Shared-Memory Computing with Open MP
undefined
Lecture 5: Shared-memory Computing with
Open MP
undefined
Shared Memory Computing
Getting Started: Example 1 (Hello)
#include "stdafx.h"
Getting Started: Example 1 (Hello)
Set the compiler option in the Visual Studio development environment
1.
Open the project's
Property Pages
dialog box.
2.
Expand the
Configuration Properties
node.
3.
Expand the
C/C++
node.
4.
Select the
Language
property page.
5.
Modify the
OpenMP Support
property.
Set the arguments to the main function parameters in the Visual Studio
development environment
(e.g.,
int
main(
int
argc,
char
* argv[])
1.
In Project Properties (click Project or project name on solution Explore),
expand Configuration Properties, and then click Debugging.
2.
In the pane on the right, in the textbox to the right of Command Arguments,
type the arguments to main you want to use. For example, one two
In case the compiler doesn’t support OpenMP
CS4230
# include <omp.h>
#ifdef _OPENMP
# include <omp.h>
#endif
08/30/2012
5
In case the compiler doesn’t support OpenMP
CS4230
# ifdef _OPENMP
int my_rank = omp_get_thread_num ( );
int thread_count = omp_get_num_threads ( );
# e l s e
int my_rank = 0;
int thread_count = 1;
# endif
08/30/2012
6
OpenMP: Prevailing Shared Memory Programming Approach
•
Model for shared-memory parallel programming
•
Portable across shared-memory architectures
•
Scalable (on shared-memory platforms)
•
Incremental parallelization
-
Parallelize individual computations in a program while leaving
the rest of the program sequential
•
Compiler based
-
Compiler generates thread program and synchronization
•
Extensions to existing programming languages
(Fortran, C and C++)
-
mainly by directives
-
a few library routines
See http://www.openmp.org
09/06/2011
CS4961
OpenMP Execution Model
09/04/2012
CS4230
join
OpenMP uses Pragmas
•
Pragmas are special preprocessor instructions.
•
Typically added to a system to allow behaviors that
aren’t part of the basic C specification.
•
Compilers that don’t support the pragmas ignore them.
•
The interpretation of OpenMP pragmas
-
They modify the statement immediately following the pragma
-
This could be a compound statement such as a loop
#pragma omp …
08/30/2012
CS4230
9
OpenMP parallel region construct
•
Block of code to be executed by multiple threads in
parallel
•
E
a
c
h
t
h
r
e
a
d
e
x
e
c
u
t
e
s
t
h
e
s
a
m
e
c
o
d
e
r
e
d
u
n
d
a
n
t
l
y
(
S
P
M
D
)
-
W
o
r
k
w
i
t
h
i
n
w
o
r
k
-
s
h
a
r
i
n
g
c
o
n
s
t
r
u
c
t
s
i
s
d
i
s
t
r
i
b
u
t
e
d
a
m
o
n
g
t
h
e
t
h
r
e
a
d
s
i
n
a
t
e
a
m
•
Example with C/C++ syntax
#pragma omp parallel
[ clause [ clause ] ... ] new-line
structured-block
•
clause can include the following:
private
(list)
s
h
a
r
e
d
(
l
i
s
t
)
09/04/2012
CS4230
Programming Model – Data Sharing
•
Parallel programs often employ
two types of data
-
Shared data, visible to all
threads, similarly named
-
Private data, visible to a single
thread (often stack-allocated)
•
OpenMP:
•
shared
variables are shared
•
private
variables are private
•
Default is
shared
•
Loop index is
private
/
/
s
h
a
r
e
d
,
g
l
o
b
a
l
s
i
n
t
b
i
g
d
a
t
a
[
1
0
2
4
]
;
v
o
i
d
*
f
o
o
(
v
o
i
d
*
b
a
r
)
{/
/
p
r
i
v
a
t
e
,
s
t
a
c
k
i
n
t
t
i
d
;
/
*
C
a
l
c
u
l
a
t
i
o
n
g
o
e
s
h
e
r
e
*
/
}
int bigdata[1024];
void* foo(void* bar) {int tid;
#pragma omp parallel \
shared ( bigdata ) \
private ( tid )
{/* Calc. here */
}
}
OpenMP critical directive
•
Enclosed code
– executed by all threads, but
–
restricted to only one thread at a time
#pragma omp critical
[
(
name
)
] new-line
structured-block
•
A thread waits at the beginning of a critical region until no
other thread in the team is executing a critical region with
the same name.
