OpenMP-Quinn17_L4bOpen <MP_Open MP_Open MP

Parallel Programming
in C with MPI and OpenMP
Michael J. Quinn

Chapter 17
Shared-memory Programming

Outline
• OpenMP
• Shared-memory model
• Parallel for loops
• Declaring private variables
• Critical sections
• Reductions
• Performance improvements
• More general data parallelism
• Functional parallelism

OpenMP
• OpenMP: An application programming
interface (API) for parallel programming on
multiprocessors
– Compiler directives
– Library of support functions
• OpenMP works in conjunction with
Fortran, C, or C++

What’s OpenMP Good For?
• C + OpenMP sufficient to program
multiprocessors
• C + MPI + OpenMP a good way to
program multicomputers built out of
multiprocessors
– IBM RS/6000 SP
– Fujitsu AP3000
– Dell High Performance Computing Cluster

Shared-memory Model
Proc essor Proc essor Proc essor Proc essor
M em ory
Processors interact and synchronize with each
other through shared variables.

Fork/Join Parallelism
• Initially only master thread is active
• Master thread executes sequential code
• Fork: Master thread creates or awakens
additional threads to execute parallel code
• Join: At end of parallel code created
threads die or are suspended

Fork/Join Parallelism
Tim
e
fork
join
M aster T hread
fork
join
O ther threads

Shared-memory Model vs.
Message-passing Model (#1)
– Number active threads 1 at start and finish
of program, changes dynamically during
execution
• Message-passing model
– All processes active throughout execution
of program

Incremental Parallelization
• Sequential program a special case of a
shared-memory parallel program
• Parallel shared-memory programs may
only have a single parallel loop
• Incremental parallelization: process of
converting a sequential program to a
parallel program a little bit at a time

Shared-memory Model vs.
Message-passing Model (#2)
– Execute and profile sequential program
– Incrementally make it parallel
– Stop when further effort not warranted
• Message-passing model
– Sequential-to-parallel transformation requires
major effort
– Transformation done in one giant step rather
than many tiny steps

Parallel for Loops
• C programs often express data-parallel
operations as for loops
for (i = first; i < size; i += prime)
marked[i] = 1;
• OpenMP makes it easy to indicate when the
iterations of a loop may execute in parallel
• Compiler takes care of generating code that
forks/joins threads and allocates the iterations to
threads

Pragmas
• Pragma: a compiler directive in C or C++
• Stands for “pragmatic information”
• A way for the programmer to communicate
with the compiler
• Compiler free to ignore pragmas
• Syntax:
#pragma omp <rest of pragma>

Parallel for Pragma
• Format:
#pragma omp parallel for
for (i = 0; i < N; i++)
a[i] = b[i] + c[i];
• Compiler must be able to verify the run-
time system will have information it needs
to schedule loop iterations

Canonical Shape of for Loop
Control Clause
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start

Execution Context
• Every thread has its own execution context
• Execution context: address space containing all
of the variables a thread may access
• Contents of execution context:
– static variables
– dynamically allocated data structures in the
heap
– variables on the run-time stack
– additional run-time stack for functions invoked
by the thread

Shared and Private Variables
• Shared variable: has same address in
execution context of every thread
• Private variable: has different address in
execution context of every thread
• A thread cannot access the private
variables of another thread

Shared and Private Variables
int m ain (int argc , c har *argv[])
{
int b[3];
c har *c ptr;
int i;
c ptr = m alloc (1);
#pragm a om p parallel for
for (i = 0; i < 3; i++)
b[i] = i;
Heap
Stac k
cptr
b i
i
i
M aster Thread
(Thread 0)
Thread 1

Function omp_get_num_procs
• Returns number of physical processors
available for use by the parallel program
int omp_get_num_procs (void)

Function omp_set_num_threads
• Uses the parameter value to set the
number of threads to be active in parallel
sections of code
• May be called at multiple points in a
program
void omp_set_num_threads (int t)

Pop Quiz:
Write a C program segment that sets the
number of threads equal to the number of
processors that are available.

