Algoritmi e Calcolo Parallelo 2012/2013 - OpenMP

OpenMP
Algoritmi e Calcolo Parallelo

Prof. Pier Luca Lanzi

References

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2

Using OpenMP: Portable Shared Memory Parallel
Programming,
Barbara Chapman, Gabriele Jost and Ruud van der Pas
OpenMP.org
http://openmp.org/
OpenMP Tutorial
https://computing.llnl.gov/tutorials/openMP/


Prologue

Threads for Dummies (1)

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A thread is a sequence of instructions to be executed within a
program
Usually a program consist of a single thread of execution that starts in
main(). Each line of your code is executed in turn, exactly one line at a
time.
A (UNIX) process consists of
A virtual address space (i.e., stack, data, and code segments)
System resources (e.g., open files)
A thread of execution

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Threads for Dummies (2)

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In a threaded environment, one process consists of multiple threads
Each user process can have several threads of execution, different
parts of the user’s code can be executing simultaneously
The collection of threads form a single process!
Each thread in the process sees the same virtual address space, files,
etc.


You can manage the multiple threads in your
process yourself (but it is quite complex!)
Or you can let OpenMP do it almost automatically!


The Parallelization Model of OpenMP


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Fork Join Parallelization


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Introduction

OpenMP = Open Multi-Processing
API for multi-platform shared memory
multiprocessing programming
Supports C/C++ and Fortran
Available on Unix and on MS Windows
Compiler directives, Library routines, &
Environment variables

History of OpenMP

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The OpenMP Architecture Review Board (ARB) published its first API
specifications, OpenMP for Fortran 1.0, in October 1997. October the
following year they released the C/C++ standard.
2000 saw version 2.0 of the Fortran specifications with version 2.0
of the C/C++ specifications being released in 2002.
Version 2.5 is a combined C/C++/Fortran specification that was
released in 2005.
Version 3.0, released in May, 2008, is the current version of the API
specifications. Included in the new features in 3.0 is the concept of
tasks and the task construct. These new features are summarized in
Appendix F of the OpenMP 3.0 specifications.

What are the Goals of OpenMP?

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Standardization
Provide a standard among a variety of shared memory
architectures
Lean and Mean
Establish a simple and limited set of directives for programming
shared memory machines (significant parallelism can be
implemented by using just 3 or 4 directives)
Ease of Use
Provide capability to incrementally parallelize a serial program
(unlike message-passing libraries)
Provide the capability to implement both coarse-grain
and fine-grain parallelism
Portability
Supports Fortran (77, 90, and 95), C, and C++

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How Does It Work?

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OpenMP is an implementation of multithreading
A master thread "forks" a specified number of slave threads
Tasks are divided among slaves
Slaves run concurrently as the runtime environment allocating threads
to different processors


Most OpenMP constructs are
compiler directives or pragmas
The focus of OpenMP is to parallelize loops
OpenMP offers an incremental
approach to parallelism


Overview of OpenMP

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A compiler directive in C/C++ is called a pragma (pragmatic information). It is
a preprocessor directive, thus it is declared with a hash (#). Compiler
directives specific to OpenMP in C/C++ are written in codes as follows:
#pragma omp <rest of pragma>

The OpenMP Programming Model

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Based on compiler directives
Nested Parallelism Support
API allows parallel constructs inside other parallel constructs
Dynamic Threads
API allows to dynamically change the number of threads which
may used to execute different parallel regions
Input/Output
OpenMP specifies nothing about parallel I/O
The programmer has to insure that I/O is conducted correctly
within the context of a multi-threaded program
Memory consistency
Threads can "cache" their data and are not required to maintain
exact consistency with real memory all of the time
When it is critical that all threads view a shared variable
identically, the programmer is responsible for insuring that the
variable is FLUSHed by all threads as needed

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Basic Elements

Hello World

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#include <iostream>
using namespace std;
int main()
{
cout << "Hello Worldn";
}

$ ./hello
Hello World


Hello World in OpenMP

#include <omp.h>
$ ./hello
#include <iostream>
Hello World
int main()
Hello World
{
Hello World
#pragma omp parallel num_threads(3)
{
cout << "Hello Worldn”;
// what if
// cout << "Hello World" << endl;
}
}


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Compiling an OpenMP Program

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OpenMP consists of a set of directives and macros that
are translated in C/C++ and thread instructions

To compile a program using OpenMP, we need to include
the flag “openmp”,
g++ -o example ./source/openmp00.cpp –fopenmp

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The command compile the file openmp00.cpp using
openmp and generates the executable named “example”


What Does Really Happen?

