chap7_slidesforparallelcomputingananthgrama

Programming Shared Address Space
Platforms
Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar
To accompany the text “Introduction to Parallel Computing”,
Addison Wesley, 2003.

Topic Overview
• Thread Basics
• The POSIX Thread API
• Synchronization Primitives in Pthreads
• Controlling Thread and Synchronization Attributes
• Composite Synchronization Constructs
• OpenMP: a Standard for Directive Based Parallel Programming

Overview of Programming Models
• Programming models provide support for expressing concurrency
and synchronization.
• Process based models assume that all data associated with a
process is private, by default, unless otherwise specified.
• Lightweight processes and threads assume that all memory is
global.
• Directive based programming models extend the threaded
model by facilitating creation and synchronization of threads.

Overview of Programming Models
• A thread is a single stream of control in the flow of a program.
A program like:
for (row = 0; row < n; row++)
for (column = 0; column < n; column++)
c[row][column] =
dot_product(get_row(a, row),
get_col(b, col));
can be transformed to:
for (row = 0; row < n; row++)
for (column = 0; column < n; column++)
c[row][column] =
create_thread(
dot_product(get_row(a, row),
get_col(b, col)));
In this case, one may think of the thread as an instance of a
function that returns before the function has finished executing.

Thread Basics
• All memory in the logical machine model of a thread is globally
accessible to every thread.
• The stack corresponding to the function call is generally
treated as being local to the thread for liveness reasons.
• This implies a logical machine model with both global memory
(default) and local memory (stacks).
• It is important to note that such a flat model may result in
very poor performance since memory is physically distributed
in typical machines.

Thread Basics
P
P
P
Shared
Address
Space
P
P
P
M
M
M
Shared
Address
Space
The logical machine model of a thread-based programming
paradigm.

Thread Basics
• Threads provide software portability.
• Inherent support for latency hiding.
• Scheduling and load balancing.
• Ease of programming and widespread use.

The POSIX Thread API
• Commonly referred to as Pthreads, POSIX has emerged as the
standard threads API, supported by most vendors.
• The concepts discussed here are largely independent of the
API and can be used for programming with other thread APIs
(NT threads, Solaris threads, Java threads, etc.) as well.

Thread Basics: Creation and Termination
• Pthreads provides two basic functions for specifying concurrency
in a program:
#include <pthread.h>
int pthread_create (
pthread_t *thread_handle,
const pthread_attr_t *attribute,
void * (*thread_function)(void *),
void *arg);
int pthread_join (
pthread_t thread,
void **ptr);
• The function pthread_create invokes function thread_function
as a thread.

Thread Basics: Creation and Termination (Example)
#include <pthread.h>
#include <stdlib.h>
#define MAX_THREADS 512
void *compute_pi (void *);
....
main() {
...
pthread_t p_threads[MAX_THREADS];
pthread_attr_t attr;
pthread_attr_init (&attr);
for (i=0; i< num_threads; i++) {
hits[i] = i;
pthread_create(&p_threads[i], &attr, compute_pi,
(void *) &hits[i]);
}
for (i=0; i< num_threads; i++) {
pthread_join(p_threads[i], NULL);
total_hits += hits[i];
}
...
}

Thread Basics: Creation and Termination (Example)
void *compute_pi (void *s) {
int seed, i, *hit_pointer;
double rand_no_x, rand_no_y;
int local_hits;
hit_pointer = (int *) s;
seed = *hit_pointer;
local_hits = 0;
for (i = 0; i < sample_points_per_thread; i++) {
rand_no_x =(double)(rand_r(&seed))/(double)((2<<14)-1);
rand_no_y =(double)(rand_r(&seed))/(double)((2<<14)-1);
if (((rand_no_x - 0.5) * (rand_no_x - 0.5) +
(rand_no_y - 0.5) * (rand_no_y - 0.5)) < 0.25)
local_hits ++;
seed *= i;
}
*hit_pointer = local_hits;
pthread_exit(0);
}

Programming and Performance Notes
• Note the use of the function rand r (instead of superior
random number generators such as drand48).
• Executing this on a 4-processor SGI Origin, we observe a 3.91
fold speedup at 32 threads. This corresponds to a parallel
efficiency of 0.98!
• We can also modify the program slightly to observe the effect
of false-sharing.
• The program can also be used to assess the secondary cache
line size.

