In this document, we concentrate on the C++ language in particular, and use GCC to compile the examples.
To harness that power, it is becoming important for programmers to be knowledgeable in parallel programming — making a program execute multiple things simultaneously.
This document attempts to give a quick introduction to OpenMP, a simple C/C++/Fortran compiler extension that allows to add parallelism into existing source code without significantly having to entirely rewrite it.
Note: If your GCC complains that "-fopenmp" is valid for D but not for C++ when you try to use it, or does not recognize the option at all, your GCC version is too old. If your linker complains about missing GOMP functions, you forgot to specify "-fopenmp" in the linking.
More information: http://openmp.org/wp/openmp-compilers/
Here are two simple example programs demonstrating OpenMP.
You can compile them like this:
g++ tmp.cpp -fopenmp
#include <cmath> int main() { const int size = 256; double sinTable[size]; #pragma omp parallel for for(int n=0; n<size; ++n) sinTable[n] = std::sin(2 * M_PI * n / size); // the table is now initialized }
#include <cmath> int main() { const int size = 256; double sinTable[size]; #pragma omp simd for(int n=0; n<size; ++n) sinTable[n] = std::sin(2 * M_PI * n / size); // the table is now initialized }
#include <cmath> int main() { const int size = 256; double sinTable[size]; #pragma omp target teams distribute parallel for map(from:sinTable[0:256]) for(int n=0; n<size; ++n) sinTable[n] = std::sin(2 * M_PI * n / size); // the table is now initialized }
#include <complex> #include <cstdio> typedef std::complex<double> complex; int MandelbrotCalculate(complex c, int maxiter) { // iterates z = z + c until |z| >= 2 or maxiter is reached, // returns the number of iterations. complex z = c; int n=0; for(; n<maxiter; ++n) { if( std::abs(z) >= 2.0) break; z = z*z + c; } return n; } int main() { const int width = 78, height = 44, num_pixels = width*height; const complex center(-.7, 0), span(2.7, -(4/3.0)*2.7*height/width); const complex begin = center-span/2.0;//, end = center+span/2.0; const int maxiter = 100000; #pragma omp parallel for ordered schedule(dynamic) for(int pix=0; pix<num_pixels; ++pix) { const int x = pix%width, y = pix/width; complex c = begin + complex(x * span.real() / (width +1.0), y * span.imag() / (height+1.0)); int n = MandelbrotCalculate(c, maxiter); if(n == maxiter) n = 0; #pragma omp ordered { char c = ' '; if(n > 0) { static const char charset[] = ".,c8M@jawrpogOQEPGJ"; c = charset[n % (sizeof(charset)-1)]; } std::putchar(c); if(x+1 == width) std::puts("|"); } } }This program can be improved in many different ways, but it is left simple for the sake of an introductory example.
Only when the compiler interprets those #pragma lines, it becomes a parallel program. It really does calculate N values simultaneously where N is the number of threads. In GCC, libgomp determines that from the number of processors.
By C and C++ standards, if the compiler encounters a #pragma that it does not support, it will ignore it. So adding the OMP statements can be done safely[1] without breaking compatibility with legacy compilers.
There is also a runtime library that can be accessed through omp.h, but it is less often needed. If you need it, you can check the #define _OPENMP for conditional compilation in case of compilers that don't support OpenMP.
[1]: Within the usual parallel programming issues (concurrency, mutual exclusion) of course.
#pragma omp parallel { // Code inside this region runs in parallel. printf("Hello!\n"); }This code creates a team of threads, and each thread executes the same code. It prints the text "Hello!" followed by a newline, as many times as there are threads in the team created. For a dual-core system, it will output the text twice. (Note: It may also output something like "HeHlellolo", depending on system, because the printing happens in parallel.) At the }, the threads are joined back into one, as if in non-threaded program.
Internally, GCC implements this by creating a magic function and
moving the associated code into that function, so that all the
variables declared within that block become local variables of
that function (and thus, locals to each thread).
ICC, on the other hand, uses a mechanism resembling fork(),
and does not create a magic function. Both implementations are,
of course, valid, and semantically identical.
Variables shared from the context are handled transparently, sometimes by passing a reference and sometimes by using register variables which are flushed at the end of the parallel block (or whenever a flush is executed).
extern int parallelism_enabled; #pragma omp parallel for if(parallelism_enabled) for(int c=0; c<n; ++c) handle(c);In this case, if parallelism_enabled evaluates to a zero value, the number of threads in the team that processes the for loop will always be exactly one.
#pragma omp for for(int n=0; n<10; ++n) { printf(" %d", n); } printf(".\n");This loop will output each number from 0…9 once. However, it may do it in arbitrary order. It may output, for example:
0 5 6 7 1 8 2 3 4 9.Internally, the above loop becomes into code equivalent to this:
int this_thread = omp_get_thread_num(), num_threads = omp_get_num_threads(); int my_start = (this_thread ) * 10 / num_threads; int my_end = (this_thread+1) * 10 / num_threads; for(int n=my_start; n<my_end; ++n) printf(" %d", n);So each thread gets a different section of the loop, and they execute their own sections in parallel.
Note: #pragma omp for only delegates portions of the loop for different threads in the current team. A team is the group of threads executing the program. At program start, the team consists only of a single member: the master thread that runs the program.
To create a new team of threads, you need to specify the parallel keyword. It can be specified in the surrounding context:
#pragma omp parallel { #pragma omp for for(int n=0; n<10; ++n) printf(" %d", n); } printf(".\n");Equivalent shorthand is to specify it in the pragma itself, as #pragma omp parallel for:
#pragma omp parallel for for(int n=0; n<10; ++n) printf(" %d", n); printf(".\n");You can explicitly specify the number of threads to be created in the team, using the num_threads attribute:
#pragma omp parallel num_threads(3) { // This code will be executed by three threads. // Chunks of this loop will be divided amongst // the (three) threads of the current team. #pragma omp for for(int n=0; n<10; ++n) printf(" %d", n); }Note that OpenMP also works for C. However, in C, you need to set explicitly the loop variable as private, because C does not allow declaring it in the loop body:
int n; #pragma omp for private(n) for(n=0; n<10; ++n) printf(" %d", n); printf(".\n");See the "private and shared clauses" section for details.
In OpenMP 2.5, the iteration variable in for must be a signed integer variable type. In OpenMP 3.0, it may also be an unsigned integer variable type, a pointer type or a constant-time random access iterator type. In the latter case, std::distance() will be used to determine the number of loop iterations.
If your program never contains a parallel construct, there is never more than one thread; the master thread that starts the program and runs it, as in non-threading programs.
#pragma omp for schedule(static) for(int n=0; n<10; ++n) printf(" %d", n); printf(".\n");There are five scheduling types: static, dynamic, guided, auto, and (since OpenMP 4.0) runtime. In addition, there are three scheduling modifiers (since OpenMP 4.5): monotonic, nonmonotonic, and simd.
static is the default schedule as shown above. Upon entering the loop, each thread independently decides which chunk of the loop they will process.
There is also the dynamic schedule:
#pragma omp for schedule(dynamic) for(int n=0; n<10; ++n) printf(" %d", n); printf(".\n");In the dynamic schedule, there is no predictable order in which the loop items are assigned to different threads. Each thread asks the OpenMP runtime library for an iteration number, then handles it, then asks for next, and so on. This is most useful when used in conjunction with the ordered clause, or when the different iterations in the loop may take different time to execute.
The chunk size can also be specified to lessen the number of calls to the runtime library:
#pragma omp for schedule(dynamic, 3) for(int n=0; n<10; ++n) printf(" %d", n); printf(".\n");In this example, each thread asks for an iteration number, executes 3 iterations of the loop, then asks for another, and so on. The last chunk may be smaller than 3, though.
Internally, the loop above becomes into code equivalent to this (illustration only, do not write code like this):
int a,b; if(GOMP_loop_dynamic_start(0,10,1, 3, &a,&b)) { do { for(int n=a; n<b; ++n) printf(" %d", n); } while(GOMP_loop_dynamic_next(&a,&b)); }The guided schedule appears to have behavior of static with the shortcomings of static fixed with dynamic-like traits. It is difficult to explain — this example program maybe explains it better than words do. (Requires libSDL to compile.)
The "runtime" option means the runtime library chooses one of the scheduling options at runtime at the compiler library's discretion.
A scheduling modifier can be added to the clause, e.g.: #pragma omp for schedule(nonmonotonic:dynamic
The modifiers are:
However, it is possible to force that certain events within the loop happen in a predicted order, using the ordered clause.
#pragma omp for ordered schedule(dynamic) for(int n=0; n<100; ++n) { files[n].compress(); #pragma omp ordered send(files[n]); }This loop "compresses" 100 files with some files being compressed in parallel, but ensures that the files are "sent" in a strictly sequential order.
If the thread assigned to compress file 7 is done but the file 6 has not yet been sent, the thread will wait before sending, and before starting to compress another file. The ordered clause in the loop guarantees that there always exists one thread that is handling the lowest-numbered unhandled task.
Each file is compressed and sent exactly once, but the compression may happen in parallel.
There may only be one ordered block per an ordered loop, no less and no more. In addition, the enclosing for construct must contain the ordered clause.
OpenMP 4.5 added some modifiers and clauses to the ordered construct.
Example:
#pragma omp parallel for collapse(2) for(int y=0; y<25; ++y) for(int x=0; x<80; ++x) { tick(x,y); }
int sum=0; #pragma omp parallel for reduction(+:sum) for(int n=0; n<1000; ++n) sum += table[n];
#pragma omp sections { { Work1(); } #pragma omp section { Work2(); Work3(); } #pragma omp section { Work4(); } }This code indicates that any of the tasks Work1, Work2 + Work3 and Work4 may run in parallel, but that Work2 and Work3 must be run in sequence. Each work is done exactly once.