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OpenMP consists of a set of directives and macros that
are translated in C/C++ and thread instructions

We can check how the original program using OpenMP is
translated, using the command
g++ -fdump-tree-omplower ./source/openmp00.cpp –
fopenmp


OpenMP Directives

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A valid OpenMP directive must appear after the #pragma omp name
name identify an OpenMP directive
The directive name can be followed by optional clauses (in any order)
An OpenMP directive precedes the structured block which is enclosed
by the directive itself
#pragma omp name [clause, ...]
{
…
}

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Example
{
cout << "Hello Worldn";
}

Directive Scoping

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Static (Lexical) Extent
The code textually enclosed between the beginning and the end of
a structured block following a directive
The static extent of a directives does not span multiple routines or
code files
Orphaned Directive
An OpenMP directive that appears independently from another
enclosing directive. It exists outside of another directive's static
(lexical) extent.
Will span routines and possibly code files
Dynamic Extent
The dynamic extent of a directive includes both its static (lexical)
extent and the extents of its orphaned directives.

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Directive Scoping: example
void f(){
#pragma omp name2 [clause, ...]
{
…
}
}
int main(){
{
f();
…
}
}

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void f(){
{
…
}
}

Static extent of
name1

int main(){
{
f();
…
}
}

void f(){
{
…
}

Orphaned
directive

}

Dynamic extent of
name1

int main(){
{
f();
…
}
}

Parallel Construct

The Parallel Construct/Directive

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Plays a crucial role in OpenMP, a program without a
parallel construct will be executed sequentially
#pragma omp parallel [clause, clause, ... ]
{
// block
}

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When a thread reaches a parallel directive, it creates a
team of threads and becomes the master of the team.
The master is a member of that team and has thread
number 0 within that team.


The Parallel Construct/Directive

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Starting from the beginning of the parallel region, the code
is duplicated and all threads will execute that code.

There is an implied barrier at the end of a parallel section.
Only the master thread continues execution past this
point.
If any thread terminates within a parallel region, all
threads in the team will terminate, and the work done up
until that point is undefined.
Threads are numbered from 0 (master thread) to N-1


How Many Threads?

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The number of threads in a parallel region is determined
by the following factors, in order of precedence:
Evaluation of the if clause
Setting of the num_threads clause
Use of the omp_set_num_threads() library function
Setting of the OMP_NUM_THREADS environment
variable
Implementation default, e.g., the number of CPUs on a
node

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When present, the if clause must evaluate to true (i.e.,
non-zero integer) in order for a team of threads to be
created; otherwise, the region is executed serially by the
master thread. Prof. Pier Luca Lanzi

Run-Time Library Routines

Overview

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The OpenMP standard defines an API for a variety of library functions:
Query the number of threads/processors, set number of threads to
use
General purpose locking routines (semaphores)
Portable wall clock timing routines

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Set execution environment functions: nested parallelism, dynamic
adjustment of threads
It may be necessary to specify the include file "omp.h".
For the lock routines/functions
The lock variable must be accessed only through the locking
routines
The lock variable must have type omp_lock_t or type
omp_nest_lock_t, depending on the function being used.

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Run-Time Routines

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void omp_set_num_threads(int num_threads)

 Sets the number of threads that will be used in the next parallel
region
num_threads must be a postive integer

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 This routine can only be called from the serial portions of the code
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int omp_get_num_threads(void)

 Returns the number of threads that are currently in the team
executing the parallel region from which it is called

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int omp_get_thread_num(void)

 Returns the thread number of the thread, within the team,
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making this call.
This number will be between 0 and OMP_GET_NUM_THREADS1. The master thread of the team is thread 0

A better Hello World in OpenMP

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#include <omp.h>
#include <iostream>
int main()
{
{
cout << "Hello World. I am thread";
cout << omp_get_thread_num() << endl;
}
}
$ ./hello
Hello World. I am thread 0

Conditional Compiling
// openmp03.cpp
#ifdef _OPENMP
#include <omp.h>
#else
#define omp_get_thread_num() 0
#endif
...
// numero del thread corrente
int TID = omp_get_thread_num();
...