Programming and Performance Notes
0
1
2
3
4
5
6
0 1 2 3 4 5 6 7 8 9
"optimal"
"local"
"spaced_1"
"spaced_16"
"spaced_32"
logarithm of number of threads
Time
Execution time of the compute pi program.

Synchronization Primitives in Pthreads
• When multiple threads attempt to manipulate the same data
item, the results can often be incoherent if proper care is not
taken to synchronize them.
• Consider:
/* each thread tries to update variable best_cost as follows */
if (my_cost < best_cost)
best_cost = my_cost;
• Assume that there are two threads, the initial value of
best cost is 100, and the values of my cost are 50 and 75 at
threads t1 and t2.
• Depending on the schedule of the threads, the value of
best cost could be 50 or 75!
• The value 75 does not correspond to any serialization of the
threads.

Mutual Exclusion
• The code in the previous example corresponds to a critical
segment; i.e., a segment that must be executed by only one
thread at any time.
• Critical segments in Pthreads are implemented using mutex
locks.
• Mutex-locks have two states: locked and unlocked. At any
point of time, only one thread can lock a mutex lock. A lock is
an atomic operation.
• A thread entering a critical segment first tries to get a lock. It
goes ahead when the lock is granted.

Mutual Exclusion
The Pthreads API provides the following functions for handling
mutex-locks:
int pthread_mutex_lock (
pthread_mutex_t *mutex_lock);
int pthread_mutex_unlock (
int pthread_mutex_init (
pthread_mutex_t *mutex_lock,
const pthread_mutexattr_t *lock_attr);

Mutual Exclusion
We can now write our previously incorrect code segment as:
pthread_mutex_t minimum_value_lock;
...
main() {
....
pthread_mutex_init(&minimum_value_lock, NULL);
....
}
void *find_min(void *list_ptr) {
....
pthread_mutex_lock(&minimum_value_lock);
if (my_min < minimum_value)
minimum_value = my_min;
/* and unlock the mutex */
pthread_mutex_unlock(&minimum_value_lock);
}

Producer-Consumer Using Locks
The producer-consumer scenario imposes the following
constraints:
• The producer thread must not overwrite the shared buffer when
the previous task has not been picked up by a consumer
thread.
• The consumer threads must not pick up tasks until there is
something present in the shared data structure.
• Individual consumer threads should pick up tasks one at a time.

pthread_mutex_t task_queue_lock;
int task_available;
...
main() {
....
task_available = 0;
pthread_mutex_init(&task_queue_lock, NULL);
....
}
void *producer(void *producer_thread_data) {
....
while (!done()) {
inserted = 0;
create_task(&my_task);
while (inserted == 0) {
pthread_mutex_lock(&task_queue_lock);
if (task_available == 0) {
insert_into_queue(my_task);
task_available = 1;
inserted = 1;
}
pthread_mutex_unlock(&task_queue_lock);
}
}
}

void *consumer(void *consumer_thread_data) {
int extracted;
struct task my_task;
/* local data structure declarations */
while (!done()) {
extracted = 0;
while (extracted == 0) {
pthread_mutex_lock(&task_queue_lock);
if (task_available == 1) {
extract_from_queue(&my_task);
task_available = 0;
extracted = 1;
}
pthread_mutex_unlock(&task_queue_lock);
}
process_task(my_task);
}
}

Types of Mutexes
• Pthreads supports three types of mutexes – normal, recursive,
and error-check.
• A normal mutex deadlocks if a thread that already has a lock
tries a second lock on it.
• A recursive mutex allows a single thread to lock a mutex as
many times as it wants. It simply increments a count on the
number of locks. A lock is relinquished by a thread when the
count becomes zero.
• An error check mutex reports an error when a thread with a
lock tries to lock it again (as opposed to deadlocking in the first
case, or granting the lock, as in the second case).
• The type of the mutex can be set in the attributes object before
it is passed at time of initialization.