As usual, if the compiler ignores the pragmas, the result is still a correctly running program.
Internally, GCC implements this as a combination of the parallel for and a switch-case construct. Other compilers may implement it differently.
Note: #pragma omp sections only delegates the sections
for different threads in the current team. To create a team, you
need to specify the parallel keyword either in the surrounding
context or in the pragma, as #pragma omp parallel sections.
Example:
#pragma omp parallel sections // starts a new team { { Work1(); } #pragma omp section { Work2(); Work3(); } #pragma omp section { Work4(); } }or
#pragma omp parallel // starts a new team { //Work0(); // this function would be run by all threads. #pragma omp sections // divides the team into sections { // everything herein is run only once. { Work1(); } #pragma omp section { Work2(); Work3(); } #pragma omp section { Work4(); } } //Work5(); // this function would be run by all threads. }
There are two use cases for the simd construct.
Firstly, #pragma omp simd can be used to declare that a loop will be utilizing SIMD.
float a[8], b[8]; ... #pragma omp simd for(int n=0; n<8; ++n) a[n] += b[n];Secondly, #pragma omp declare simd can be used to indicate a function or procedure that is explicitly designed to take advantage of SIMD parallelism. The compiler may create multiple versions of the same function that use different parameter passing conventions for different CPU capabilities for SIMD processing.
#pragma omp declare simd aligned(a,b:16) void add_arrays(float *__restrict__ a, float *__restrict__ b) { #pragma omp simd aligned(a,b:16) for(int n=0; n<8; ++n) a[n] += b[n]; }Without the pragma, the function will use the default non-SIMD-aware ABI, even though the function itself may do calculation using SIMD.
Since compilers of today attempt to do SIMD regardless of OpenMP simd directives, the simd directive can be thought essentially as a directive to the compiler, saying: “Try harder”.
#pragma omp simd collapse(2) for(int i=0; i<4; ++i) for(int j=0; j<4; ++j) a[j*4+i] += b[i*4+j];
int sum=0; #pragma omp simd reduction(+:sum) for(int n=0; n<1000; ++n) sum += table[n];
The attribute can be used in both the function declaration, and in the individual SIMD statements.
#pragma omp declare simd aligned(a,b:16) void add_arrays(float *__restrict__ a, float *__restrict__ b) { #pragma omp simd aligned(a,b:16) for(int n=0; n<8; ++n) a[n] += b[n]; }
In the example code below, the compiler is informed that a[x] and b[y] are independent as long as the difference between x and y is smaller than 4. In reality, the clause controls the upper limit of concurrent loop iterations. It means that only 4 items can be processed concurrently at most. The actual concurrency may be smaller, and depends on the compiler implementation and hardware limits.
#pragma omp declare simd void add_arrays(float* a, float* b) { #pragma omp simd aligned(a,b:16) safelen(4) for(int n=0; n<8; ++n) a[n] += b[n]; }
Consider this example code:
#include <stdio.h> int b = 10; int main() { int array[8]; #pragma omp simd linear(b:2) for(int n=0; n<8; ++n) array[n] = b; for(int n=0; n<8; ++n) printf("%d\n", array[n]); }What does this code print? If we ignore the SIMD constructs, we can see it should print the sequence 10,10,10,10,10,10,10,10.
But, if we enable the OpenMP SIMD construct, the program should now print 10,12,14,16,18,20,22,24. This is because the linear clause tells the compiler, that the value of b inside each iteration of the loop should be a copy of the original value of b before the SIMD construct, plus the loop iteration number, times the linear scale, which is 2 in this case.
In essence, it should be equivalent to the following code:
int b_original = b; for(int n=0; n<8; ++n) array[n] = b_original + n*2;However, as of GCC version 6.1.0, the linear clause does not seem to be implemented correctly, at least according to my understanding of the specification, so I cannot do more experimentation.
The compiler may use this knowledge to optimize the code.
float sum(float* table) { float result=0; #pragma omp parallel for simd reduction(+:result) for(int n=0; n<1000; ++n) result += table[n]; return result; }
These examples are from the OpenMP 3.0 manual:
struct node { node *left, *right; }; extern void process(node* ); void traverse(node* p) { if (p->left) #pragma omp task // p is firstprivate by default traverse(p->left); if (p->right) #pragma omp task // p is firstprivate by default traverse(p->right); process(p); }In the next example, we force a postorder traversal of the tree by adding a taskwait directive. Now, we can safely assume that the left and right sons have been executed before we process the current node.
struct node { node *left, *right; }; extern void process(node* ); void postorder_traverse(node* p) { if (p->left) #pragma omp task // p is firstprivate by default postorder_traverse(p->left); if (p->right) #pragma omp task // p is firstprivate by default postorder_traverse(p->right); #pragma omp taskwait process(p); }The following example demonstrates how to use the task construct to process elements of a linked list in parallel. The pointer p is firstprivate by default on the task construct so it is not necessary to specify it in a firstprivate clause.
struct node { int data; node* next; }; extern void process(node* ); void increment_list_items(node* head) { #pragma omp parallel { #pragma omp single { for(node* p = head; p; p = p->next) { #pragma omp task process(p); // p is firstprivate by default } } } }
Example:
#pragma omp declare target int x; void murmur() { x+=5; } #pragma omp end declare targetThis creates one or more versions of "x" and "murmur". A set that exists on the host computer, and also a separate set that exists and can be run on a device.