Declaring Private Variables
for (i = 0; i < BLOCK_SIZE(id,p,n); i++)
for (j = 0; j < n; j++)
a[i][j] = MIN(a[i][j],a[i][k]+tmp);
• Either loop could be executed in parallel
• We prefer to make outer loop parallel, to reduce
number of forks/joins
• We then must give each thread its own private
copy of variable j

private Clause
• Clause: an optional, additional component
to a pragma
• Private clause: directs compiler to make
one or more variables private
private ( <variable list> )

Example Use of private Clause
#pragma omp parallel for private(j)
#pragma omp parallel for private(j)
for (j = 0; j < n; j++)
for (j = 0; j < n; j++)

firstprivate Clause
• Used to create private variables having initial
values identical to the variable controlled by the
master thread as the loop is entered
• Variables are initialized once per thread, not
once per loop iteration
• If a thread modifies a variable’s value in an
iteration, subsequent iterations will get the
modified value

lastprivate Clause
• Sequentially last iteration: iteration that
occurs last when the loop is executed
sequentially
• lastprivate clause: used to copy back
to the master thread’s copy of a variable
the private copy of the variable from the
thread that executed the sequentially last
iteration

Critical Sections
double area, pi, x;
int i, n;
...
area = 0.0;
for (i = 0; i < n; i++) {
x += (i+0.5)/n;
area += 4.0/(1.0 + x*x);
}
pi = area / n;

Race Condition
• Consider this C program segment to
compute  using the rectangle rule:
double area, pi, x;
int i, n;
...
area = 0.0;
for (i = 0; i < n; i++) {
x = (i+0.5)/n;
area += 4.0/(1.0 + x*x);
}
pi = area / n;

Race Condition (cont.)
• If we simply parallelize the loop...
double area, pi, x;
int i, n;
...
area = 0.0;
#pragma omp parallel for private(x)
for (i = 0; i < n; i++) {
x = (i+0.5)/n;
area += 4.0/(1.0 + x*x);
}
pi = area / n;

Race Condition (cont.)
• ... we set up a race condition in which one
process may “race ahead” of another and
not see its change to shared variable
area
11.667
area
area += 4.0/(1.0 + x*x)
Thread A Thread B
15.432
11.667
11.667
15.432 15.230
15.230 Answer should be 18.995

Race Condition Time Line
T hread A T hread B
Value of area
11.667
+ 3.765
+ 3.563
11.667
15.432
15.230

critical Pragma
• Critical section: a portion of code that only
thread at a time may execute
• We denote a critical section by putting the
pragma
#pragma omp critical
in front of a block of C code

Correct, But Inefficient, Code
double area, pi, x;
int i, n;
...
area = 0.0;
#pragma omp parallel for private(x)
for (i = 0; i < n; i++) {
x = (i+0.5)/n;
area += 4.0/(1.0 + x*x);
}
pi = area / n;

Source of Inefficiency
• Update to area inside a critical section
• Only one thread at a time may execute the
statement; i.e., it is sequential code
• Time to execute statement significant part
of loop
• By Amdahl’s Law we know speedup will
be severely constrained

Reductions
• Reductions are so common that OpenMP
provides support for them
• May add reduction clause to parallel for
pragma
• Specify reduction operation and reduction
variable
• OpenMP takes care of storing partial results in
private variables and combining partial results
after the loop

reduction Clause
• The reduction clause has this syntax:
reduction (<op> :<variable>)
• Operators
– + Sum
– * Product
– & Bitwise and
– | Bitwise or
– ^ Bitwise exclusive or
– && Logical and
– || Logical or

-finding Code with Reduction Clause
double area, pi, x;
int i, n;
...
area = 0.0;
#pragma omp parallel for
private(x) reduction(+:area)
for (i = 0; i < n; i++) {
x = (i + 0.5)/n;
area += 4.0/(1.0 + x*x);
}
pi = area / n;

Performance Improvement #1
• Too many fork/joins can lower
performance
• Inverting loops may help performance if
– Parallelism is in inner loop
– After inversion, the outer loop can be made
parallel
– Inversion does not significantly lower cache
hit rate

• If loop has too few iterations, fork/join
overhead is greater than time savings from
parallel execution
• The if clause instructs compiler to insert
code that determines at run-time whether
loop should be executed in parallel; e.g.,
#pragma omp parallel for if(n > 5000)

• We can use schedule clause to specify how
iterations of a loop should be allocated to
threads
• Static schedule: all iterations allocated to
threads before any iterations executed
• Dynamic schedule: only some iterations
allocated to threads at beginning of loop’s
execution. Remaining iterations allocated to
threads that complete their assigned iterations.