Overheads of Locking
• Locks represent serialization points since critical sections must
be executed by threads one after the other.
• Encapsulating large segments of the program within locks can
lead to significant performance degradation.
• It is often possible to reduce the idling overhead associated
with locks using an alternate function, pthread mutex trylock.
int pthread_mutex_trylock (
• pthread mutex trylock is typically much faster than pthread mutex
on typical systems since it does not have to deal with queues
associated with locks for multiple threads waiting on the lock.

Alleviating Locking Overhead (Example)
/* Finding k matches in a list */
void *find_entries(void *start_pointer) {
/* This is the thread function */
struct database_record *next_record;
int count;
current_pointer = start_pointer;
do {
next_record = find_next_entry(current_pointer);
count = output_record(next_record);
} while (count < requested_number_of_records);
}
int output_record(struct database_record *record_ptr) {
int count;
pthread_mutex_lock(&output_count_lock);
output_count ++;
count = output_count;
pthread_mutex_unlock(&output_count_lock);
if (count <= requested_number_of_records)
print_record(record_ptr);
return (count);
}

Alleviating Locking Overhead (Example)
/* rewritten output_record function */
int output_record(struct database_record *record_ptr) {
int count;
int lock_status;
lock_status = pthread_mutex_trylock(&output_count_lock);
if (lock_status == EBUSY) {
insert_into_local_list(record_ptr);
return(0);
}
else {
count = output_count;
output_count += number_on_local_list + 1;
pthread_mutex_unlock(&output_count_lock);
print_records(record_ptr, local_list,
requested_number_of_records - count);
return(count + number_on_local_list + 1);
}
}

Condition Variables for Synchronization
• A condition variable allows a thread to block itself until
specified data reaches a predefined state.
• A condition variable is associated with this predicate. When
the predicate becomes true, the condition variable is used to
signal one or more threads waiting on the condition.
• A single condition variable may be associated with more than
one predicate.
• A condition variable always has a mutex associated with it. A
thread locks this mutex and tests the predicate defined on the
shared variable.
• If the predicate is not true, the thread waits on the condition
variable associated with the predicate using the function
pthread cond wait.

Condition Variables for Synchronization
Pthreads provides the following functions for condition variables:
int pthread_cond_wait(pthread_cond_t *cond,
pthread_mutex_t *mutex);
int pthread_cond_signal(pthread_cond_t *cond);
int pthread_cond_broadcast(pthread_cond_t *cond);
int pthread_cond_init(pthread_cond_t *cond,
const pthread_condattr_t *attr);
int pthread_cond_destroy(pthread_cond_t *cond);

Producer-Consumer Using Condition Variables
pthread_cond_t cond_queue_empty, cond_queue_full;
pthread_mutex_t task_queue_cond_lock;
int task_available;
/* other data structures here */
main() {
/* declarations and initializations */
task_available = 0;
pthread_init();
pthread_cond_init(&cond_queue_empty, NULL);
pthread_cond_init(&cond_queue_full, NULL);
pthread_mutex_init(&task_queue_cond_lock, NULL);
/* create and join producer and consumer threads */
}

void *producer(void *producer_thread_data) {
int inserted;
while (!done()) {
create_task();
pthread_mutex_lock(&task_queue_cond_lock);
while (task_available == 1)
pthread_cond_wait(&cond_queue_empty,
&task_queue_cond_lock);
insert_into_queue();
task_available = 1;
pthread_cond_signal(&cond_queue_full);
pthread_mutex_unlock(&task_queue_cond_lock);
}
}

void *consumer(void *consumer_thread_data) {
while (!done()) {
pthread_mutex_lock(&task_queue_cond_lock);
while (task_available == 0)
pthread_cond_wait(&cond_queue_full,
&task_queue_cond_lock);
my_task = extract_from_queue();
task_available = 0;
pthread_cond_signal(&cond_queue_empty);
pthread_mutex_unlock(&task_queue_cond_lock);
process_task(my_task);
}
}

Controlling Thread and Synchronization Attributes
• The Pthreads API allows a programmer to change the default
attributes of entities using attributes objects.
• An attributes object is a data-structure that describes entity
(thread, mutex, condition variable) properties.
• Once these properties are set, the attributes object can be
passed to the method initializing the entity.
• Enhances modularity, readability, and ease of modification.

Attributes Objects for Threads
• Use pthread attr init to create an attributes object.
• Individual properties associated with the attributes object can
be changed using the following functions:
pthread attr setdetachstate,
pthread attr setguardsize np,
pthread attr setstacksize,
pthread attr setinheritsched,
pthread attr setschedpolicy, and
pthread attr setschedparam.

Attributes Objects for Mutexes
• Initialize the attrributes object using function:
pthread mutexattr init.
• The function pthread mutexattr settype np can be used
for setting the type of mutex specified by the mutex attributes
object.
pthread_mutexattr_settype_np (
pthread_mutexattr_t *attr,
int type);
Here, type specifies the type of the mutex and can take one
of:
– PTHREAD MUTEX NORMAL NP
– PTHREAD MUTEX RECURSIVE NP
– PTHREAD MUTEX ERRORCHECK NP

Composite Synchronization Constructs
• By design, Pthreads provide support for a basic set of
operations.
• Higher level constructs can be built using basic synchronization
constructs.
• We discuss two such constructs – read-write locks and barriers.

Read-Write Locks
• In many applications, a data structure is read frequently but
written infrequently. For such applications, we should use read-
write locks.
• A read lock is granted when there are other threads that may
already have read locks.
• If there is a write lock on the data (or if there are queued write
locks), the thread performs a condition wait.
• If there are multiple threads requesting a write lock, they must
perform a condition wait.
• With this description, we can design functions for read locks
mylib rwlock rlock, write locks mylib rwlock wlock, and
unlocking mylib rwlock unlock.

Read-Write Locks
• The lock data type mylib rwlock t holds the following:
– a count of the number of readers,
– the writer (a 0/1 integer specifying whether a writer is
present),
– a condition variable readers proceed that is signaled when
readers can proceed,
– a condition variable writer proceed that is signaled when
one of the writers can proceed,
– a count pending writers of pending writers, and
– a mutex read write lock associated with the shared data
structure.

Read-Write Locks
typedef struct {
int readers;
int writer;
pthread_cond_t readers_proceed;
pthread_cond_t writer_proceed;
int pending_writers;
pthread_mutex_t read_write_lock;
} mylib_rwlock_t;
void mylib_rwlock_init (mylib_rwlock_t *l) {
l -> readers = l -> writer = l -> pending_writers = 0;
pthread_mutex_init(&(l -> read_write_lock), NULL);
pthread_cond_init(&(l -> readers_proceed), NULL);
pthread_cond_init(&(l -> writer_proceed), NULL);
}

Read-Write Locks
void mylib_rwlock_rlock(mylib_rwlock_t *l) {
/* if there is a write lock or pending writers, perform condition
wait.. else increment count of readers and grant read lock */
pthread_mutex_lock(&(l -> read_write_lock));
while ((l -> pending_writers > 0) || (l -> writer > 0))
pthread_cond_wait(&(l -> readers_proceed),
&(l -> read_write_lock));
l -> readers ++;
pthread_mutex_unlock(&(l -> read_write_lock));
}

Read-Write Locks
void mylib_rwlock_wlock(mylib_rwlock_t *l) {
/* if there are readers or writers, increment pending writers
count and wait. On being woken, decrement pending writers
count and increment writer count */
while ((l -> writer > 0) || (l -> readers > 0)) {
l -> pending_writers ++;
pthread_cond_wait(&(l -> writer_proceed),
&(l -> read_write_lock));
}
l -> pending_writers --;
l -> writer ++
}