These two functions and variables are separate, and may contain values separate from each others.
Variables declared in this manner can be accessed by the device code without separate map clauses.
OpenACC differences
In OpenACC, device-functions are declared by prefixing each function with #pragma acc routine. Its data model is more complicated and has no direct translation from/to OpenMP.
The target construct executes the construct on a device (and also has target data features).
These two constructs are identical in effect:
#pragma omp target // device()... map()... if()... { <<statements...>> }And:
#pragma omp target data // device()... map()... if()... { #pragma omp target { <<statements...>> } }IMPORTANT: The target construct does not add any parallelism to the program by itself. It only transfers the execution into another device, and executes the code there in a single thread.
To utilize parallelism on device, you have to engage a teams construct inside the target construct. Example:
#include <stdio.h> long long r = 1; int main(void) { r=10; #pragma omp target teams distribute parallel for reduction(+:r) map(tofrom:r) for(unsigned long long n=0; n<0x800000000ull; ++n) r += n; printf("r=%llX\n", r); return 0; }See the teams keyword below for details.
Example:
#include <stdlib.h> #include <stdio.h> int main(int argc, char** argv) { int r=0; #pragma omp target if(atoi(argv[1])) map(tofrom:r) r += 4; printf("r=%d\n", r); }
int device_number = ...; #pragma omp target device(device_number) { //... }You can acquire device numbers by using the <omp.h> library functions, such as omp_set_default_device, omp_get_default_device, omp_get_num_devices, and omp_is_initial_device.
If the device clause is not used, the code is executed on the default device. The default device number is controlled by the omp_set_default_device function, or the OMP_DEFAULT_DEVICE environment variable.
There are four different types of mappings:
Variables are initialized and assigned through bitwise copy, i.e. constructors / operators are not called.
The mapping items can be entire variables or array sections.
Array sections can only be specified in the map, and depend clauses. They are invalid in e.g private.
An example of a valid array subscript mapping:
void foo (int *p) { int i; #pragma omp parallel #pragma omp single #pragma omp target teams distribute parallel for map(p[0:24]) for (i = 0; i < 24; i++) p[i] = p[i] + 1; }
Examples:
This directive can be only used directly inside a target construct. The optional attribute num_teams can be used to specify the maximum number of teams created. The actual number of teams may be smaller than this number. The master thread of each team will execute the code inside that team.
The example code below may print the message multiple times.
#include <stdio.h> int main(void) { #pragma omp target teams { printf("test\n"); } return 0; }OpenACC differences
OpenACC calls teams and threads gangs and workers respectively. In OpenACC, a set of new teams is launched on the device with #pragma acc parallel, with the optional attribute num_gangs(n). This combines the behavior of #pragma omp target and #pragma omp teams.
For example, if there are 20 teams, the loop will be distributed across 20 master threads.
#include <stdio.h> int main(void) { int r=0; #pragma omp target teams distribute reduction(+:r) for(int n=0; n<10000; ++n) r += n; printf("r=%d\n", r); return 0; }OpenACC differences
In OpenACC this behavior is achieved by adding the word gang to existing worksharing constructs like #pragma acc parallel and #pragma acc kernels.
For example, if there are 20 teams, and each team consists of 256 threads, the loop will be distributed across 5120 threads.
#include <stdio.h> int main(void) { int r=0; #pragma omp target teams distribute parallel for reduction(+:r) for(int n=0; n<10000; ++n) r += n; printf("r=%d\n", r); return 0; }The number of threads created in each team is implementation defined, but can be explicitly defined with the num_threads attribute.
The simd clause can be added once again to the loop to add SIMD execution, if possible.
OpenACC differences
In OpenACC this behavior is achieved by adding the word worker to existing worksharing constructs like #pragma acc parallel and #pragma acc kernels. Additionally the word vector can be added to achieve SIMD parallelism as well.
Instead, I will explain the tools that OpenMP provides to handle mutual exclusion correctly.
#pragma omp atomic counter += value;The atomic keyword in OpenMP specifies that the denoted action happens atomically. It is commonly used to update counters and other simple variables that are accessed by multiple threads simultaneously.
See also reduction.
There are four different types of atomic expressions (since OpenMP 3.1):
#pragma omp atomic read var = x;Here the reading of x is guaranteed to happen atomically, but nothing is guaranteed about var. Note that var may not access the memory location designated for x.
#pragma omp atomic write x = expr;Here the writing of x is guaranteed to happen atomically, but nothing is guaranteed about expr. Note that expr may not access the memory location designated for x.
