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Microsoft PowerPoint – omp-hands-on-SC08 (2).ppt [Read-Only]

A “Hands-on” Introduction to
OpenMP*

Tim Mattson
Principal Engineer
Intel Corporation

timothy.g.mattson@intel.com

* The name “OpenMP” is the property of the OpenMP Architecture Review Board.

Larry Meadows
Principal Engineer
Intel Corporation

lawrence.f.meadows@intel.com

Preliminaries: part 1

Disclosures
The views expressed in this tutorial are those of the
people delivering the tutorial.

– We are not speaking for our employers.
– We are not speaking for the OpenMP ARB

This is a new tutorial for us:
Help us improve … tell us how you would make this
tutorial better.

Preliminaries: Part 2
Our plan for the day .. Active learning!

We will mix short lectures with short exercises.
You will use your laptop for the exercises … that
way you’ll have an OpenMP environment to take
home so you can keep learning on your own.

Please follow these simple rules
Do the exercises we assign and then change things
around and experiment.

– Embrace active learning!
Don’t cheat: Do Not look at the solutions before
you complete an exercise … even if you get really
frustrated.

Our Plan for the day

Tasks and other OpenMP 3
features

Linked listIX OpenMP 3 and tasks

Point to point synch with flushProducer
consumer

VIII. Memory model

For, schedules, sectionsLinked list,
matmul

VII. Worksharing and
schedule

Data environment details,
modular software,
threadprivate

Pi_mcVI. Data Environment

Single, master, runtime
libraries, environment
variables, synchronization, etc.

No exerciseV. Odds and ends

For, reductionPi_loopIV. Parallel loops

False sharing, critical, atomicPi_spmd_finalIII. Synchronization

Parallel, default data
environment, runtime library
calls

Pi_spmd_simpleII. Creating threads

Parallel regionsInstall sw,
hello_world

I. OMP Intro

conceptsExerciseTopic

Break

lunch

Outline

Introduction to OpenMP
Creating Threads
Synchronization
Parallel Loops
Synchronize single masters and stuff
Data environment
Schedule your for and sections
Memory model
OpenMP 3.0 and Tasks

OpenMP* Overview:

omp_set_lock(lck)

#pragma omp parallel for private(A, B)

#pragma omp critical

C$OMP parallel do shared(a, b, c)

C$OMP PARALLEL REDUCTION (+: A, B)

call OMP_INIT_LOCK (ilok)

call omp_test_lock(jlok)

setenv OMP_SCHEDULE “dynamic”

CALL OMP_SET_NUM_THREADS(10)

C$OMP DO lastprivate(XX)

C$OMP ORDERED

C$OMP SINGLE PRIVATE(X)

C$OMP SECTIONS

C$OMP MASTERC$OMP ATOMIC

C$OMP FLUSH

C$OMP PARALLEL DO ORDERED PRIVATE (A, B, C)

C$OMP THREADPRIVATE(/ABC/)

C$OMP PARALLEL COPYIN(/blk/)

Nthrds = OMP_GET_NUM_PROCS()

!$OMP BARRIER

OpenMP: An API for Writing Multithreaded
Applications

A set of compiler directives and library
routines for parallel application programmers
Greatly simplifies writing multi-threaded (MT)

programs in Fortran, C and C++
Standardizes last 20 years of SMP practice

* The name “OpenMP” is the property of the OpenMP Architecture Review Board.

OpenMP Basic Defs: Solution Stack

OpenMP Runtime library

OS/system support for shared memory and threading

S
ys

te
m

la
ye

Directives,
Compiler

OpenMP library Environment variablesPr
og

.
L a

y e
r

Application

End User

U
se

r
l a

y e
r

Shared Address Space

Proc3Proc2Proc1 ProcN

H
W

OpenMP core syntax
Most of the constructs in OpenMP are compiler
directives.

#pragma omp construct [clause [clause]…]
Example

#pragma omp parallel num_threads(4)
Function prototypes and types in the file:

#include
Most OpenMP* constructs apply to a
“structured block”.

Structured block: a block of one or more statements
with one point of entry at the top and one point of
exit at the bottom.
It’s OK to have an exit() within the structured block.

Exercise 1, Part A: Hello world
Verify that your environment works
Write a program that prints “hello world”.

void main()
{

int ID = 0;

printf(“ hello(%d) ”, ID);
printf(“ world(%d) \n”, ID);

}

void main()
{

int ID = 0;

printf(“ hello(%d) ”, ID);
printf(“ world(%d) \n”, ID);

}

Exercise 1, Part B: Hello world
Verify that your OpenMP environment works
Write a multithreaded program that prints “hello world”.

void main()
{

int ID = 0;

printf(“ hello(%d) ”, ID);
printf(“ world(%d) \n”, ID);

}

void main()
{

int ID = 0;

printf(“ hello(%d) ”, ID);
printf(“ world(%d) \n”, ID);

}

#pragma omp parallel

{

}

#include “omp.h”

Switches for compiling and linking

-fopenmp gcc

-mp pgi

/Qopenmp intel

Exercise 1: Solution
A multi-threaded “Hello world” program

Write a multithreaded program where each
thread prints “hello world”.

#include “omp.h”
void main()
{

#pragma omp parallel
{

int ID = omp_get_thread_num();
printf(“ hello(%d) ”, ID);
printf(“ world(%d) \n”, ID);

}
}

#include “omp.h”
void main()
{

#pragma omp parallel
{

int ID = omp_get_thread_num();
printf(“ hello(%d) ”, ID);
printf(“ world(%d) \n”, ID);

}
}

Sample Output:
hello(1) hello(0) world(1)

world(0)

hello (3) hello(2) world(3)

world(2)

Sample Output:
hello(1) hello(0) world(1)

world(0)

hello (3) hello(2) world(3)

world(2)

OpenMP include fileOpenMP include file

Parallel region with default
number of threads

Runtime library function to
return a thread ID.

Runtime library function to
return a thread ID.End of the Parallel regionEnd of the Parallel region

OpenMP Overview:
How do threads interact?
OpenMP is a multi-threading, shared address
model.

– Threads communicate by sharing variables.
Unintended sharing of data causes race
conditions:

– race condition: when the program’s outcome
changes as the threads are scheduled differently.

To control race conditions:
– Use synchronization to protect data conflicts.

Synchronization is expensive so:
– