Parallel Programming at MCSR

• Message Passing Computing– Processes coordinate and communicate results via calls to message passing

library routines– Programmers “parallelize” algorithm and add message calls– At MCSR, this is via MPI programming with C or Fortran

• Sweetgum – Origin 2800 Supercomputer• Mimosa – Beowulf Cluster with 219 Nodes

• Shared Memory Computing– Processes or threads coordinate and communicate results via shared memory

variables– Care must be taken not to modify the wrong memory areas– At MCSR, this is via OpenMP programming with C or Fortran on sweetgum

Message Passing Computing at MCSR

• Process Creation• Slave and Master Processes• Static vs. Dynamic Work Allocation • Compilation• Models• Basics• Synchronous Message Passing• Collective Message Passing• Deadlocks• Examples

Message Passing Process Creation

• Dynamic– one process spawns other processes & gives them work– PVM– More flexible– More overhead - process creation and cleanup

• Static– Total number of processes determined before execution

begins– MPI

Message Passing Processes

• Often, one process will be the master, and the remaining processes will be the slaves

• Each process has a unique rank/identifier

• Each process runs in a separate memory space and has its own copy of variables

Message Passing Work Allocation

• Master Process– Does initial sequential processing– Initially distributes work among the slaves

• Statically or Dynamically– Collects the intermediate results from slaves– Combines into the final solution

• Slave Process– Receives work from, and returns results to, the master– May distribute work amongst themselves

(decentralized load balancing)

Message Passing Compilation

• Compile/link programs w/ message passing libraries using regular (sequential) compilers

• Fortran MPI example:include mpif.h

• C MPI example:#include “mpi.h”

• See http://www.mcsr.olemiss.edu/computing/mpi.html for exact MCSR MPI directory locations

Message Passing Models

• SPMD – Shared Program/Multiple Data– Single version of the source code used for each process– Master executes one portion of the program; slaves

execute another; some portions executed by both– Requires one compilation per architecture type– MPI

• MPMP – Multiple Program/Multiple Data– Once source code for master; another for slave– Each must be compiled separately– PVM

Message Passing Basics

• Each process must first establish the message passing environment

• Fortran MPI example:integer ierrorcall MPI_INIT (ierror)

• C MPI example:int ierror;ierror = MPI_Init(&argc, &argv);

Message Passing Basics

• Each process has a rank, or id number– 0, 1, 2, … n-1, where there are n processes

• With SPMD, each process must determine its own rank by calling a library routine

• Fortran MPI Example:integer comm, rank, ierrorcall MPI_COMM_RANK(MPI_COMM_WORLD, rank, ierror)

• C MPI Exampleierror = MPI_Comm_rank(MPI_COMM_WORLD, &rank);

Message Passing Basics

• Each process has a rank, or id number– 0, 1, 2, … n-1, where there are n processes

• Each process may use a library call to determine how many total processes it has to play with

• Fortran MPI Example:integer comm, size, ierrorcall MPI_COMM_SIZE(MPI_COMM_WORLD, size, ierror)

• C MPI Exampleierror = MPI_Comm_rank(MPI_COMM_WORLD, &size);

Message Passing Basics

• Each process has a rank, or id number– 0, 1, 2, … n-1, where there are n processes

• Once a process knows the size, it also knows the ranks (id #’s) of those other processes, and can send or receive a message to/from any other process.

• Fortran MPI Example:call MPI_SEND(buf, count, datatype, dest, tag, comm, ierror) ------DATA---------- ---EVELOPE--- -status------call MPI_RECV(buf, count, datatype, sourc,tag,comm, status,ierror)

MPI Send and Receive Arguments

• Buf starting location of data• Count number of elements• Datatype MPI_Integer, MPI_Real, MPI_Character…• Destination rank of process to whom msg being sent• Source rank of sender from whom msg being received

or MPI_ANY_SOURCE• Tag integer chosen by program to indicate type of message

or MPI_ANY_TAG• Communicator id’s the process team, e.g., MPI_COMM_WORLD• Status the result of the call (such as the # data items received)

Synchronous Message Passing

• Message calls may be blocking or nonblocking• Blocking Send

– Waits to return until the message has been received by the destination process

– This synchronizes the sender with the receiver

• Nonblocking Send– Return is immediate, without regard for whether the message has

been transferred to the receiver– DANGER: Sender must not change the variable containing the old

message before the transfer is done.– MPI_ISend() is nonblocking

Synchronous Message Passing

• Locally Blocking Send– The message is copied from the send parameter

variable to intermediate buffer in the calling process– Returns as soon as the local copy is complete– Does not wait for receiver to transfer the message from

the buffer– Does not synchronize– The sender’s message variable may safely be reused

immediately – MPI_Send() is locally blocking

Synchronous Message Passing

• Blocking Receive– The call waits until a message matching the given tag has been

received from the specified source process.– MPI_RECV() is blocking.