#pragma omp atomic update // The word "update" is optional // One of these: ++x; --x; x++; x--; x += expr; x -= expr; x *= expr; x /= expr; x &= expr; x = x+expr; x = x-expr; x = x*expr; x = x/expr; x = x&expr; x = expr+x; x = expr-x; x = expr*x; x = expr/x; x = expr&x; x |= expr; x ^= expr; x <<= expr; x >>= expr; x = x|expr; x = x^expr; x = x<<expr; x = x>>expr; x = expr|x; x = expr^x; x = expr<<x; x = expr>>x;Here the updating of x is guaranteed to happen atomically, but nothing is guaranteed about expr. Note that expr may not access the memory location designated for x.
#pragma omp atomic capture // One of these: var = x++; /* Or any other of the update expressions listed above */ { var = x; x++; /* Or any other of of the update expressions listed above */ } { x++; /* Or any other of of the update expressions listed above */; var = x; } { var = x; x = expr; }Note that neither var nor expr may not access the memory location designated for x.
The critical construct may optionally contain a global name that identifies the type of the critical construct. No two threads can execute a critical construct of the same name at the same time.
If the name is omitted, a default name is assumed.
#pragma omp critical(dataupdate) { datastructure.reorganize(); } ... #pragma omp critical(dataupdate) { datastructure.reorganize_again(); }In this example, only one of the critical sections named "dataupdate" may be executed at any given time, and only one thread may be executing it at that time. I.e. the functions "reorganize" and "reorganize_again" cannot be invoked at the same time, and two calls to the function cannot be active at the same time. (Except if other calls exist elsewhere, unprotected by the critical construct.)
Note: The critical section names are global to the entire program (regardless of module boundaries). So if you have a critical section by the same name in multiple modules, not two of them can be executed at the same time.
If you need something like a local mutex, see below.
The lock type has five manipulator functions:
Here is an example of a wrapper around std::set<> that provides per-instance mutual exclusion while still working even if the compiler does not support OpenMP.
You can maintain backward compability with non-OpenMP-supporting compilers by enclosing the library references in #ifdef _OPENMP…#endif blocks.
#ifdef _OPENMP # include <omp.h> #endif #include <set> class data { private: std::set<int> flags; #ifdef _OPENMP omp_lock_t lock; #endif public: data() : flags() { #ifdef _OPENMP omp_init_lock(&lock); #endif } ~data() { #ifdef _OPENMP omp_destroy_lock(&lock); #endif } bool set_get(int c) { #ifdef _OPENMP omp_set_lock(&lock); #endif bool found = flags.find(c) != flags.end(); if(!found) flags.insert(c); #ifdef _OPENMP omp_unset_lock(&lock); #endif return found; } };Of course, you would really rather wrap the lock into a custom container to avoid littering the code with #ifdefs and also for providing exception-safety:
#ifdef _OPENMP # include <omp.h> struct MutexType { MutexType() { omp_init_lock(&lock); } ~MutexType() { omp_destroy_lock(&lock); } void Lock() { omp_set_lock(&lock); } void Unlock() { omp_unset_lock(&lock); } MutexType(const MutexType& ) { omp_init_lock(&lock); } MutexType& operator= (const MutexType& ) { return *this; } public: omp_lock_t lock; }; #else /* A dummy mutex that doesn't actually exclude anything, * but as there is no parallelism either, no worries. */ struct MutexType { void Lock() {} void Unlock() {} }; #endif /* An exception-safe scoped lock-keeper. */ struct ScopedLock { explicit ScopedLock(MutexType& m) : mut(m), locked(true) { mut.Lock(); } ~ScopedLock() { Unlock(); } void Unlock() { if(!locked) return; locked=false; mut.Unlock(); } void LockAgain() { if(locked) return; mut.Lock(); locked=true; } private: MutexType& mut; bool locked; private: // prevent copying the scoped lock. void operator=(const ScopedLock&); ScopedLock(const ScopedLock&); };This way, the example above becomes a lot simpler, and also exception-safe:
#include <set> class data { private: std::set<int> flags; MutexType lock; public: bool set_get(int c) { ScopedLock lck(lock); // locks the mutex if(flags.find(c) != flags.end()) return true; // was found flags.insert(c); return false; // was not found } // automatically releases the lock when lck goes out of scope. };There is also a lock type that supports nesting, omp_nest_lock_t. I will not cover it here.
This example comes from the OpenMP specification.
/* presumption: int a = 0, b = 0; */ /* First thread */ /* Second thread */ b = 1; a = 1; #pragma omp flush(a,b) #pragma omp flush(a,b) if(a == 0) if(b == 0) { { /* Critical section */ /* Critical section */ } }In this example, it is enforced that at the time either of a or b is accessed, the other is also up-to-date, practically ensuring that not both of the two threads enter the critical section. (Note: It is still possible that neither of them can enter it.)