Read-Write Locks
void mylib_rwlock_unlock(mylib_rwlock_t *l) {
/* if there is a write lock then unlock, else if there are
read locks, decrement count of read locks. If the count
is 0 and there is a pending writer, let it through, else
if there are pending readers, let them all go through */
if (l -> writer > 0)
l -> writer = 0;
else if (l -> readers > 0)
l -> readers --;
if ((l -> readers == 0) && (l -> pending_writers > 0))
pthread_cond_signal(&(l -> writer_proceed));
else if (l -> readers > 0)
pthread_cond_broadcast(&(l -> readers_proceed));
}

Barriers
• As in MPI, a barrier holds a thread until all threads participating
in the barrier have reached it.
• Barriers can be implemented using a counter, a mutex and a
condition variable.
• A single integer is used to keep track of the number of threads
that have reached the barrier.
• If the count is less than the total number of threads, the threads
execute a condition wait.
• The last thread entering (and setting the count to the number
of threads) wakes up all the threads using a condition
broadcast.

Barriers
typedef struct {
pthread_mutex_t count_lock;
pthread_cond_t ok_to_proceed;
int count;
} mylib_barrier_t;
void mylib_init_barrier(mylib_barrier_t *b) {
b -> count = 0;
pthread_mutex_init(&(b -> count_lock), NULL);
pthread_cond_init(&(b -> ok_to_proceed), NULL);
}

Barriers
void mylib_barrier (mylib_barrier_t *b, int num_threads) {
pthread_mutex_lock(&(b -> count_lock));
b -> count ++;
if (b -> count == num_threads) {
b -> count = 0;
pthread_cond_broadcast(&(b -> ok_to_proceed));
}
else
while (pthread_cond_wait(&(b -> ok_to_proceed),
&(b -> count_lock)) != 0);
pthread_mutex_unlock(&(b -> count_lock));
}

Barriers
• The barrier described above is called a linear barrier.
• The trivial lower bound on execution time of this function is
therefore O(n) for n threads.
• This implementation of a barrier can be speeded up using
multiple barrier variables organized in a tree.
• We use n/2 condition variable-mutex pairs for implementing a
barrier for n threads.
• At the lowest level, threads are paired up and each pair of
threads shares a single condition variable-mutex pair.
• Once both threads arrive, one of the two moves on, the other
one waits.
• This process repeats up the tree.
• This is also called a log barrier and its runtime grows as O(log p).

Barrier
0
5
10
15
20
25
30
35
40
45
50
0 20 40 60 80 100 120 140
Time
(seconds)
Number of threads
Log Barrier (1000, 32 procs)
Linear Barrier (1000, 32 procs)
Execution time of 1000 sequential and logarithmic barriers as a
function of number of threads on a 32 processor SGI Origin 2000.

Tips for Designing Asynchronous Programs
• Never rely on scheduling assumptions when exchanging data.
• Never rely on liveness of data resulting from assumptions on
scheduling.
• Do not rely on scheduling as a means of synchronization.
• Where possible, define and use group synchronizations and
data replication.

OpenMP: a Standard for Directive Based Parallel
Programming
• OpenMP is a directive-based API that can be used with
FORTRAN, C, and C++ for programming shared address space
machines.
• OpenMP directives provide support for concurrency, synchronization,
and data handling while obviating the need for explicitly
setting up mutexes, condition variables, data scope, and
initialization.

OpenMP Programming Model
• OpenMP directives in C and C++ are based on the #pragma
compiler directives.
• A directive consists of a directive name followed by clauses.
#pragma omp directive [clause list]
• OpenMP programs execute serially until they encounter the
parallel directive, which creates a group of threads.
#pragma omp parallel [clause list]
/* structured block */
• The main thread that encounters the parallel directive
becomes the master of this group of threads and is assigned
the thread id 0 within the group.

