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*[http://meditation-art.blogspot.com/2007/04/openmp-with-gcc-on-fedora-core-5.html openmp on FC5] How to use openmp on Fedora Core 5
*[http://meditation-art.blogspot.com/2007/04/openmp-with-gcc-on-fedora-core-5.html openmp on FC5] How to use openmp on Fedora Core 5
*[http://www.plutospin.com/OpenMPref.html OpenMP Reference] A one page (both sides) OpenMP reference sheet
*[http://www.plutospin.com/OpenMPref.html OpenMP Reference] A one page (both sides) OpenMP reference sheet
*[http://karminghenry.sinaman.com/gfortran.html OpenMP in gfortran information web page]


{{Parallel Computing}}
{{Parallel Computing}}

Revision as of 23:52, 5 February 2008

File:Openmp.png
OpenMP logo

The OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared memory multiprocessing programming in C/C++ and Fortran on many architectures, including Unix and Microsoft Windows platforms. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.

Jointly defined by a group of major computer hardware and software vendors, OpenMP is a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the desktop to the supercomputer.

An application built with the hybrid model of parallel programming can run on a computer cluster using both OpenMP and Message Passing Interface (MPI).

Introduction

An illustration of multithreading where the master thread forks off a number of threads which execute blocks of code in parallel

OpenMP is an implementation of multithreading, a method of parallelization whereby the master "thread" (a series of instructions executed consecutively) "forks" a specified number of slave "threads" and a task is divided among them. The threads then run concurrently, with the runtime environment allocating threads to different processors.

The section of code that is meant to run in parallel is marked accordingly, with a preprocessor directive that will cause the threads to form before the section is executed. Each thread has an "id" attached to it which can be obtained using a function (called omp_get_thread_num() in C/C++ and OMP_GET_THREAD_NUM() in FORTRAN). The thread id is an integer, and the master thread has an id of "0". After the execution of the parallelized code, the threads "join" back into the master thread, which continues onward to the end of the program.

By default, each thread executes the parallelized section of code independently. "Work-sharing constructs" can be used to divide a task among the threads so that each thread executes its allocated part of the code. Both Task parallelism and Data parallelism can be achieved using OpenMP in this way.

The runtime environment allocates threads to processors depending on usage, machine load and other factors. The number of threads can be assigned by the runtime environment based on environment variables or in code using functions. The OpenMP functions are included in a header file labelled "omp.h" in C/C++.

History

The OpenMP Architecture Review Board (ARB) published its first standard, OpenMP for FORTRAN 1.0, in October of 1997. October the following year they released the C/C++ standard. 2000 saw version 2.0 of the FORTRAN standard with version 2.0 of the C/C++ standard being released in 2002. The current version is 2.5. It is a combined C/C++/FORTRAN standard, which was released in 2005.

The core elements

Chart of OpenMP Constructs

The core elements of OpenMP are the constructs for thread creation, work load distribution (work sharing), data environment management, thread synchronization, user level runtime routines and environment variables.

A compiler directive in C/C++ is called a pragma (pragmatic information). It is a preprocessor directive, thus it is declared with a hash (#). Compiler directives specific to OpenMP in C/C++ are written in codes as follows: 

#pragma omp <rest of pragma>

The OpenMP specific pragmas are listed below:

Thread creation

omp parallel. It is used to fork additional threads to carry out the work enclosed in the construct in parallel. The original process will be denoted as master thread with thread ID 0.

Example: Display "Hello, world" using multiple threads. 

int main(int argc, char* argv[])
{
  #pragma omp parallel  
  printf("Hello, world.\n");
  return 0;
}

Work-sharing constructs

used to specify how to assign independent work to one or all of the threads.

  • omp for or omp do: used to split up loop iterations among the threads
  • sections: assigning consecutive but independent code blocks to different threads
  • single: specifying a code block that is executed by only one thread, a barrier is implied in the end
  • master: similar to single, but the code block will be executed by the master thread only and no barrier implied in the end.

Example: initialize the value of a large array in parallel, using each thread to do a portion of the work 

#define N 100000
int main(int argc, char *argv[])
{
  int i, a[N];
  #pragma omp parallel for
  for (i=0;i<N;i++) 
     a[i]= 2*i;
  return 0;
}

OpenMP Clauses

Since OpenMP is a shared memory programming model, most variables in OpenMP code are visible to all threads by default. But sometimes private variables are necessary to avoid race condition and there is a need to pass values between the sequential part and the parallel region (the code block executed in parallel), so data environment management is introduced as data clauses by appending them to the OpenMP directive. The different types of clauses are

Data Scoping Clauses

  • shared: the data within a parallel region is shared, which means visible and accessible by all threads simultaneously. By default, all variables in the work sharing region are shared except the loop iteration counter.
  • private: the data within a parallel region is private to each thread, which means each thread will have a local copy and use it as a temporary variable. A private variable is not initialized and the value is not maintained for use outside the parallel region. By default, the loop iteration counter in the work-sharing region (if any) is private.
  • default: allows the programmer to state that the default data scoping within a parallel region will be either shared, private, or none. The none option forces the programmer to declare each variable in the parallel region as either shared or private.

