Figure A7 Fortran and C examples of a typical loop-oriented program parallelized with OpenMP in Java

Print Data Matrix ECC200 in Java Figure A7 Fortran and C examples of a typical loop-oriented program parallelized with OpenMP
Figure A7 Fortran and C examples of a typical loop-oriented program parallelized with OpenMP
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The OpenMP parallel construct is used to create the team of threads This is followed by the worksharing construct to split up loop iterations among the threads: a DO construct in the case of Fortran and a for construct for C/C++ The program runs correctly in parallel and preserves sequential equivalence because no two threads update the same variable and any operations (such as calls to the combine() function) that do not commute or are not associative are carried out in the sequential order Notice that, according to the rules we gave earlier concerning the sharing of variables, the loop control variable i would be shared between threads The OpenMP specification, however, recognizes that it never makes sense to share the loop control index on a parallel loop, so it automatically creates a private copy of the loop control index for each thread By default, there is an implicit barrier at the end of any OpenMP workshare construct; that is, all the threads wait at the end of the construct and only proceed after all of the threads have arrived This barrier can be removed by adding a nowait clause to the worksharing construct:
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#pragma omp for nowait
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One should be very careful when using a nowait because, in most cases, these barriers are needed to prevent race conditions There are two other commonly used types of worksharing constructs in OpenMP: the single construct and the sections construct The single construct defines a block of code that will be executed by the first thread that encounters the construct The other threads skip the construct and wait at the implicit barrier at the end of the single (unless the nowait clause is used) We used this construct in the OpenMP reduction example in the Implementation Mechanisms design space We needed to copy the number of threads used in a parallel region into a shared variable To make sure only one thread wrote into this shared variable, we placed it in a single construct:
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#pragma omp single num_threads = omp_get_num_threads();
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The sections construct is used to set up a region of the program where distinct blocks of code are to be assigned to different threads Each block is defined with a section construct We do not use sections in any of our examples, so we will not discuss them in detail here Finally, it is very common in an OpenMP program to have a parallel construct immediately followed by a worksharing construct For example, in Fig A7, we had
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#pragma omp parallel { #pragma omp for }
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As a shortcut, OpenMP defines a combined construct:
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This is identical to the case where the parallel and for constructs are placed within separate pragmas
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OpenMP is a relatively simple API Most of the challenges in working with OpenMP arise from the details of how data is shared between threads and how data initialization interacts with OpenMP We will address some of the more common issues here, but to thoroughly understand the data environment associated with OpenMP, one should read the OpenMP specification [OMP] Details of memory synchronization are discussed in the section on synchronization (Section 63) in the Implementation Mechanisms design space We begin by defining the terms we will use to describe the data environment in OpenMP In a program, a variable is a container (or more concretely, a storage location in memory) bound to a name and holding a value Variables can be read and written as the program runs (as opposed to constants that can only be read) In OpenMP, the variable that is bound to a given name depends on whe ther the name appears prior to a parallel region, inside a parallel region, or following a parallel region When the variable is declared prior to a parallel region, it is by default shared and the name is always bound to the same variable OpenMP, however, includes clauses that can be added to parallel and to the worksharing constructs to control the data environment These clauses affect the variable bound to a name A private (list) clause directs the compiler to create, for each thread, a private (or local) variable for each name included in the list The names in the private list must have been defined and bound to shared variables prior to the parallel region The initial values of these new private variables are undefined, so they must be explicitly initialized Furthermore, after the parallel region, the value of a variable bound to a name appearing in a private clause for the region is undefined For example, in the Loop Parallelism pattern we presented a program to carry out a simple trapezoid integration The program consists of a single main loop in which values of the integrand are computed for a range of x values The x variable is a temporary variable set and then used in each iteration of the loop Hence, any dependencies implied by this variable can be removed by giving each thread its own copy of the variable This can be done simply with the private (x) clause, as shown in Fig A8 (The reduction clause in this example is discussed later)
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Figure A8 C program to carry out a trapezoid rule integration to compute (here comes equation)
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