The clause list is used to specify conditional parallelization,
number of threads, and data handling.
• Conditional Parallelization: The clause if (scalar expression)
determines whether the parallel construct results in creation of
threads.
• Degree of Concurrency: The clause num threads (integer
expression) specifies the number of threads that are
created.
• Data Handling: The clause private (variable list)
indicates variables local to each thread. The clause
firstprivate (variable list) is similar to the private,
except values of variables are initialized to corresponding
values before the parallel directive. The clause shared
(variable list) indicates that variables are shared across
all the threads.

pthread_create (......., internal_thread_fn_name, ...);
// serial segment
for (i = 0; i < 8; i++)
for (i = 0; i < 8; i++)
pthread_join (.......);
// rest of serial segment
}
void *internal_thread_fn_name (void *packaged_argument) [
int a;
// parallel segment
}
main() {
int a, b;
Code
inserted by
the OpenMP
compiler
Sample OpenMP program
Corresponding Pthreads translation
{
// parallel segment
}
// serial segment
#pragma omp parallel num_threads (8) private (a) shared (b)
// rest of serial segment
}
main() {
int a, b;
A sample OpenMP program along with its Pthreads translation
that might be performed by an OpenMP compiler.

#pragma omp parallel if (is_parallel == 1) num_threads(8)
private (a) shared (b) firstprivate(c)
{
}
• If the value of the variable is parallel equals one, eight
threads are created.
• Each of these threads gets private copies of variables a and c,
and shares a single value of variable b.
• The value of each copy of c is initialized to the value of c before
the parallel directive.
• The default state of a variable is specified by the clause
default (shared) or default (none).

Reduction Clause in OpenMP
• The reduction clause specifies how multiple local copies of a
variable at different threads are combined into a single copy
at the master when threads exit.
• The usage of the reduction clause is reduction (operator:
variable list).
• The variables in the list are implicitly specified as being private
to threads.
• The operator can be one of +, *, -, &, |, ˆ, &&, and ||.
#pragma omp parallel reduction(+: sum) num_threads(8)
{
/* compute local sums here */
}
/* sum here contains sum of all local instances of sums */

OpenMP Programming: Example
/* ******************************************************
An OpenMP version of a threaded program to compute PI.
****************************************************** */
#pragma omp parallel default(private) shared (npoints)
reduction(+: sum) num_threads(8)
{
num_threads = omp_get_num_threads();
sample_points_per_thread = npoints / num_threads;
sum = 0;
for (i = 0; i < sample_points_per_thread; i++) {
(rand_no_y - 0.5) * (rand_no_y - 0.5)) < 0.25)
sum ++;
}
}

Specifying Concurrent Tasks in OpenMP
• The parallel directive can be used in conjunction with other
directives to specify concurrency across iterations and tasks.
• OpenMP provides two directives – for and sections - to
specify concurrent iterations and tasks.
• The for directive is used to split parallel iteration spaces across
threads. The general form of a for directive is as follows:
#pragma omp for [clause list]
/* for loop */
• The clauses that can be used in this context are: private,
firstprivate, lastprivate, reduction, schedule, nowait,
and ordered.

Specifying Concurrent Tasks in OpenMP: Example
#pragma omp parallel default(private) shared (npoints)
reduction(+: sum) num_threads(8)
{
sum = 0;
#pragma omp for
for (i = 0; i < npoints; i++) {
(rand_no_y - 0.5) * (rand_no_y - 0.5)) < 0.25)
sum ++;
}
}

Assigning Iterations to Threads
• The schedule clause of the for directive deals with the
assignment of iterations to threads.
• The general form of the schedule directive is
schedule(scheduling class[, parameter]).
• OpenMP supports four scheduling classes: static, dynamic,
guided, and runtime.

Assigning Iterations to Threads: Example
/* static scheduling of matrix multiplication loops */
#pragma omp parallel default(private) shared (a, b, c, dim)
num_threads(4)
#pragma omp for schedule(static)
for (i = 0; i < dim; i++) {
for (j = 0; j < dim; j++) {
c(i,j) = 0;
for (k = 0; k < dim; k++) {
c(i,j) += a(i, k) * b(k, j);
}
}
}

Assigning Iterations to Threads: Example
32
C C
16 cols
A
B
C
32
A
A
(c)
(b)
128
B
(a)
128
128
32
32
B
Three different schedules using the static scheduling class of
OpenMP
.