Static vs. Dynamic Scheduling
• Static scheduling
– Low overhead
– May exhibit high workload imbalance
• Dynamic scheduling
– Higher overhead
– Can reduce workload imbalance

Chunks
• A chunk is a contiguous range of iterations
• Increasing chunk size reduces overhead
and may increase cache hit rate
• Decreasing chunk size allows finer
balancing of workloads

schedule Clause
• Syntax of schedule clause
schedule (<type>[,<chunk> ])
• Schedule type required, chunk size optional
• Allowable schedule types
– static: static allocation
– dynamic: dynamic allocation
– guided: guided self-scheduling
– runtime: type chosen at run-time based on
value of environment variable
OMP_SCHEDULE

Scheduling Options
• schedule(static): block allocation of about
n/t contiguous iterations to each thread
• schedule(static,C): interleaved allocation
of chunks of size C to threads
• schedule(dynamic): dynamic one-at-a-time
allocation of iterations to threads
• schedule(dynamic,C): dynamic allocation
of C iterations at a time to threads

Scheduling Options (cont.)
• schedule(guided, C): dynamic allocation of
chunks to tasks using guided self-scheduling
heuristic. Initial chunks are bigger, later chunks
are smaller, minimum chunk size is C.
• schedule(guided): guided self-scheduling with
minimum chunk size 1
• schedule(runtime): schedule chosen at run-time
based on value of OMP_SCHEDULE; Unix
example:
setenv OMP_SCHEDULE “static,1”

More General Data Parallelism
• Our focus has been on the parallelization
of for loops
• Other opportunities for data parallelism
– processing items on a “to do” list
– for loop + additional code outside of loop

Processing a “To Do” List
Heap
job_ptr
Shared
Variables
M aster T hread T hread 1
task_ptr task_ptr

Sequential Code (1/2)
int main (int argc, char *argv[])
{
struct job_struct *job_ptr;
struct task_struct *task_ptr;
...
task_ptr = get_next_task (&job_ptr);
while (task_ptr != NULL) {
complete_task (task_ptr);
}
...
}

Sequential Code (2/2)
char *get_next_task(struct job_struct
**job_ptr) {
struct task_struct *answer;
if (*job_ptr == NULL) answer = NULL;
else {
answer = (*job_ptr)->task;
*job_ptr = (*job_ptr)->next;
}
return answer;
}

Parallelization Strategy
• Every thread should repeatedly take next
task from list and complete it, until there
are no more tasks
• We must ensure no two threads take
same take from the list; i.e., must declare
a critical section

parallel Pragma
• The parallel pragma precedes a block
of code that should be executed by all of
the threads
• Note: execution is replicated among all
threads

Use of parallel Pragma
#pragma omp parallel private(task_ptr)
{
while (task_ptr != NULL) {
complete_task (task_ptr);
}
}

Critical Section for get_next_task
char *get_next_task(struct job_struct
**job_ptr) {
struct task_struct *answer;
{
if (*job_ptr == NULL) answer = NULL;
else {
answer = (*job_ptr)->task;
*job_ptr = (*job_ptr)->next;
}
}
return answer;
}

Functions for SPMD-style
Programming
• The parallel pragma allows us to write
SPMD-style programs
• In these programs we often need to
know number of threads and thread ID
number
• OpenMP provides functions to retrieve
this information

Function omp_get_thread_num
• This function returns the thread
identification number
• If there are t threads, the ID numbers
range from 0 to t-1
• The master thread has ID number 0
int omp_get_thread_num (void)