Synchronization clauses

  • critical section: the enclosed code block will be executed by all threads but only one thread at a time, not simultaneously executed. It is often used to protect shared data from race condition.
  • atomic: similar to critical section, but advise the compiler to use special hardware instructions for better performance. Compilers may choose to ignore this suggestion from users and use critical section instead.
  • ordered: the structure block is executed in the order in which iterations would be executed in a sequential loop
  • barrier: each thread waits until all of the other threads of a team have reached this point. A work-sharing construct has an implicit barrier synchronization at the end.
  • nowait: specifies that threads completing assigned work can proceed. In the absence of this clause, threads would encounter a barrier synchronization at the end of the work sharing construct by default.

Scheduling clauses

  • schedule(type, chunk):This is useful if the work sharing construct is a do-loop or for-loop. The iteration(s) in the work sharing construct are allocated to threads. The scheduling of the threads are controlled by this clause. The three types of scheduling are:
  1. static: Here,all the threads are allocated iterations before they execute the loop iterations. The iterations are divided among threads equally by default.However, specifying an integer for the parameter "chunk" will allocate "chunk" number of contiguous iterations to a particular thread.
  2. dynamic:Here,some of the iterations are allocated to a smaller number of threads.Once a particular thread finishes its allocated iteration, it returns to get another one from the iterations that are left. The parameter "chunk" defines the number of contiguous iterations that are allocated to a thread at a time.
  3. guided:A large chunk of contiguous iterations are allocated to each thread dynamically (as above). The chunk size decreases exponentially with each successive allocation to a minimum size specified in the parameter "chunk"

IF control

  • if: This will cause the threads to parallelize the task only if a condition is met. Otherwise the code block executes serially.

Initialization

  • firstprivate: the data is private to each thread, but initialized using the value of the variable using the same name from the master thread.
  • lastprivate: the data is private to each thread. The value of this private data will be copied to a global variable using the same name outside the parallel region if current iteration is the last iteration in the parallelized loop. A variable can be both firstprivate and lastprivate.
  • threadprivate: The data is a global data, but it is private in each parallel region during the runtime. The difference between threadprivate and private is the global scope associated with threadprivate and the preserved value across parallel regions.

Data copying

  • copyin: similar to firstprivate for private variables, threadprivate variables are not initialized, unless using copyin to pass the value from the corresponding global variables. No copyout is needed because the value of a threadprivate variable is maintained throughout the execution of the whole program.
  • copyprivate: used with single to support the copying of data values from private objects on one thread (the single thread) to the corresponding objects on other threads in the team.

Reduction

  • reduction(operator|intrinsic:list): the variable has a local copy in each thread, but the values of the local copies will be summarized (reduced) into a global shared variable. This is very useful if a particular operation (specified in "operator" for this particular clause) on a datatype that runs iteratively so that its value at a particular iteration depends on its value at a previous iteration. Basically, the steps that lead up to the operational increment are parallelized, but the threads gather up and wait before updating the datatype, then increments the datatype in order so as to avoid racing condition. This would be required in parallelizing Numerical Integration of functions and Differential Equations, as a common example.

Others

  • flush: The value of this variable is restored from the register to the memory for using this value outside of a parallel part
  • master:Executed only by the master thread (the thread which forked off all the others during the execution of the OpenMP directive).No implicit barrier; other team members (threads) not required to reach.

User-level runtime routines

Used to modify/check the number of threads, detect if the execution context is in a parallel region, how many processors in current system, set/unset locks, timing functions, etc.

Environment variables

a method to alter the execution features of OpenMP applications. Used to control loop iterations scheduling, default number of threads, etc.

Sample Programs

In this section, some sample programs are provided to illustrate the concepts explained above.

Hello World

This is the most basic program, one that prints "hello world".

C/C++

#include <omp.h>
#include <stdio.h>

int main (int argc, char *argv[]) {
  int th_id, nthreads;
  #pragma omp parallel private(th_id)
  {
    th_id = omp_get_thread_num();
    printf("Hello World from thread %d\n", th_id);
    #pragma omp barrier
    if ( th_id == 0 ) {
      nthreads = omp_get_num_threads();
      printf("There are %d threads\n",nthreads);
    }
  }
  return 0;
}

Fortran 77

      PROGRAM HELLO
      INTEGER ID, NTHRDS
      INTEGER OMP_GET_THREAD_NUM, OMP_GET_NUM_THREADS
C$OMP PARALLEL PRIVATE(ID)
      ID = OMP_GET_THREAD_NUM()
      PRINT *, 'HELLO WORLD FROM THREAD', ID
C$OMP BARRIER
      IF ( ID .EQ. 0 ) THEN
        NTHRDS = OMP_GET_NUM_THREADS()
        PRINT *, 'THERE ARE', NTHRDS, 'THREADS'
      END IF
C$OMP END PARALLEL
      END

