OpenMP

OpenMP

Original author(s)	OpenMP Architecture Review Board^[1]
Developer(s)	OpenMP Architecture Review Board^[1]

Stable release	4.5 / November 15, 2015 (2015-11-15)
Operating system	Cross-platform
Platform	Cross-platform
Type	Extension to C, C++, and Fortran; API
License	Various^[2]
Website	openmp.org

OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran,^[3] on most platforms, processor architectures and operating systems, including Solaris, AIX, HP-UX, Linux, OS X, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.^[2]^[4]^[5]

OpenMP is managed by the nonprofit technology consortium OpenMP Architecture Review Board (or OpenMP ARB), jointly defined by a group of major computer hardware and software vendors, including AMD, IBM, Intel, Cray, HP, Fujitsu, Nvidia, NEC, Red Hat, Texas Instruments, Oracle Corporation, and more.^[1]

OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer 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), such that OpenMP is used for parallelism within a (multi-core) node while MPI is used for parallelism between nodes. There have also been efforts to run OpenMP on software distributed shared memory systems,^[6] to translate OpenMP into MPI^[7]^[8] and to extend OpenMP for non-shared memory systems.^[9]

Introduction

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

History

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

Up to version 2.0, OpenMP 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 2005, an effort to standardize task parallelism was formed, which published a proposal in 2007, taking inspiration from task parallelism features in Cilk, X10 and Chapel.^[10]

Version 3.0 was released in May 2008. Included in the new features in 3.0 is the concept of tasks and the task construct,^[11] significantly broadening the scope of OpenMP beyond the parallel loop constructs that made up most of OpenMP 2.0.^[12]

Version 4.0 of the specification was released in July 2013.^[13] It adds or improves the following features: support for accelerators; atomics; error handling; thread affinity; tasking extensions; user defined reduction; SIMD support; Fortran 2003 support.^[14]

The current version is 4.5.

Note that not all compilers (and OSes) support the full set of features for the latest version/s.

The core elements

Chart of OpenMP constructs.

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

In C/C++, OpenMP uses #pragmas. The OpenMP specific pragmas are listed below.

Thread creation

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

Example (C program): Display "Hello, world." using multiple threads.

#include <stdio.h>

int main(void)
{
    #pragma omp parallel
    printf("Hello, world.\n");
    return 0;
}

Use flag -fopenmp to compile using GCC:

$ gcc -fopenmp hello.c -o hello

Output on a computer with two cores, and thus two threads:

Hello, world.
Hello, world.

However, the output may also be garbled because of the race condition caused from the two threads sharing the standard output.

Hello, wHello, woorld.
rld.

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, also called loop constructs.
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 part of the work

int main(int argc, char **argv)
{
    int a[100000];

    #pragma omp parallel for
    for (int i = 0; i < 100000; i++) {
        a[i] = 2 * i;
    }

    return 0;
}

The loop counter i is declared inside the parallel for loop in C99 style, which gives each thread a unique and private version of the variable.^[15]

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 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 OpenMP directive. The different types of clauses are

Data sharing attribute 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 counters in the OpenMP loop constructs are private.
default: allows the programmer to state that the default data scoping within a parallel region will be either shared, or none for 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.
firstprivate: like private except initialized to original value.
lastprivate: like private except original value is updated after construct.
reduction: a safe way of joining work from all threads after construct.

Synchronization clauses

critical: the enclosed code block will be executed by only one thread at a time, and not simultaneously executed by multiple threads. It is often used to protect shared data from race conditions.
atomic: the memory update (write, or read-modify-write) in the next instruction will be performed atomically. 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.
ordered: the structured 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 without waiting for all threads in the team to finish. In the absence of this clause, threads encounter a barrier synchronization at the end of the work sharing construct.

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 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.
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.
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 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.

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. For example, OMP_NUM_THREADS is used to specify number of threads for an application.

Sample programs

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

Hello World

A basic program that exercises the parallel, private and barrier directives, and the functions omp_get_thread_num and omp_get_num_threads (not to be confused).

C

This C program can be compiled using gcc-4.4 with the flag -fopenmp

#include <omp.h>
#include <stdio.h>
#include <stdlib.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 EXIT_SUCCESS;
}

C++

This C++ program can be compiled using GCC: g++ -Wall -Wextra -Werror -fopenmp test.cpp
NOTE: iostream is not thread-safe. Therefore, for instance, cout calls must be executed in critical areas or by only one thread (e.g. masterthread).

#include <iostream>
#include <omp.h>

int main()
{
  int th_id, nthreads;
  #pragma omp parallel private(th_id) shared(nthreads)
  {
    th_id = omp_get_thread_num();
    #pragma omp critical
    {
      std::cout << "Hello World from thread " << th_id << '\n';
    }
    #pragma omp barrier

    #pragma omp master
    {
      nthreads = omp_get_num_threads();
      std::cout << "There are " << nthreads << " threads" << '\n';
    }
  }

  return 0;
}

Fortran 77

Here is a Fortran 77 version.

      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

Fortran 90 free-form

Here is a Fortran 90 free-form version.

 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 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 OpenMP 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)
 {
  /*Starts the work sharing construct*/
  #pragma omp for schedule(dynamic, CHUNKSIZE)
  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 code sample 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(long *); /*The function that calculates the elements of a*/
 int i;
 long w;
 long a[N];
 calculate(a);
 long sum = 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 %li",sum);

An equivalent, less elegant, implementation of the above code is to create a local sum variable for each thread ("loc_sum"), and make a protected update of the global variable sum at the end of the process, through the directive critical. Note that this protection is critical, as explained elsewhere.

 ...
 long sum = 0, loc_sum;
 /*forks off the threads and starts the work-sharing construct*/
 #pragma omp parallel private(w,loc_sum)
 {
   loc_sum = 0;
   #pragma omp for schedule(static,1)
   for(i = 0; i < N; i++)
     {
       w = i*i;
       loc_sum = loc_sum + w*a[i];
     }
   #pragma omp critical
   sum = sum + loc_sum;
 }
 printf("\n %li",sum);

Implementations

OpenMP has been implemented in many commercial compilers. For instance, Visual C++ 2005, 2008, 2010, 2012 and 2013 support it (OpenMP 2.0, in Professional, Team System, Premium and Ultimate editions^[16]^[17]^[18]), as well as Intel Parallel Studio for various processors.^[19] Oracle Solaris Studio compilers and tools support the latest OpenMP specifications with productivity enhancements for Solaris OS (UltraSPARC and x86/x64) and Linux platforms. The Fortran, C and C++ compilers from The Portland Group also support OpenMP 2.5. GCC has also supported OpenMP since version 4.2.

Compilers with an implementation of OpenMP 3.0:

GCC 4.3.1
Mercurium compiler
Intel Fortran and C/C++ versions 11.0 and 11.1 compilers, Intel C/C++ and Fortran Composer XE 2011 and Intel Parallel Studio.
IBM XL C/C++ compiler^[20]
Sun Studio 12 update 1 has a full implementation of OpenMP 3.0^[21]

Several compilers support OpenMP 3.1:

GCC 4.7^[22]
Intel Fortran and C/C++ compilers 12.1^[23]
LLVM/Clang 3.7^[24]

Compilers supporting OpenMP 4.0:

GCC 4.9.0 for C/C++, GCC 4.9.1 for Fortran^[22]^[25]
Intel Fortran and C/C++ compilers 15.0^[26]
LLVM/Clang 3.7 (partial)^[24]

Auto-parallelizing compilers that generates source code annotated with OpenMP directives:

iPat/OMP
Parallware
PLUTO
ROSE (compiler framework)
S2P by KPIT Cummins Infosystems Ltd.

Several profilers and debuggers expressly support OpenMP:

Allinea Distributed Debugging Tool (DDT) – debugger for OpenMP and MPI codes
Allinea MAP – profiler for OpenMP and MPI codes
ompP – profiler for OpenMP
VAMPIR – profiler for OpenMP and MPI codes

Pros and cons

Pros:

Portable multithreading code (in C/C++ and other languages, one typically has to call platform-specific primitives in order to get multithreading).
Simple: need not deal with message passing as MPI does.
Data layout and decomposition is handled automatically by directives.
Scalability comparable to MPI on shared-memory systems.^[27]
Incremental parallelism: can work on one part 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.
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 OpenMP can have a big performance advantage over MPI.^[27]^[28]
Can be used on various accelerators such as GPGPU.^[29]

Cons:

Risk of introducing difficult to debug synchronization bugs and race conditions.^[30]^[31]
Currently only runs efficiently in shared-memory multiprocessor platforms (see however Intel's Cluster OpenMP and other distributed shared memory platforms).
Requires a compiler that supports OpenMP.
Scalability is limited by memory architecture.
No support for compare-and-swap.^[32]
Reliable error handling is missing.
Lacks fine-grained mechanisms to control thread-processor mapping.
High chance of accidentally writing false sharing code.

Performance expectations

One might expect to get an N times speedup when running a program parallelized using OpenMP on a N processor platform. 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 OpenMP, 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 OpenMP, like load balancing and synchronization overhead.

Thread affinity

Some vendors recommend setting the processor affinity on OpenMP threads to associate them with particular processor cores.^[33]^[34]^[35] This minimizes thread migration and context-switching cost among cores. It also improves the data locality and reduces the cache-coherency traffic among the cores (or processors).

Benchmarks

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

Learning resources online

References

1 2 3 "About the OpenMP ARB and". OpenMP.org. 2013-07-11. Retrieved 2013-08-14.
1 2 "OpenMP Compilers". OpenMP.org. 2013-04-10. Retrieved 2013-08-14.
1 2 Gagne, Abraham Silberschatz, Peter Baer Galvin, Greg. Operating system concepts (9th ed.). Hoboken, N.J.: Wiley. pp. 181–182. ISBN 978-1-118-06333-0.
↑ OpenMP Tutorial at Supercomputing 2008
↑ Using OpenMP – Portable Shared Memory Parallel Programming – Download Book Examples and Discuss
↑ Costa, J.J.; et al. (May 2006). "Running OpenMP applications efficiently on an everything-shared SDSM". Journal of Parallel and Distributed Computing. Elsevier. 66 (5): 647–658. doi:10.1016/j.jpdc.2005.06.018. Retrieved 12 October 2016.
↑ Basumallik, Ayon; Min, Seung-Jai; Eigenmann, Rudolf (2007). "Programming Distributed Memory Sytems [sic] using OpenMP". Proceedings of the 2007 IEEE International Parallel and Distributed Processing Symposium. New York: IEEE Press. doi:10.1109/IPDPS.2007.370397. A preprint is available on Chen Ding's home page; see especially Section 3 on Translation of OpenMP to MPI.
↑ Wang, Jue; Hu, ChangJun; Zhang, JiLin; Li, JianJiang (May 2010). "OpenMP compiler for distributed memory architectures". Science China Information Sciences. Springer Publishing. 53 (5): 932–944. doi:10.1007/s11432-010-0074-0. Retrieved 12 October 2016. (As of 2016 the KLCoMP software described in this paper does not appear to be publicly available)
↑ Cluster OpenMP (a product that used to be available for Intel C++ Compiler versions 9.1 to 11.1 but was dropped in 12.0)
↑ Ayguade, Eduard; Copty, Nawal; Duran, Alejandro; Hoeflinger, Jay; Lin, Yuan; Massaioli, Federico; Su, Ernesto; Unnikrishnan, Priya; Zhang, Guansong (2007). A proposal for task parallelism in OpenMP (PDF). Proc. Int'l Workshop on OpenMP.
↑ "OpenMP Application Program Interface, Version 3.0" (PDF). openmp.org. May 2008. Retrieved 2014-02-06.
↑ LaGrone, James; Aribuki, Ayodunni; Addison, Cody; Chapman, Barbara (2011). A Runtime Implementation of OpenMP Tasks. Proc. Int'l Workshop on OpenMP. pp. 165–178. CiteSeerX 10.1.1.221.2775. doi:10.1007/978-3-642-21487-5_13.
↑ "OpenMP 4.0 API Released". OpenMP.org. 2013-07-26. Retrieved 2013-08-14.
↑ "OpenMP Application Program Interface, Version 4.0" (PDF). openmp.org. July 2013. Retrieved 2014-02-06.
↑ http://supercomputingblog.com/openmp/tutorial-parallel-for-loops-with-openmp/
↑ Visual C++ Editions, Visual Studio 2005
↑ Visual C++ Editions, Visual Studio 2008
↑ Visual C++ Editions, Visual Studio 2010
↑ David Worthington, "Intel addresses development life cycle with Parallel Studio", SDTimes, 26 May 2009 (accessed 28 May 2009)
↑ "XL C/C++ for Linux Features", (accessed 9 June 2009)
↑ "Oracle Technology Network for Java Developers | Oracle Technology Network | Oracle". Developers.sun.com. Retrieved 2013-08-14.
1 2 "openmp – GCC Wiki". Gcc.gnu.org. 2013-07-30. Retrieved 2013-08-14.
↑ Submitted by Patrick Kennedy... on Fri, 09/02/2011 – 11:28 (2011-09-06). "Intel® C++ and Fortran Compilers now support the OpenMP* 3.1 Specification | Intel® Developer Zone". Software.intel.com. Retrieved 2013-08-14.
1 2 "Clang 3.7 Release Notes". llvm.org. Retrieved 2015-10-10.
↑ "GCC 4.9 Release Series – Changes". www.gnu.org.
↑ "OpenMP* 4.0 Features in Intel Compiler 15.0". Software.intel.com.
1 2 Amritkar, Amit; Tafti, Danesh; Liu, Rui; Kufrin, Rick; Chapman, Barbara (2012). "OpenMP parallelism for fluid and fluid-particulate systems". Parallel Computing. 38 (9): 501. doi:10.1016/j.parco.2012.05.005.
↑ Amritkar, Amit; Deb, Surya; Tafti, Danesh (2014). "Efficient parallel CFD-DEM simulations using OpenMP". Journal of Computational Physics. 256: 501. Bibcode:2014JCoPh.256..501A. doi:10.1016/j.jcp.2013.09.007.
↑ Frequently Asked Questions on OpenMP
↑ Detecting and Avoiding OpenMP Race Conditions in C++
↑ Alexey Kolosov, Evgeniy Ryzhkov, Andrey Karpov 32 OpenMP traps for C++ developers
↑ Stephen Blair-Chappell, Intel Corporation, Becoming a Parallel Programming Expert in Nine Minutes, presentation on ACCU 2010 conference
↑ Chen, Yurong (2007-11-15). "Multi-Core Software". Intel Technology Journal. Intel. 11 (4). doi:10.1535/itj.1104.08.
↑ "OMPM2001 Result". SPEC. 2008-01-28.
↑ "OMPM2001 Result". SPEC. 2003-04-01.

External links

Official website, includes the latest OpenMP specifications, links to resources, lively set of forums where questions can be asked and are answered by OpenMP experts and implementors
OpenMPCon, website of the OpenMP Developers Conference
IWOMP, website for the annual International Workshop on OpenMP
GOMP is GCC's OpenMP implementation, part of GCC
IBM Octopiler with OpenMP support
Blaise Barney, Lawrence Livermore National Laboratory site on OpenMP
ompca, an application in REDLIB project for the interactive symbolic model-checker of C/C++ programs with OpenMP directives
Combining OpenMP and MPI (PDF)
Mixing MPI and OpenMP
Measure and visualize OpenMP parallelism by means of a C++ routing planer calculating the Speedup factor

Parallel computing

General	Distributed computing Cloud computing High-performance computing

Levels	Bit Instruction Task Data Memory Loop Pipeline

Multithreading	Temporal Simultaneous Preemptive Cooperative

Theory	PRAM model Analysis of parallel algorithms Amdahl's law Gustafson's law Cost efficiency Karp–Flatt metric Slowdown Speedup

Elements	Process Thread Fiber Instruction window

Coordination	Multiprocessing Memory coherency Cache coherency Cache invalidation Barrier Synchronization Application checkpointing

Programming	Stream Processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm

Hardware	Flynn's taxonomy SISD SIMD MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Grid computer

APIs	Ateji PX Boost.Thread Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays MPI OpenMP OpenCL OpenHMPP OpenACC TPL PLINQ PVM POSIX Threads RaftLib UPC TBB

Problems	Embarrassingly parallel Software lockout Scalability Race condition Deadlock Livelock Starvation Deterministic algorithm Parallel slowdown

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