Introduction to Parallel Programming with C and MPI at MCSR Part 1 MCSR Unix Camp
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Transcript of Introduction to Parallel Programming with C and MPI at MCSR Part 1 MCSR Unix Camp
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Introduction to Parallel Programming
with C and MPI at MCSR
Part 1
MCSR Unix Camp
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What is a Supercomputer?
Loosely speaking, it is a “large” computer with an architecture that has been optimized for bigger solving problems faster than a conventional desktop, mainframe, or server computer.
- Pipelining
- Parallelism (lots of CPUs or Computers)
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Supercomputers at MCSR: mimosa
- 253 CPU Intel Linux Cluster – Pentium 4- Distributed memory – 500MB – 1GB per node- Gigabit Ethernet
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What is Parallel Computing?
Using more than one computer (or processor) to complete a computational problem
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How May a Problem be Parallelized?
Data Decomposition
Task Decomposition
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Models of Parallel Programming
• 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 (128 CPUs)• Mimosa – Beowulf Cluster with 253 Nodes• Redwood – Altix 3700 Supercomputer (224 CPUs)
• 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
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Message Passing Computing at MCSR
• Process Creation• Manager and Worker Processes• Static vs. Dynamic Work Allocation • Compilation• Models• Basics• Synchronous Message Passing• Collective Message Passing• Deadlocks• Examples
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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
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Message Passing Processes
• Often, one process will be the manager, and the remaining processes will be the workers
• Each process has a unique rank/identifier
• Each process runs in a separate memory space and has its own copy of variables
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Message Passing Work Allocation
• Manager Process– Does initial sequential processing– Initially distributes work among the workers
• Statically or Dynamically
– Collects the intermediate results from workers– Combines into the final solution
• Worker Process– Receives work from, and returns results to, the manager– May distribute work amongst themselves
(decentralized load balancing)
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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”
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Message Passing Compilation
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Message Passing Models
• SPMD – Shared Program/Multiple Data– Single version of the source code used for each process– Manager executes one portion of the program; workers
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
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Message Passing Basics
• Each process must first establish the message passing environment
• Fortran MPI example:integer ierror
call MPI_INIT (ierror)
• C MPI example:MPI_Init(&argc, &argv);
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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 ExampleMPI_Comm_rank(MPI_COMM_WORLD, &rank);
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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 ExampleMPI_Comm_size(MPI_COMM_WORLD, &size);
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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.
• C Example:MPI_Send(buf, count, datatype, dest, tag, comm, ierror)
------DATA---------- ---EVELOPE--- -status------MPI_Recv(buf, count, datatype, sourc,tag,comm, status,ierror)
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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 messageor 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)
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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
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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
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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
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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
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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
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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
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Portable Batch System in SGI
• Sweetgum: – PBS Professional 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
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Portable Batch System on Mimosa
• Example Mimosa PBS Configuration: – PBS Professional
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
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Sample PBS Script
mimosa% vi example.pbs #!/bin/bash#PBS -l nodes=4 # MIMOSA#PBS –l ncpus=4 # SWEETGUM#PBS -q MCSR-CA#PBS –N examplecd $PWDrm *.pbs.[eo]*pgcc –o add_mpi.exe add_mpi.c –Mmpi-mpich #mimosampirun -np 4 add_mpi.exe
mimosa % qsub example.pbs37537.mimosa.mcsr.olemiss.edu
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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 using PBS at MCSR: http://www.mcsr.olemiss.edu/appssubpage.php?pagename=pbs_1.inc&menu=vMBPBS.inc
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For More Information
Hello World MPI Examples on Sweetgum (/usr/local/appl/mpihello) and Mimosa (/usr/local/apps/ppro/mpiworkshop):
http://www.mcsr.olemiss.edu/appssubpage.php?pagename=MPI_Ex1.inc
http://www.mcsr.olemiss.edu/appssubpage.php?pagename=MPI_Ex2.inc
http://www.mcsr.olemiss.edu/appssubpage.php?pagename=MPI_Ex3.inc
WebsitesMPI at MCSR: http://www.mcsr.olemiss.edu/appssubpage.php?pagename=mpi.inc
PBS at MCSR: http://www.mcsr.olemiss.edu/appssubpage.php?pagename=pbs_1.inc&menu=vMBPBS.inc
Mimosa Cluster: http://www.mcsr.olemiss.edu/supercomputerssubpage.php?pagename=mimosa2.inc
MCSR Accounts: http://www.mcsr.olemiss.edu/supercomputerssubpage.php?pagename=accounts.incThe
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MPI Programming Exercises
Hello World
sequential
parallel (w/MPI and PBS)
Add and Array of numbers
sequential
parallel (w/MPI and PBS)
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Log in to mimosa & get workshop files
A. Use secure shell to login to mimosa using your assigned training account:
ssh [email protected] [email protected]
See lab instructor for password.
B. Copy workshop files into your home directory by running: /usr/local/apps/ppro/prepare_mpi_workshop
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Examine, compile, and execute hello.c
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Examine hello_mpi.c
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Examine hello_mpi.c
Add macro to include theheader file for the MPI library calls.
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Examine hello_mpi.c
Add function call to initialize
the MPI environment
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Examine hello_mpi.c
Add function call find out how many parallel
processes there are.
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Examine hello_mpi.c
Add function call to find out which process
this is – the MPI process ID of this process.
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Examine hello_mpi.c
Add IF structure so that the manager/boss process can do
one thing, and everyone else (the workers/servants)
can do something else.
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Examine hello_mpi.c
All processes, whether manager or worker, must finalize MPI operations.
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Compile hello_mpi.c
Why won’t this compile?
You must link to the MPI library.
Compile it.
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Run hello_mpi.exe
On 1 CPU
On 2 CPUs
On 4 CPUs
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hello_mpi.pbs
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hello_mpi.pbs
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hello_mpi.pbs
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hello_mpi.pbs
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hello_mpi.pbs
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hello_mpi.pbs
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hello_mpi.pbs
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Submit hello_mpi.pbs
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Submit hello_mpi.pbs
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Submit hello_mpi.pbs
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Submit hello_mpi.pbs
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Examine add.c
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Compile & execute add.c
Edit add_mpi.c
or
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Edit add_mpi.c
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Edit add_mpi.c
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Edit add_mpi.c
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Edit add_mpi.c
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Edit add_mpi.c
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Edit add_mpi.c
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Compile/debug add_mpi.c
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Examine add_mpi.pbs
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Submit add_mpi.pbs