•
All unnamed
critical
directives map to the same
unspecified name.
09/04/2012
CS4230
Example 2: calculate the area under a curve
CS4230
T
h
e
t
r
a
p
e
z
o
i
d
a
l
r
u
l
e
S
e
r
i
a
l
p
r
o
g
r
a
m
P
a
r
a
l
l
e
l
p
r
o
g
r
a
m
f
o
r
m
y
_
r
a
n
k
m
y
_
r
a
n
k
=
0
m
y
_
r
a
n
k
=
1
CS4230
Example 2: calculate the area under a curve using
critical directive
CS4230
Example 2: calculate the area under a curve using
critical directive
OpenMp Reductions
•
OpenMP has reduce operation
sum = 0;
#pragma omp parallel for reduction(+:sum)
for (i=0; i < 100; i++) {sum += array[i];
}
•
Reduce ops and init() values (C and C++):
+ 0 bitwise & ~0 logical & 1
- 0 bitwise | 0 logical | 0
* 1 bitwise ^ 0
FORTRAN also supports min and max reductions
09/04/2012
CS4230
CS4230
Example 2: calculate the area under a curve using
reduction clause
Local_trap
Local_trap
CS4230
09/04/2012
Example 2: calculate the area under a curve using loop
Example 3: calculate
π
#include
"stdafx.h"
#ifdef
_OPENMP
#include
<omp.h>
#endif
#include
<stdio.h>
#include
<stdlib.h>
#include
<windows.h>
int
main(
int
argc,
char
* argv[]) {double
global_result = 0.0;
volatile
DWORD dwStart;
int
n = 100000000;
printf(
"numberInterval %d \n"
, n);
int
numThreads = strtol(argv[1], NULL, 10);
dwStart = GetTickCount();
# pragma
omp parallel num_threads(numThreads)
PI(0, 1, n, &global_result);
printf(
"number of threads %d \n"
, numThreads);
printf(
"Pi = %f \n"
, global_result);
printf_s(
"milliseconds %d \n"
, GetTickCount() - dwStart);
}
Example 3: calculate
π
void
PI(
double
a,
double
b,
int
numIntervals,
double
* global_result_p) {int
i;
double
x, my_result, sum = 0.0, interval, local_a, local_b, local_numIntervals;
int
myThread = omp_get_thread_num();
int
numThreads = omp_get_num_threads();
interval = (b-a)/ (
double
) numIntervals;
local_numIntervals = numIntervals/numThreads;
local_a = a + myThread*local_numIntervals*interval;
local_b = local_a + local_numIntervals*interval;
sum = 0.0;
for
(i = 0; i < local_numIntervals; i++) {x = local_a + i*interval;
sum = sum + 4.0 / (1.0 + x*x);
};
my_result = interval * sum;
# pragma
om critical
*global_result_p += my_result;
}
A Programmer’s View of OpenMP
•
OpenMP is a portable, threaded, shared-memory
programming
specification
with “light” syntax
-
Exact behavior depends on OpenMP implementation!
-
Requires compiler support (
C/C++
or Fortran)
•
OpenMP will:
-
Allow a programmer to separate a program into
serial regions
and
parallel regions,
rather than concurrently-executing
threads
.
-
Hide stack management
-
Provide synchronization constructs
•
OpenMP will not:
-
Parallelize automatically
-
Guarantee speedup
-
Provide freedom from data races
08/30/2012
CS4230
21
OpenMP runtime library, Query Functions
omp_get_num_threads
:
Returns the number of threads currently in the team executing the
parallel region from which it is called
int omp_get_num_threads(void);
omp_get_thread_num:
Returns the thread number, within the team, that lies between
0
and
omp_get_num_threads()
, inclusive. The master thread of the
team is thread
0
int omp_get_thread_num(void);
08/30/2012
CS4230
22
Impact of Scheduling Decision
•
Load balance
-
Same work in each iteration?
-
Processors working at same speed?
•
Scheduling overhead
-
Static decisions are cheap because they require no run-time
coordination
-
Dynamic decisions have overhead that is impacted by
complexity and frequency of decisions
•
Data locality
-
Particularly within cache lines for small chunk sizes
-
Also impacts data reuse on same processor
09/04/2012
CS4230
09/04/2012
CS4230
Summary of Lecture
•
OpenMP, data-parallel constructs only
-
Task-parallel constructs later
•
What’s good?
