Parallel Programming with OpenMP

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In some cases, it is necessary to synchronize all the threads.The #pragma omp barrier statement sets up a virtual hurdle: All the threads wait until the last one reaches the barrier before processing can continue. But think carefully before you introduce an artificial barrier – causing threads to suspend processing is going to affect the performance boost that parallelizing the program gave you. Threads that are waiting do not do any work. Listing 4 shows an example in which a barrier is unavoidable.

Listing 4

Unavoidable Barrier


The Calculationfunction() line in this listing calculates the second argument with reference to the first one. The arguments in this case could be arrays, and the calculation function could be a complex mathematical matrix operation. Here, it is essential to use #pragma omp barrier – the failure to do so would mean some threads would start with the second round of calculations before the values for the calculation in B become available.

Some OpenMP constructs (such as parallel, for, single) include an implicit barrier that you can explicitly disable by adding a nowait clause, as in #pragma omp for nowait. Other synchronize mechanisms include:

  • # pragma omp master {Code}: Code that is only executed once and only by the master thread.
  • # pragma omp single {Code}: Code that is only executed once, but not necessarily by the master thread
  • # pragma omp flush (Variables): Cached variables written back to main memory ensures a consistent view of the memory.

These synchronization mechanisms will help keep your code running smoothly in multi-processor environments.

Library Functions

OpenMP has a couple of additional functions, which are listed in Table 2. If you want to use them, you need to include the omp.h header file in C/C++. To make sure the program will build without OpenMP, it would make sense to add the #ifdef _OPENMP line for conditional compilation.

#ifdef _OPENMP
#include <omp.h>
threads = omp_get_num_threads();
threads = 1

Locking functions allow a thread to lock a resource, by reserving exclusive access

Figure 5: Formula for calculating pi by Gregor Leibniz.

(omp_set_lock()) to it. Other threads can then use a omp_test_lock() query to find out whether the resource is locked. This setup is useful if you want multiple threads to write data to a file, but want to restrict access to one thread at a time. When you use locking functions, be careful to avoid deadlocks.

A deadlock can occur if threads need resources but lock each other out. For example, if thread 1 successfully locks up resource A and is now waiting to use resource B, while thread 2 does exactly the opposite. Both threads wait forever.

Environmental Variables

Some environmental variables control the run-time behavior of OpenMP programs; the most important is OMP_NUM_THREADS. It specifies how many threads can operate in a parallel regions, because too many threads will actually slow down processing. The export OMP_NUM_THREADS=1 tells a program to run with just one thread in bash – just like a normal serial program.

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