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Data Scope Attribute Clauses

Variables Scope in OpenMP

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OpenMP is based upon the shared memory programming
model, most variables are shared by default

The OpenMP Data Scope Attribute Clauses are used to
explicitly define how variables should be scoped
Four main scope types
private
shared
default
reduction

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Variables Scope in OpenMP

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Data Scope Attribute Clauses are used in conjunction with
several directives to
Define how and which data variables in the serial
section of the program are transferred to the parallel
sections
Define which variables will be visible to all threads in
the parallel sections and which variables will be
privately allocated to all threads

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Data Scope Attribute Clauses are effective only within
their lexical/static extent.


The private Clause

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Syntax: private (list)
The private clause declares variables in its list to be
private to each thread
Private variables behave as follows:
a new object of the same type is declared once for
each thread in the team
all references to the original object are replaced with
references to the new object
variables should be assumed to be uninitialized for
each thread

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Example: private Clause

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#pragma omp parallel for private(i,a)
for (i=0; i<n; i++)
{
a = i+1;
printf("Thread %d has a value of a = %d for i = %dn",
omp_get_thread_num(),a,i);
} /*-- End of parallel for --*/


The shared Clause

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Syntax: shared (list)
The shared clause declares variables in its list to be
shared among all threads in the team.
A shared variable exists in only one memory location and
all threads can read or write to that address.
It is the programmer's responsibility to ensure that multiple
threads properly access shared variables


Example: shared Clause
#pragma omp parallel for shared(a)
for (i=0; i<n; i++)
{
a[i] += i;
} /*-- End of parallel for --*/


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The default clause

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Syntax: default (shared | none)
The default scope of all variables is either set to shared or
none
The default clause allows the user to specify a default
scope for all variables in the lexical extent of any parallel
region
Specific variables can be exempted from the default using
specific clauses (e.g., private, shared, etc.)
Using none as a default requires that the programmer
explicitly scope all variables.


The reduction Clause

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Syntax: reduction (operator: list)
Performs a reduction on the variables that appear in its list
Operator defines the reduction operation
A private copy for each list variable is created for each
thread
At the end of the reduction, the reduction variable is
applied to all private copies of the shared variable, and the
final result is written to the global shared variable


Work-Sharing

Work-Sharing Constructs

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Divide the execution of the enclosed code region among the members of the
team that encounter it
A work-sharing construct must be enclosed dynamically within a parallel region in
order for the directive to execute in parallel
Work-sharing constructs do not launch new threads
There is no implied barrier upon entry to a work-sharing construct, however there is an
implied barrier at the end of a work sharing construct

for: shares iterations of a loop
across the team (data
parallelism)

sections: breaks work into
separate, discrete sections, each
executed by a thread (functional
parallelism)


single: serializes a
section of code.

The for Construct/Directive

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#pragma omp for [clause ...]
for_loop
Three main clause types: schedule, nowait, and the data scope
schedule (type [,chunk]) describes how iterations of the loop are
divided among the threads in the team (default schedule is
implementation dependent)
nowait if specified, then threads do not synchronize at the end of the
parallel loop.
The data-scope clauses (private, shared, reduction)


The for Construct/Directive
(Schedule Types)

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static: loop iterations are divided into blocks of size chunk
and then statically assigned to threads
If chunk is not specified, the iterations are evenly (if
possible) divided contiguously among the threads
dynamic: loop iterations are divided into blocks of size
chunk, and dynamically scheduled among the threads
When a thread finishes one chunk, it is dynamically
assigned another. The default chunk size is 1


Example: Dot Product (1)
int
main()
{
double sum;
vector<double> a(10000000), b(10000000);
generate(a.begin(),a.end(),drand48);
generate(b.begin(),b.end(),drand48);
sum = 0;
for (int i=0; i<a.size(); ++i)
{
sum = sum + a[i]*b[i];
}
cout << sum << endl;
}