Free form Fortran 90

program hello90
use omp_lib
integer:: id, nthreads
  !$omp parallel private(id)
  id = omp_get_thread_num()
  write (*,*) 'Hello World from thread', id
  !$omp barrier
  if ( id == 0 ) then
    nthreads = omp_get_num_threads()
    write (*,*) 'There are', nthreads, 'threads'
  end if
  !$omp end parallel
end program

Clauses in work-sharing constructs (in C/C++)

The application of some OpenMP clauses are illustrated in the simple examples in this section. The piece of code below updates the elements of an array "b" by performing a simple operation on the elements of an array "a". The parallelization is done by the OpenMP directive "#pragma". The scheduling of tasks is dynamic. Notice how the iteration counters "j" and "k" have to be made private, whereas the primary iteration counter "i" is private by default. The task of running through "i" is divided among multiple threads, and each thread creates its own versions of "j" and "k" in its execution stack, thus doing the full task allocated to it and updating the allocated part of the array "b" at the same time as the other threads. 

#define CHUNKSIZE 1 /*defines the chunk size as 1 contiguous iteration*/
/*forks off the threads*/
#pragma omp parallel schedule(dynamic, CHUNKSIZE) private(j,k) 
{
 /*Starts the work sharing construct*/
 #pragma omp for
 for(i = 2; i <= N-1; i++)
    for(j = 2; j <= i; j++)
       for(k = 1; k <= M; k++)
          b[i][j] +=   a[i-1][j]/k + a[i+1][j]/k;
 }

The next piece of code is a common usage of the "reduction" clause to calculate reduced sums. Here, we add up all the elements of an array "a" with an "i" dependent weight using a for-loop which we parallelize using OpenMP directives and reduction clause. The scheduling is kept static. 

#define N 10000 /*size of a*/
void calculate(int); /*The function that calculates the elements of a*/
long w;
double a[N];
calculate(a);
sum = 0.0;
/*forks off the threads and starts the work-sharing construct*/
#pragma omp parallel for private(w) reduction(+:sum) schedule(static,1)
for(i = 0; i < N; i++)
   {
     w = i*i;
     sum = sum + w*a[i];
   }
printf("\n %lf",sum);

Implementations

OpenMP has been implemented in many commercial compilers. For instance, Visual C++ 2005 supports it (in its Professional and Team System editions [1]), and so do the Intel compilers for their x86 and IPF product series. Sun Studio supports the latest OpenMP 2.5 specification with productivity enhancements for Solaris OS (UltraSPARC and x86/x64) and is planning similar support for the Linux platform in its next release.

GCC 4.2 supports OpenMP, and some distributions (such as Fedora Core 5 gcc) have support for OpenMP in their GCC 4.1 based compilers.

Pros and Cons of OpenMP

Pros

  • Simple: need not deal with message passing as MPI does
  • Data layout and decomposition is handled automatically by directives.
  • Incremental parallelism: can work on one portion of the program at one time, no dramatic change to code is needed.
  • Unified code for both serial and parallel applications: OpenMP constructs are treated as comments when sequential compilers are used.
  • Original (serial) code statements need not, in general, be modified when parallelized with OpenMP. This reduces the chance of inadvertently introducing bugs.
  • Both coarse-grained and fine-grained parallelism are possible

Cons

  • Currently only runs efficiently in shared-memory multiprocessor platforms
  • Requires a compiler that supports OpenMP.
  • Scalability is limited by memory architecture.
  • Reliable error handling is missing.
  • Lacks fine-grained mechanisms to control thread-processor mapping.
  • Synchronization between a subset of threads is not allowed.

Performance expectations of OpenMP

One might expect to get N times less wall clock execution time (or N times speedup) when running a program parallelized using OpenMP on a N processor platform. However, this is seldom the case due to the following reasons:

  • A large portion of the program may not be parallelized by OpenMP, which sets theoretical upper limit of speedup according to Amdahl's law.
  • N processors in a SMP may bring N times computation power, but the memory bandwidth usually does not scale up N times. Quite often, the original memory path is shared by multiple processors and performance degradation may be observed when they compete for the shared memory bandwidth.
  • Many other common problems affecting the final speedup in parallel computing also apply to OpenMP, like load balancing and synchronization overhead.

OpenMP Benchmarks

There are some public domain OpenMP benchmarks for users to try.

This commercial benchmark is also very popular.

OpenMP learning resources online

See also

References

  • Quinn Michael J, Parallel Programming in C with MPI and OpenMP McGraw-Hill Inc. 2004. ISBN 0-07-058201-7
  • R. Chandra, R. Menon, L. Dagum, D. Kohr, D. Maydan, J. McDonald, Parallel Programming in OpenMP. Morgan Kaufmann, 2000. ISBN 1558606718
  • R. Eigenmann (Editor), M. Voss (Editor), OpenMP Shared Memory Parallel Programming: International Workshop on OpenMP Applications and Tools, WOMPAT 2001, West Lafayette, IN, USA, July 30-31, 2001. (Lecture Notes in Computer Science). Springer 2001. ISBN 354042346X
  • B.Chapman, G. Jost, R. vanderPas, D.J. Kuck, Using OpenMP: Portable Shared Memory Parallel Programming. The MIT Press (October 31, 2007). ISBN 0262533022
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