You need the flush directive when you have writes to and reads from the same data in different threads.
If the program appears to work correctly without the flush directive, it does not mean that the flush directive is not required. It just may be that your compiler is not utilizing all the freedoms the standard allows it to do. You need the flush directive whenever you access shared data in multiple threads: After a write, before a read.
However, I do not know these:
int a, b=0; #pragma omp parallel for private(a) shared(b) for(a=0; a<50; ++a) { #pragma omp atomic b += a; }This example explicitly specifies that a is private (each thread has their own copy of it) and that b is shared (each thread accesses the same variable).
Example:
#include <string> #include <iostream> int main() { std::string a = "x", b = "y"; int c = 3; #pragma omp parallel private(a,c) shared(b) num_threads(2) { a += "k"; c += 7; std::cout << "A becomes (" << a << "), b is (" << b << ")\n"; } }This will output the string "k", not "xk". At the entrance of the block, a becomes a new instance of std::string, that is initialized with the default constructor; it is not initialized with the copy constructor.
Internally, the program becomes like this:
int main() { std::string a = "x", b = "y"; int c = 3; OpenMP_thread_fork(2); { // Start new scope std::string a; // Note: It is a new local variable. int c; // This too. a += "k"; c += 7; std::cout << "A becomes (" << a << "), b is (" << b << ")\n"; } // End of scope for the local variables OpenMP_join(); }In the case of primitive (POD) datatypes (int, float, char* etc.), the private variable is uninitialized, just like any declared but not initialized local variable. It does not contain the value of the variable from the surrounding context. Therefore, the increment of c is moot here; the value of the variable is still undefined. (If you are using GCC version earlier than 4.4, you do not even get a warning about the use of uninitialized value in situations like this.)
If you actually need a copy of the original value, use the firstprivate clause instead.
#include <string> #include <iostream> int main() { std::string a = "x", b = "y"; int c = 3; #pragma omp parallel firstprivate(a,c) shared(b) num_threads(2) { a += "k"; c += 7; std::cout << "A becomes (" << a << "), b is (" << b << ")\n"; } }Now the output becomes "A becomes (xk), b is (y)".
Example:
#include <stdio.h> int main() { int done = 4, done2 = 5; #pragma omp parallel for lastprivate(done, done2) num_threads(2) schedule(static) for(int a=0; a<8; ++a) { if(a==2) done=done2=0; if(a==3) done=done2=1; } printf("%d,%d\n", done,done2); }This program outputs "4196224,-348582208", because internally, this program became like this:
#include <stdio.h> int main() { int done = 4, done2 = 5; OpenMP_thread_fork(2); { int this_thread = omp_get_thread_num(), num_threads = 2; int my_start = (this_thread ) * 8 / num_threads; int my_end = (this_thread+1) * 8 / num_threads; int priv_done, priv_done2; // not initialized, because firstprivate was not used for(int a=my_start; a<my_end; ++a) { if(a==2) priv_done=priv_done2=0; if(a==3) priv_done=priv_done2=1; } if(my_end == 8) { // assign the values back, because this was the last iteration done = priv_done; done2 = priv_done2; } } OpenMP_join(); }As one can observe, the values of priv_done and priv_done2 are not assigned even once during the course of the loop that iterates through 4...7. As such, the values that are assigned back are completely bogus.
Therefore, lastprivate cannot be used to e.g. fetch the value of a flag assigned randomly during a loop. Use reduction for that, instead.
Where this behavior can be utilized though, is in situations like this (from OpenMP manual):
void loop() { int i; #pragma omp for lastprivate(i) for(i=0; i<get_loop_count(); ++i) // note: get_loop_count() must be a pure function. { ... } printf("%d\n", i); // this shows the number of loop iterations done. }
int a, b=0; // This code won't compile: It requires explicitly // specifying whether a is shared or private. #pragma omp parallel default(none) shared(b) { b += a; }The default clause can also be used to set that all variables are shared by default (default(shared)).
Note: Because different compilers have different ideas about which variables are implicitly private or shared, and for which it is an error to explicitly state the private/shared status, it is recommended to use the default(none) setting only during development, and drop it in production/distribution code.
This example calculates factorial using threads:
int factorial(int number) { int fac = 1; #pragma omp parallel for reduction(*:fac) for(int n=2; n<=number; ++n) fac *= n; return fac; }
The syntax of the clause is:
reduction(operator:list)where list is the list of variables where the operator will be applied to, and operator is one of these:
| Operator | Initialization value |
|---|---|
| +, -, |, ^, || | 0 |
| *, && | 1 |
| & | ~0 |
| min | largest representable number |
| max | smallest representable number |
To write the factorial function (shown above) without reduction, it probably would look like this:
int factorial(int number) { int fac = 1; #pragma omp parallel for for(int n=2; n<=number; ++n) { #pragma omp atomic fac *= n; } return fac; }However, this code would be less optimal than the one with reduction: it misses the opportunity to use a local (possible register) variable for the cumulation, and needlessly places load/synchronization demands on the shared memory variable. In fact, due to the bottleneck of that atomic variable (only one thread may access it simultaneously), it would completely nullify any gains of parallelism in that loop.
