Open. MP - Wikipedia, the free encyclopedia. Open. MP (Open Multi- Processing) is an application programming interface (API) that supports multi- platform shared memorymultiprocessing programming in C, C++, and Fortran.
It consists of a set of compiler directives, library routines, and environment variables that influence run- time behavior. 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. 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.
This tutorial is adapted from my Julia introductory lecture taught in the graduate course Practical Computing for Economists, Department of Economics, University of. This document gives a brief overview of how to use SMP Linux systems for parallel processing. The most up-to-date information on SMP Linux is probably.
Both task parallelism and data parallelism can be achieved using Open. MP in this way. The runtime environment allocates threads to processors depending on usage, machine load and other factors. The runtime environment can assign the number of threads based on environment variables, or the code can do so using functions.
The Open. MP functions are included in a header file labelled omp. C/C++. History. October the following year they released the C/C++ standard. Fortran specifications with version 2. C/C++ specifications being released in 2.
Version 2. 5 is a combined C/C++/Fortran specification that was released in 2. Up to version 2. 0, Open. MP primarily specified ways to parallelize highly regular loops, as they occur in matrix- oriented numerical programming, where the number of iterations of the loop is known at entry time. This was recognized as a limitation, and various task parallel extensions were added to implementations. In 2. 00. 5, an effort to standardize task parallelism was formed, which published a proposal in 2. Cilk, X1. 0 and Chapel.
Included in the new features in 3. The Open. MP specific pragmas are listed below. Thread creation. The original thread will be denoted as master thread with thread ID 0. Example (C program): Display .
But sometimes private variables are necessary to avoid race conditions 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 sharing attribute clauses by appending them to the Open. MP directive. The different types of clauses are. Data sharing attribute clauses. By default, all variables in the work sharing region are shared except the loop iteration counter.
A private variable is not initialized and the value is not maintained for use outside the parallel region. By default, the loop iteration counters in the Open. MP loop constructs are private. C/C++, or shared, firstprivate, private, or none for Fortran. The none option forces the programmer to declare each variable in the parallel region using the data sharing attribute clauses. Synchronization clauses. It is often used to protect shared data from race conditions.
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It does not make the entire statement atomic; only the memory update is atomic. A compiler might use special hardware instructions for better performance than when using critical.
A work- sharing construct has an implicit barrier synchronization at the end. In the absence of this clause, threads encounter a barrier synchronization at the end of the work sharing construct. Scheduling clauses.
The iteration(s) in the work sharing construct are assigned to threads according to the scheduling method defined by this clause. The three types of scheduling are: 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. 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.
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. Otherwise the code block executes serially. Initialization. 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. 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. No copyout is needed because the value of a threadprivate variable is maintained throughout the execution of the whole program. Reduction. 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 prior 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.
The value of this variable is restored from the register to the memory for using this value outside of a parallel partmaster: Executed only by the master thread (the thread which forked off all the others during the execution of the Open. MP directive). No implicit barrier; other team members (threads) not required to reach. User- level runtime routines. Used to control loop iterations scheduling, default number of threads, etc. Therefore, for instance, cout calls must be executed in critical areas or by only one thread (e. The code sample 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 Open.
MP directive #pragma omp. 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 private(j,k). Here, we add up all the elements of an array a with an i- dependent weight using a for loop, which we parallelize using Open.
MP directives and reduction clause. The scheduling is kept static.#define N 1.
The function that calculates the elements of a*/inti; longw; longa. Note that this protection is critical, as explained elsewhere.. For instance, Visual C++ 2. Open. MP 2. 0, in Professional, Team System, Premium and Ultimate editions.
The Fortran, C and C++ compilers from The Portland Group also support Open. MP 2. 5. GCC has also supported Open. MP since version 4. Compilers with an implementation of Open.
MP 3. 0: GCC 4. 3. Mercurium compiler. Intel Fortran and C/C++ versions 1. Intel C/C++ and Fortran Composer XE 2.
Intel Parallel Studio. IBM XL C/C++ compiler.
This reduces the chance of inadvertently introducing bugs. Both coarse- grained and fine- grained parallelism are possible. In irregular multi- physics applications which do not adhere solely to the SPMD mode of computation, as encountered in tightly coupled fluid- particulate systems, the flexibility of Open. MP can have a big performance advantage over MPI. However, this seldom occurs for these reasons: When a dependency exists, a process must wait until the data it depends on is computed. When multiple processes share a non- parallel proof resource (like a file to write in), their requests are executed sequentially.
Therefore, each thread must wait until the other thread releases the resource. A large part of the program may not be parallelized by Open. MP, which means that the theoretical upper limit of speedup is limited according to Amdahl's law. N processors in a symmetric multiprocessing (SMP) may have N times the 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 Open.
MP, like load balancing and synchronization overhead. Thread affinity. It also improves the data locality and reduces the cache- coherency traffic among the cores (or processors). Benchmarks. Operating system concepts (9th ed.). ISBN 9. 78- 1- 1.
A proposal for task parallelism in Open. MP(PDF). Int'l Workshop on Open.
MP. A Runtime Implementation of Open. MP Tasks. Int'l Workshop on Open. MP. Cite. Seer. X: 1. Journal of Computational Physics. Bibcode: 2. 01. 4JCo. Ph. 2. 56. 5. 01.
A. Intel Technology Journal. Mc. Donald, Parallel Programming in Open. MP. Morgan Kaufmann, 2. Eigenmann (Editor), M. Voss (Editor), Open. MP Shared Memory Parallel Programming: International Workshop on Open. MP Applications and Tools, WOMPAT 2.
West Lafayette, IN, USA, July 3. Kuck (foreword), Using Open. MP: Portable Shared Memory Parallel Programming.
The MIT Press (October 3.