-
Small changes are required to produce a parallel program from
sequential (parallel formulation)
-
Avoid having to express low-level mapping details
-
Portable and scalable, correct on 1 processor
•
What is missing?
-
Not completely natural if want to write a parallel code from
scratch
-
Not always possible to express certain common parallel
constructs
-
Locality management
-
Control of performance
E
x
e
r
c
i
s
e
(1)
Read example 1 -3. Compile them and run them, respectively.
(2) Revise the programs in (1) using reduction clause and using loop, respectively.
(3) For each value of N obtained in (2), get the running time when the number of threads is
1,2,4,8,16, respectively, find the speed-up rate (notice that the ideal rate is, investigate the
change in the speed-up rates, and discuss the reasons.
(4)
Suppose matrix A and matrix B are saved in two dimension arrays. Write OpenMP
programs for A+B and A*B, respectively. Run them using different number of threads, and
find the speed-up rate.
Shared-memory computing with Open MP offers a parallel programming model that is portable and scalable across shared-memory architectures. It allows for incremental parallelization, compiler-based thread program generation, and synchronization. Open MP pragmas help in parallelizing individual computations within a program while maintaining the sequential execution of the rest. The approach involves extending existing programming languages like Fortran, C, and C++ through directives and a few library routines.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
E N D
Presentation Transcript
Getting Started: Example 1 (Hello) #include "stdafx.h"
Getting Started: Example 1 (Hello) Set the compiler option in the Visual Studio development environment 1. Open the project's Property Pages dialog box. 2. Expand the Configuration Properties node. 3. Expand the C/C++ node. 4. Select the Language property page. 5. Modify the OpenMP Support property. Set the arguments to the main function parameters in the Visual Studio development environment (e.g., int main(int argc, char* argv[]) 1. In Project Properties (click Project or project name on solution Explore), expand Configuration Properties, and then click Debugging. 2. In the pane on the right, in the textbox to the right of Command Arguments, type the arguments to main you want to use. For example, one two
In case the compiler doesnt support OpenMP # include <omp.h> #ifdef _OPENMP # include <omp.h> #endif CS4230 08/30/2012 5
In case the compiler doesnt support OpenMP # ifdef _OPENMP int my_rank = omp_get_thread_num ( ); int thread_count = omp_get_num_threads ( ); # e l s e int my_rank = 0; int thread_count = 1; # endif CS4230 08/30/2012 6
OpenMP: Prevailing Shared Memory Programming Approach Model for shared-memory parallel programming Portable across shared-memory architectures Scalable (on shared-memory platforms) Incremental parallelization - Parallelize individual computations in a program while leaving the rest of the program sequential Compiler based - Compiler generates thread program and synchronization Extensions to existing programming languages (Fortran, C and C++) - mainly by directives - a few library routines See http://www.openmp.org 09/06/2011 CS4961
OpenMP Execution Model fork join 09/04/2012 CS4230
OpenMP uses Pragmas Pragmas are special preprocessor instructions. Typically added to a system to allow behaviors that aren t part of the basic C specification. Compilers that don t support the pragmas ignore them. The interpretation of OpenMP pragmas - They modify the statement immediately following the pragma - This could be a compound statement such as a loop #pragma omp 08/30/2012 CS4230 9
OpenMP parallel region construct Block of code to be executed by multiple threads in parallel Each thread executes the same code redundantly (SPMD) - Work within work-sharing constructs is distributed among the threads in a team Example with C/C++ syntax #pragma omp parallel [ clause [ clause ] ... ] new-line structured-block clause can include the following: private (list) shared (list) 09/04/2012 CS4230
Programming Model Data Sharing // shared, globals Parallel programs often employ two types of data - Shared data, visible to all threads, similarly named - Private data, visible to a single thread (often stack-allocated) int bigdata[1024]; int bigdata[1024]; void* foo(void* bar) { void* foo(void* bar) { // private, stack int tid; int tid; OpenMP: shared variables are shared private variables are private Default is shared Loop index is private #pragma omp parallel \ /* Calculation goes shared ( bigdata ) \ here */ private ( tid ) } { /* Calc. here */ } }