Function
omp_get_num_threads
• Function omp_get_num_threads returns
the number of active threads
• If call this function from sequential portion
of program, it will return 1
int omp_get_num_threads (void)

for Pragma
• The parallel pragma instructs every
thread to execute all of the code inside the
block
• If we encounter a for loop that we want to
divide among threads, we use the for
pragma
#pragma omp for

Example Use of for Pragma
#pragma omp parallel private(i,j)
for (i = 0; i < m; i++) {
low = a[i];
high = b[i];
if (low > high) {
printf ("Exiting (%d)n", i);
break;
}
#pragma omp for
for (j = low; j < high; j++)
c[j] = (c[j] - a[i])/b[i];
}

single Pragma
• Suppose we only want to see the output
once
• The single pragma directs compiler that
only a single thread should execute the
block of code the pragma precedes
• Syntax:
#pragma omp single

Use of single Pragma
#pragma omp parallel private(i,j)
for (i = 0; i < m; i++) {
low = a[i];
high = b[i];
if (low > high) {
#pragma omp single
break;
}
#pragma omp for
c[j] = (c[j] - a[i])/b[i];
}

nowait Clause
• Compiler puts a barrier synchronization at
end of every parallel for statement
• In our example, this is necessary: if a
thread leaves loop and changes low or
high, it may affect behavior of another
thread
• If we make these private variables, then it
would be okay to let threads move ahead,
which could reduce execution time

Use of nowait Clause
#pragma omp parallel private(i,j,low,high)
for (i = 0; i < m; i++) {
low = a[i];
high = b[i];
if (low > high) {
#pragma omp single
break;
}
#pragma omp for nowait
c[j] = (c[j] - a[i])/b[i];
}

Functional Parallelism
• To this point all of our focus has been on
exploiting data parallelism
• OpenMP allows us to assign different
threads to different portions of code
(functional parallelism)

Functional Parallelism Example
v = alpha();
w = beta();
x = gamma(v, w);
y = delta();
printf ("%6.2fn", epsilon(x,y));
alpha beta
gam m a delta
epsilon
May execute alpha,
beta, and delta in
parallel

parallel sections Pragma
• Precedes a block of k blocks of code that
may be executed concurrently by k
threads
• Syntax:
#pragma omp parallel sections

section Pragma
• Precedes each block of code within the
encompassing block preceded by the
parallel sections pragma
• May be omitted for first parallel section
after the parallel sections pragma
• Syntax:
#pragma omp section

Example of parallel sections
#pragma omp parallel sections
{
#pragma omp section /* Optional */
v = alpha();
#pragma omp section
w = beta();
#pragma omp section
y = delta();
}
x = gamma(v, w);

Another Approach
alpha beta
gam m a delta
epsilon
Execute alpha and
beta in parallel.
Execute gamma and
delta in parallel.

sections Pragma
• Appears inside a parallel block of code
• Has same meaning as the parallel
sections pragma
• If multiple sections pragmas inside one
parallel block, may reduce fork/join costs

Use of sections Pragma
#pragma omp parallel
{
#pragma omp sections
{
v = alpha();
#pragma omp section
w = beta();
}
#pragma omp sections
{
x = gamma(v, w);
#pragma omp section
y = delta();
}
}

Summary (1/3)
• OpenMP an API for shared-memory
parallel programming
• Shared-memory model based on fork/join
parallelism
• Data parallelism
– parallel for pragma
– reduction clause

Summary (2/3)
• Functional parallelism (parallel sections pragma)
• SPMD-style programming (parallel pragma)
• Critical sections (critical pragma)
• Enhancing performance of parallel for loops
– Inverting loops
– Conditionally parallelizing loops
– Changing loop scheduling

Summary (3/3)
Characteristic
Characteristic OpenMP
OpenMP MPI
MPI
Suitable for multiprocessors
Suitable for multiprocessors Yes
Yes Yes
Yes
Suitable for multicomputers
Suitable for multicomputers No
No Yes
Yes
Supports incremental
Supports incremental
parallelization
parallelization
Yes
Yes No
No
Minimal extra code
Minimal extra code Yes
Yes No
No
Explicit control of memory
Explicit control of memory
hierarchy
hierarchy
No
No Yes
Yes

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