The version with reduction is equivalent to this code (illustration only):
int factorial(int number) { int fac = 1; #pragma omp parallel { int omp_priv = 1; /* This value comes from the table shown above */ #pragma omp for nowait for(int n=2; n<=number; ++n) omp_priv *= n; #pragma omp atomic fac *= omp_priv; } return fac; }Note how it moves the atomic operation out from the loop.
The restrictions in reduction and atomic are very similar: both can only be done on POD types; neither allows overloaded operators, and both have the same set of supported operators.
As an example of how the reduction clause can be used to produce semantically different code when OpenMP is enabled and when it is disabled, this example prints the number of threads that executed the parallel block:
int a = 0; #pragma omp parallel reduction (+:a) { a = 1; // Assigns a value to the private copy. // Each thread increments the value by 1. } printf("%d\n", a);If you preinitialized "a" to 4, it would print a number >= 5 if OpenMP was enabled, and 1 if OpenMP was disabled.
The syntax of the declaration is one of these two:
#pragma omp declare reduction(name:type:expression)
#pragma omp declare reduction(name:type:expression) initializer(expression)
An example use case is when you are running a data compressor with different parameters, and you want to find the set of parameters that results in best compression. Below is an example of such code:
#include <cstdio> int compress(int param1, int param2) { return (param1+13)^param2; // Placeholder for a compression algorithm } int main(int argc, char** argv) { struct BestInfo { unsigned size, param1, param2; }; #pragma omp declare reduction(isbetter:BestInfo: \ omp_in.size<omp_out.size ? omp_out=omp_in : omp_out \ ) initializer(omp_priv = BestInfo{~0u,~0u,~0u}) BestInfo result{~0u,~0u,~0u}; #pragma omp parallel for collapse(2) reduction(isbetter:result) for(unsigned p1=0; p1<10; ++p1) for(unsigned p2=0; p2<10; ++p2) { unsigned size = compress(p1,p2); if(size < result.size) result = BestInfo{size,p1,p2}; } std::printf("Best compression (%u bytes) with params %u,%u\n", result.size, result.param1, result.param2); }
For more information, read the OpenMP specification.
#pragma omp parallel { /* All threads execute this. */ SomeCode(); #pragma omp barrier /* All threads execute this, but not before * all threads have finished executing SomeCode(). */ SomeMoreCode(); }Note: There is an implicit barrier at the end of each parallel block, and at the end of each sections, for and single statement, unless the nowait directive is used.
Example:
#pragma omp parallel { #pragma omp for for(int n=0; n<10; ++n) Work(); // This line is not reached before the for-loop is completely finished SomeMoreCode(); } // This line is reached only after all threads from // the previous parallel block are finished. CodeContinues(); #pragma omp parallel { #pragma omp for nowait for(int n=0; n<10; ++n) Work(); // This line may be reached while some threads are still executing the for-loop. SomeMoreCode(); } // This line is reached only after all threads from // the previous parallel block are finished. CodeContinues();The nowait directive can only be attached to sections, for and single. It cannot be attached to the within-loop ordered clause, for example.
#pragma omp parallel { Work1(); #pragma omp single { Work2(); } Work3(); }In a 2-cpu system, this will run Work1() twice, Work2() once and Work3() twice. There is an implied barrier at the end of the single construct, but not at the beginning of it.
Note: Do not assume that the single block is executed by whichever thread gets there first. According to the standard, the decision of which thread executes the block is implementation-defined, and therefore making assumptions on it is non-conforming.
The master construct is similar, except that the statement/block is run by the master thread, and there is no implied barrier; other threads skip the construct without waiting.
#pragma omp parallel { Work1(); // This... #pragma omp master { Work2(); } // ...is practically identical to this: if(omp_get_thread_num() == 0) { Work2(); } Work3(); }Unless you use the threadprivate clause, the only important difference between single nowait and master is that if you have multiple master blocks in a parallel section, you are guaranteed that they are executed by the same thread every time, and hence, the values of private (thread-local) variables are the same.
/* Returns any position from the haystack where the needle can * be found, or NULL if no such position exists. It is not guaranteed * to find the first matching position; it only guarantees to find * _a_ matching position if one exists. */ const char* FindAnyNeedle(const char* haystack, size_t size, char needle) { for(size_t p = 0; p < size; ++p) if(haystack[p] == needle) { /* This breaks out of the loop. */ return haystack+p; } return NULL; }Our first attempt might be to simply tack a #pragma parallel for before the for loop, but that doesn't work: OpenMP requires that a loop construct processes each iteration. Breaking out of the loop (using return, goto, break, throw or other means) is not allowed.
To solve this problem, OpenMP 4.0 added a mechanism called cancellation points, and a cancel construct. Cancellation points are implicitly inserted at the following positions:
It can be used to solve finder problems where N threads search for a solution and once a solution is found by any thread, all threads end their search.
