<A,B,C such that total iterations known at start of loop>for(A=C;A<B;A++) {<your code here>

OpenMP Reference Sheet for C/C++

Constructs<parallelize a for loop by breaking apart iterations into chunks>#pragma omp parallel for [shared(vars), private(vars), firstprivate(vars),lastprivate(vars), default(shared|none), reduction(op:vars), copyin(vars), if(expr),ordered, schedule(type[,chunkSize])]<A,B,C such that total iterations known at start of loop>for(A=C;A<B;A++) {<your code here><force ordered execution of part of the code. A=C will be guaranteed to execute
before A=C+1>#pragma omp ordered {<your code here>}}<parallelized sections of code with each section operating in one thread>#pragma omp parallel sections [shared(vars), private(vars), firstprivate(vars),lastprivate(vars), default(shared|none), reduction(op:vars), copyin(vars), if(expr)] {#pragma omp section {<your code here>}#pragma omp section {<your code here>
}
....
}

<force ordered execution of part of the code. A=C will be guaranteed to execute
before A=C+1>#pragma omp ordered {<your code here>}}<parallelized sections of code with each section operating in one thread>#pragma omp sections [private(vars), firstprivate(vars), lastprivate(vars),reduction(op:vars), nowait] {#pragma omp section {<your code here>}#pragma omp section {<your code here>}....
}<only one thread will execute the following. NOT always by the master thread>#pragma omp single {<your code here (only executed once)>}}

Directives

shared(vars) <share the same variables between all the threads><grand parallelization region with optional work-sharing constructs defining moreprivate(vars) <each thread gets a private copy of variables. Note that other than thespecific splitting of work and variables amongst threads. You may use work-sharing
master thread, which uses the original, these variables are not initialized toconstructs without a grand parallelization region, but it will have no effect (sometimesanything.>
useful if you are making OpenMP'able functions but want to leave the creation of threads firstprivate(vars) <like private, but the variables do get copies of their master threadto the user of those functions)>
values>#pragma omp parallel [shared(vars), private(vars), firstprivate(vars), lastprivate(vars), lastprivate(vars) <copy back the last iteration (in a for loop) or the last section (in adefault(private|shared|none), reduction(op:vars), copyin(vars), if(expr)] {sections) variables to the master thread copy (so it will persist even after the<the work-sharing constructs below can appear in any order, are optional, and canparallelization ends)>
be used multiple times. Note that no new threads will be created by the constructs.
default(private|shared|none) <set the default behavior of variables in the parallelization
They reuse the ones created by the above parallel construct.>construct. shared is the default setting, so only the private and none setting haveeffects. none forces the user to specify the behavior of variables. Note that even with<your code here (will be executed by all threads)>
shared, the iterator variable in for loops still is private by necessity >reduction(op:vars) <vars are treated as private and the specified operation(op, which<parallelize a for loop by breaking apart iterations into chunks>
can be +,*,-,&,|,&,&&,||) is performed using the private copies in each thread. The#pragma omp for [private(vars), firstprivate(vars), lastprivate(vars),
master thread copy (which will persist) is updated with the final value.>
reduction(op:vars), ordered, schedule(type[,chunkSize]), nowait]copyin(vars) <used to perform the copying of threadprivate vars to the other threads.
Similar to firstprivate for private vars.>
if(expr) <parallelization will only occur if expr evaluates to true.>schedule(type [,chunkSize]) <thread scheduling model>
typechunkSize
staticnumber of iterations per thread pre-assigned at beginning of loop(typical default is number of processors)

dynamic

number of iterations to allocate to a thread when available (typicaldefault is 1)

guidedhighly dependent on specific implementation of OpenMP

Function Based Locking < nest versions allow recursive locking>
void
void
void
void
int

omp_init_[nest_]lock(omp_lock_t*) <make a generic mutex lock>
omp_destroy_[nest_]lock(omp_lock_t*) <destroy a generic mutex lock>
omp_set_[nest_]lock(omp_lock_t*) <block until mutex lock obtained>
omp_unset_[nest_]lock(omp_lock_t*) <unlock the mutex lock>
omp_test_[nest_]lock(omp_lock_t*) <is lock currently locked by somebody>nowait <remove the implicit barrier which forces all threads to finish before continuation Settings and Control
int
omp_get_num_threads() <returns the number of threads used for the parallelin the construct>region in which the function was called>int
omp_get_thread_num() <get the unique thread number used to handle thisiteration/section of a parallel construct. You may break up algorithms into parts
based on this number.>
Synchronization/Locking Constructs <May be used almost anywhere, but willint
omp_in_parallel() <are you in a parallel construct>
only have effects within parallelization constructs.>
int
omp_get_max_threads() <get number of threads OpenMP can make>int
omp_get_num_procs() <get number of processors on this system><only the master thread will execute the following. Sometimes useful for special handlingint
omp_get_dynamic() <is dynamic scheduling allowed>of variables which will persist after the parallelization.>
int
omp_get_nested() <is nested parallelism allowed>#pragma omp master {double omp_get_wtime() <returns time (in seconds) of the system clock><your code here (only executed once and by the master thread).
double omp_get_wtick() <number of seconds between ticks on the system clock>}
void omp_set_num_threads(int) <set number of threads OpenMP can make>void omp_set_dynamic(int) <allow dynamic scheduling (note this does not make<mutex lock the region. name allows the creation of unique mutex locks.>
dynamic scheduling the default)>#pragma omp critical [(name)] {void omp_set_nested(int) <allow nested parallelism; Parallel constructs within other<your code here (only one thread allowed in at a time)>parallel constructs can make new threads (note this tends to be unimplemented}
in many OpenMP implementations)><force all threads to complete their operations before continuing>#pragma omp barrier<env vars- implementation dependent, but here are some common ones>
OMP_NUM_THREADS "number" <maximum number of threads to use>
OMP_SCHEDULE "type,chunkSize" <default #pragma omp schedule settings><like critical, but only works for simple operations and structures contained in one line ofcode>#pragma omp atomic<simple code operation, ex. a += 3; Typical supported operations are ++,--,+,*,Legend,/,&,^,<<,>>,| on primitive data types>
vars is a comma separated list of variables[optional parameters and directives]<force a register flush of the variables so all threads see the same memory><descriptions, comments, suggestions>#pragma omp flush[(vars)].... above directive can be used multiple times<applies the private clause to the vars of any future parallelize constructs encountered (a For mistakes, suggestions, and comments please email e_berta@plutospin.com
convenience routine)>#pragma omp threadprivate(vars)