OpenMP critical directive Enclosed code executed by all threads, but restricted to only one thread at a time #pragma omp critical [ ( name ) ] new-line structured-block A thread waits at the beginning of a critical region until no other thread in the team is executing a critical region with the same name. All unnamedcritical directives map to the same unspecified name. 09/04/2012 CS4230
Example 2: calculate the area under a curve The trapezoidal rule my_rank = 0 my_rank = 1 Parallel program for my_rank Serial program CS4230
Example 2: calculate the area under a curve using critical directive CS4230
Example 2: calculate the area under a curve using critical directive CS4230
OpenMp Reductions OpenMP has reduce operation sum = 0; #pragma omp parallel for reduction(+:sum) for (i=0; i < 100; i++) { sum += array[i]; } Reduce ops and init() values (C and C++): + 0 bitwise & ~0 logical & 1 - 0 bitwise | 0 logical | 0 * 1 bitwise ^ 0 FORTRAN also supports min and max reductions 09/04/2012 CS4230
Example 2: calculate the area under a curve using reduction clause Local_trap Local_trap CS4230
Example 2: calculate the area under a curve using loop CS4230 09/04/2012
Example 3: calculate #include "stdafx.h" #ifdef _OPENMP #include <omp.h> #endif #include <stdio.h> #include <stdlib.h> #include <windows.h> int main(int argc, char* argv[]) { double global_result = 0.0; volatile DWORD dwStart; int n = 100000000; printf("numberInterval %d \n", n); int numThreads = strtol(argv[1], NULL, 10); dwStart = GetTickCount(); # pragma omp parallel num_threads(numThreads) PI(0, 1, n, &global_result); printf("number of threads %d \n", numThreads); printf("Pi = %f \n", global_result); printf_s("milliseconds %d \n", GetTickCount() - dwStart); }
Example 3: calculate void PI(double a, double b, int numIntervals, double* global_result_p) { int i; double x, my_result, sum = 0.0, interval, local_a, local_b, local_numIntervals; int myThread = omp_get_thread_num(); int numThreads = omp_get_num_threads(); interval = (b-a)/ (double) numIntervals; local_numIntervals = numIntervals/numThreads; local_a = a + myThread*local_numIntervals*interval; local_b = local_a + local_numIntervals*interval; sum = 0.0; for (i = 0; i < local_numIntervals; i++) { x = local_a + i*interval; sum = sum + 4.0 / (1.0 + x*x); }; my_result = interval * sum; # pragma om critical *global_result_p += my_result; }
A Programmers View of OpenMP OpenMP is a portable, threaded, shared-memory programming specificationwith light syntax - Exact behavior depends on OpenMP implementation! - Requires compiler support (C/C++ or Fortran) OpenMP will: - Allow a programmer to separate a program into serial regions and parallel regions, rather than concurrently-executing threads. - Hide stack management - Provide synchronization constructs OpenMP will not: - Parallelize automatically - Guarantee speedup - Provide freedom from data races 08/30/2012 CS4230 21
OpenMP runtime library, Query Functions omp_get_num_threads: Returns the number of threads currently in the team executing the parallel region from which it is called int omp_get_num_threads(void); omp_get_thread_num: Returns the thread number, within the team, that lies between 0 and omp_get_num_threads(), inclusive. The master thread of the team is thread 0 int omp_get_thread_num(void); 08/30/2012 CS4230 22
Impact of Scheduling Decision Load balance - Same work in each iteration? - Processors working at same speed? Scheduling overhead - Static decisions are cheap because they require no run-time coordination - Dynamic decisions have overhead that is impacted by complexity and frequency of decisions Data locality - Particularly within cache lines for small chunk sizes - Also impacts data reuse on same processor 09/04/2012 CS4230
Summary of Lecture OpenMP, data-parallel constructs only - Task-parallel constructs later What s good? - Small changes are required to produce a parallel program from sequential (parallel formulation) - Avoid having to express low-level mapping details - Portable and scalable, correct on 1 processor What is missing? - Not completely natural if want to write a parallel code from scratch - Not always possible to express certain common parallel constructs - Locality management - Control of performance 09/04/2012 CS4230
Exercise (1)Read example 1 -3. Compile them and run them, respectively. (2) Revise the programs in (1) using reduction clause and using loop, respectively. (3) For each value of N obtained in (2), get the running time when the number of threads is 1,2,4,8,16, respectively, find the speed-up rate (notice that the ideal rate is, investigate the change in the speed-up rates, and discuss the reasons. (4) Suppose matrix A and matrix B are saved in two dimension arrays. Write OpenMP programs for A+B and A*B, respectively. Run them using different number of threads, and find the speed-up rate.