Because there is a performance overhead in checking for cancellations, it is only enabled if the library-internal global variable OMP_CANCELLATION is set. The value of this variable can be checked with the omp_get_cancellation() function, but there is no way modify it from inside the program. It can only be set from the environment when the program is launched.
In this example program, once a thread finds the "needle", it signals cancellation for all threads of the current team processing the innermost for loop. Threads check the cancellation only at every loop iteration. It also checks whether OMP_CANCELLATION is set, and if not, sets it and reruns the program.
#include <stdio.h> // For printf #include <string.h> // For strlen #include <stdlib.h> // For putenv #include <unistd.h> // For execv #include <omp.h> // For omp_get_cancellation, omp_get_thread_num() static const char* FindAnyNeedle(const char* haystack, size_t size, char needle) { const char* result = haystack+size; #pragma omp parallel { unsigned num_iterations=0; #pragma omp for for(size_t p = 0; p < size; ++p) { ++num_iterations; if(haystack[p] == needle) { #pragma omp atomic write result = haystack+p; // Signal cancellation. #pragma omp cancel for } // Check for cancellations signalled by other threads: #pragma omp cancellation point for } // All threads reach here eventually; sooner if the cancellation was signalled. printf("Thread %u: %u iterations completed\n", omp_get_thread_num(), num_iterations); } return result; } int main(int argc, char** argv) { if(!omp_get_cancellation()) { printf("Cancellations were not enabled, enabling cancellation and rerunning program\n"); putenv("OMP_CANCELLATION=true"); execv(argv[0], argv); } printf("%s\n%*s\n", argv[1], FindAnyNeedle(argv[1],strlen(argv[1]),argv[2][0])-argv[1]+1, "^"); }Example output:
./a.out "OpenMP cancellations can only be performed synchronously at cancellation points." "l"
Cancellations were not enabled, enabling cancellation and rerunning program
Thread 0: 10 iterations completed
Thread 1: 3 iterations completed
Thread 7: 10 iterations completed
Thread 3: 10 iterations completed
Thread 4: 10 iterations completed
Thread 2: 8 iterations completed
Thread 5: 5 iterations completed
Thread 6: 6 iterations completed
OpenMP cancellations can only be performed synchronously at cancellation points.
^
The keyword in the end of the #pragma omp cancellation point construct is
the name of the most closely nested OpenMP construct that you want to cancel.
In the example code above, it is the for construct, and this is why
the line says #pragma omp cancellation point for.
OpenMP cancellations can only be performed synchronously at cancellation points. GNU pthreads also permits asynchronous cancellations. This is rarely used, and requires special setup, because there are several resource leak risks involved in it. An example of such code can be found here: http://bisqwit.iki.fi/jutut/kuvat/openmphowto/pthread_cancel_demo.cpp
#pragma omp parallel for for(int y=0; y<25; ++y) { #pragma omp parallel for for(int x=0; x<80; ++x) { tick(x,y); } }The beginner expects there to be N tick() calls active at the same time (where N = number of processors). Although that is true, the inner loop is not actually parallelised. Only the outer loop is. The inner loop runs in a pure sequence, as if the whole inner #pragma was omitted.
At the entrance of the inner parallel construct, the OpenMP runtime library (libgomp in case of GCC) detects that there already exists a team, and instead of a new team of N threads, it will create a team consisting of only the calling thread.
Rewriting the code like this won't work:
#pragma omp parallel for for(int y=0; y<25; ++y) { #pragma omp for // ERROR, nesting like this is not allowed. for(int x=0; x<80; ++x) { tick(x,y); } }This code is erroneous and will cause the program to malfunction. See the restrictions chapter below for details.
Example:
#pragma omp parallel for collapse(2) for(int y=0; y<25; ++y) for(int x=0; x<80; ++x) { tick(x,y); }The number specified in the collapse clauses is the number of nested loops that are subject to the work-sharing semantics of the OpenMP for construct.
This problem only affects GCC; ICC is not affected.
If your program intends to become a background process using daemonize() or other similar means, you must not use the OpenMP features before the fork. After OpenMP features are utilized, a fork is only allowed if the child process does not use OpenMP features, or it does so as a completely new process (such as after exec()).
This is an example of an erroneous program:
#include <stdio.h> #include <sys/wait.h> #include <unistd.h> void a() { #pragma omp parallel num_threads(2) { puts("para_a"); // output twice } puts("a ended"); // output once } void b() { #pragma omp parallel num_threads(2) { puts("para_b"); } puts("b ended"); } int main() { a(); // Invokes OpenMP features (parent process) int p = fork(); if(!p) { b(); // ERROR: Uses OpenMP again, but in child process _exit(0); } wait(NULL); return 0; }When run, this program hangs, never reaching the line that outputs "b ended".
There is currently no workaround; the libgomp API does not specify functions that can be used to prepare for a call to fork().
GCC