Parallel For Loops
• Often, it is desirable to have a sequence of for-directives
within a parallel construct that do not execute an implicit
barrier at the end of each for directive.
• OpenMP provides a clause – nowait, which can be used with
a for directive.

Parallel For Loops: Example
#pragma omp parallel
{
#pragma omp for nowait
for (i = 0; i < nmax; i++)
if (isEqual(name, current_list[i])
processCurrentName(name);
#pragma omp for
for (i = 0; i < mmax; i++)
if (isEqual(name, past_list[i])
processPastName(name);
}

The sections Directive
• OpenMP supports non-iterative parallel task assignment using
the sections directive.
• The general form of the sections directive is as follows:
#pragma omp sections [clause list]
{
[#pragma omp section
]
[#pragma omp section
]
...
}

The sections Directive: Example
#pragma omp parallel
{
#pragma omp sections
{
#pragma omp section
{
taskA();
}
#pragma omp section
{
taskB();
}
#pragma omp section
{
taskC();
}
}
}

Nesting parallel Directives
• Nested parallelism can be enabled using the OMP NESTED
environment variable.
• If the OMP NESTED environment variable is set to TRUE, nested
parallelism is enabled.
• In this case, each parallel directive creates a new team of
threads.

Synchronization Constructs in OpenMP
OpenMP provides a variety of synchronization constructs:
#pragma omp barrier
#pragma omp single [clause list]
structured block
#pragma omp master
structured block
#pragma omp critical [(name)]
structured block
#pragma omp ordered
structured block

OpenMP Library Functions
In addition to directives, OpenMP also supports a number of
functions that allow a programmer to control the execution of
threaded programs.
/* thread and processor count */
void omp_set_num_threads (int num_threads);
int omp_get_num_threads ();
int omp_get_max_threads ();
int omp_get_thread_num ();
int omp_get_num_procs ();
int omp_in_parallel();

OpenMP Library Functions
/* controlling and monitoring thread creation */
void omp_set_dynamic (int dynamic_threads);
int omp_get_dynamic ();
void omp_set_nested (int nested);
int omp_get_nested ();
/* mutual exclusion */
void omp_init_lock (omp_lock_t *lock);
void omp_destroy_lock (omp_lock_t *lock);
void omp_set_lock (omp_lock_t *lock);
void omp_unset_lock (omp_lock_t *lock);
int omp_test_lock (omp_lock_t *lock);
In addition, all lock routines also have a nested lock counterpart
for recursive mutexes.

Environment Variables in OpenMP
• OMP NUM THREADS: This environment variable specifies the
default number of threads created upon entering a parallel
region.
• OMP SET DYNAMIC: Determines if the number of threads can be
dynamically changed.
• OMP NESTED: Turns on nested parallelism.
• OMP SCHEDULE: Scheduling of for-loops if the clause specifies
runtime.

Explicit Threads versus Directive Based Programming
• Directives layered on top of threads facilitate a variety of
thread-related tasks.
• A programmer is rid of the tasks of initializing attributes objects,
setting up arguments to threads, partitioning iteration spaces,
etc.
• There are some drawbacks to using directives as well.
• An artifact of explicit threading is that data exchange is more
apparent. This helps in alleviating some of the overheads from
data movement, false sharing, and contention.
• Explicit threading also provides a richer API in the form of
condition waits, locks of different types, and increased flexibility
for building composite synchronization operations.
• Finally, since explicit threading is used more widely than
OpenMP
, tools and support for Pthreads programs are easier
to find.

chap7_slidesforparallelcomputingananthgrama

More Related Content

Similar to chap7_slidesforparallelcomputingananthgrama (20)

Recently uploaded (20)

chap7_slidesforparallelcomputingananthgrama