• Nonblocking Receive– If this process has a qualifying message waiting, retrieves that message

and returns– If no messages have been received yet, returns anyway– Used if the receiver has other work it can be doing while it waits– Status tells the receive whether the message was received– MPI_Irecv() is nonblocking– MPI_Wait() and MPI_Test() can be used to periodically check to see if

the message is ready, and finally wait for it, if desired

Collective Message Passing

• Broadcast– Sends a message from one to all processes in the group

• Scatter– Distributes each element of a data array to a different

process for computation• Gather

– The reverse of scatter…retrieves data elements into an array from multiple processes

Collective Message Passing w/MPI

MPI_Bcast() Broadcast from root to all other processes

MPI_Gather() Gather values for group of processes

MPI_Scatter() Scatters buffer in parts to group of processes

MPI_Alltoall() Sends data from all processes to all processes

MPI_Reduce() Combine values on all processes to single val

MPI_Reduce_Scatter() Broadcast from root to all other processes

MPI_Bcast() Broadcast from root to all other processes

Message Passing Deadlock

• Deadlock can occur when all critical processes are waiting for messages that never come, or waiting for buffers to clear out so that their own messages can be sent

• Possible Causes– Program/algorithm errors– Message and buffer sizes

• Solutions– Order operations more carefully– Use nonblocking operations– Add debugging output statements to your code to find the problem

Portable Batch System in SGI

• Sweetgum: – PBS Pro 5.1.4 is installed on sweetgum.

Queue Max # Processors Max # Running Memory Limit CPU Time Limit Special Validationper User Job Jobs per Queue per User Job per User Job Required

SM-defR 4 40 500mb 288 hrs NoMM-defR 4 20 1gb 288 hrs NoLM-defR 4 2 4gb 288 hrs YesLM-XR 4 1 4gb 672 hrs YesLM-8p 8 1 4gb 672 hrs YesLM-16p 16 1 4gb 672 hrs Yes

Portable Batch System in Linux

• Mimosa PBS Configuration: – PBS Pro 5.2.1 is installed on mimosa

Queue Max # Nodes Default Memory Default Shared Max # Running Special Validationper User Job (MB) Memory (MB) Jobs per Queue Required

MCSR-2N 2 400 256 32 NoMCSR-4N 4 600 256 12 YesMCSR-8N 8 800 256 8 YesMCSR-16N 16 1000 256 4 YesMCSR-32N 32 1200 256 4 YesMCSR-64N 64 1200 256 2 YesMCSR-CA 0 400 256 13 Yes

Sample Portable Batch System Script Sample

mimosa% vi example.pbs #!/bin/csh

#PBS -l nodes=4:csi (MIMOSA)#PBS –l ncpus=4 (SWEETGUM)#PBS -q MCSR-4N#PBS –N examplesetenv PGI /usr/local/apps/pgiset path=($path $PGI/linux86/bin)cd $PWDrm *.pbs.[eo]*pgf90 –o mpihello mpihello.f –lfmpich -lmpichmpirun -np 4 mpihello

mimosa % qsub example.pbs37537.mimosa.mcsr.olemiss.edu

Sample Portable Batch System Script Sample

Mimosa% qstatJob id Name User Time Use S Queue--------------- -------- --------- ----------- - -----------37521.mimosa 4_3.pbs r0829 01:05:17 R MCSR-2N 37524.mimosa 2_4.pbs r0829 01:00:58 R MCSR-2N 37525.mimosa GC8w.pbs lgorb 01:03:25 R MCSR-2N 37526.mimosa 3_6.pbs r0829 01:01:54 R MCSR-2N 37528.mimosa GCr8w.pbs lgorb 00:59:19 R MCSR-2N 37530.mimosa ATr7w.pbs lgorb 00:55:29 R MCSR-2N 37537.mimosa example tpirim 0 Q MCSR-16N 37539.mimosa try1 cs49011 00:00:00 R MCSR-CA

– Further information about PBS: http://www.mcsr.olemiss.edu/computing/pbs.html

For More Information

Hello World MPI Examples on Sweetgum (/usr/local/appl/mpihello) and Mimosa (/home/appl/mpihello):

/home/appl/mpihello/mpihello.f/home/appl/miphello/mpihello.c/home/appl/mpihello/example.pbs

WebsitesMPI at MCSR: http://www.mcsr.olemiss.edu/computing/mpi.htmlPVM at MCSR: http://www.mcsr.olemiss.edu/computing/pvm.htmlPBS at MCSR: http://www.mcsr.olemiss.edu/computing/pbs.htmlMimosa Cluster:

http://www.mcsr.olemiss.edu/computing/mimosa2.htmlMCSR Accounts:

http://www.mcsr.olemiss.edu/computing/acctrequest.htmlThe MPI Standard: http://www-unix.mcs.anl.gov/mpi/