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// openmp03-dotp-openmp
int
main()
{
double sum;
vector<double> a(10000000), b(10000000);
generate(a.begin(),a.end(),drand48);
generate(b.begin(),b.end(),drand48);
sum = 0;
#pragma omp parallel for reduction(+: sum)
{
}
cout << sum << endl;
}


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// openmp03-dotp-openmp3
...
int ChunkSize = a.size()/omp_get_max_threads();
#pragma omp parallel
{
#pragma omp for schedule(dynamic, ChunkSize) reduction(+: sum)
{
}
}


Example: Varying Loads

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// openmp06
...
void f(vector<double> &a, vector<double> &b, int n)
{
int i, j;
#pragma omp parallel shared(n)
{
// schedule is dynamic of size 1 to manage the varying size of load
#pragma omp for schedule(dynamic,1) private (i,j) nowait
for (i = 1; i < n; i++)
for (j = 0; j < i; j++)
b[j + n*i] = (a[j + n*i] + a[j + n*(i-1)]) / 2.0;
}
}
//
//
//
//

time ./openmp04-seq 10000 (n is 10000^2)
11.20 real
5.68 user
0.68 sys
time ./openmp04 10000
4.55 real
13.07 user
0.62 sys

The sections Construct/Directive
#pragma omp sections [clause ...]
{
#pragma omp section
{
// execute A
}
#pragma omp section
{
// execute A
}
}

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private
reduction
nowait
etc.

Specifies that the enclosed section(s) of code are to be divided
among the threads in the team
Each section is executed once by a thread in the team. Different
sections may be executed by different threads. It is possible that for a
thread to execute more than one section.

Example: Dot Product (vectors init)
#pragma omp sections nowait
{
#pragma omp section
{
int ita;
#pragma omp parallel for shared(a) private(ita)
schedule(dynamic,ChunkSize)
for(ita=0; ita<a.size(); ++ita)
{
a[ita] = drand48();
}
}
#pragma omp section
{
int itb;
#pragma omp parallel for shared(b) private(itb)
schedule(dynamic,ChunkSize)
for(itb=0; itb<b.size(); ++itb)
{
b[itb] = drand48();
}
}
}


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The single Construct/Directive

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The single construct is associated with the structured
block of code immediately following it and specifies that
this block should be executed by one thread only
It does not state which thread should execute the code
block
The thread chosen could vary from one run to another. It
can also differ for different single constructs within one
application
#pragma omp single [private | nowait | …]
{
...
}

Example: Single Construct/Directive
#pragma omp parallel shared(a,b) private(i)
{
#pragma omp single
{
a = 10;
printf("Single construct executed by thread %dn",
omp_get_thread_num());
} /* A barrier is automatically inserted here */
#pragma omp for
for (i=0; i<n; i++)
b[i] = a;
} /*-- End of parallel region --*/


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Synchronization

The critical Construct/Directive

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#pragma omp critical [ name ]
{
…
}
The critical directive specifies a region of code that must be executed
by only one thread at a time
If a thread is currently executing inside a critical region and another
thread reaches that critical region and attempts to execute it, it will
block until the first thread exits that critical region
The optional name enables multiple different critical regions
Names act as global identifiers
Critical regions with the same name are treated as the same
region
All critical sections which are unnamed, are treated as the same
section

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Example: critical Construct/Directive

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sum = 0;
#pragma omp parallel shared(n,a,sum) private(TID,sumLocal)
{
TID = omp_get_thread_num();
double sumLocal = 0;
#pragma omp for
for (size_t i=0; i<n; i++)
sumLocal += a[i];
#pragma omp critical (update_sum)
{
sum += sumLocal;
cout << "TID=" << TID << "sumLocal="
<< sumLocal << " sum=" << sum << endl;
}
} /*-- End of parallel region --*/
cout << "Value of sum after parallel region: " << sum << endl;


The barrier Construct/Directive

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Syntax: #pragma omp barrier
The barrier directive synchronizes all threads in the team.
When a barrier directive is reached, a thread will wait at
that point until all other threads have reached that barrier.
All threads then resume executing in parallel the code that
follows the barrier.
All threads in a team (or none) must execute the barrier
region.


When to Use OpenMP?
Target platform is multicore or multiprocessor
Application is cross-platform
Parallelizable loops
Last-minute optimizations


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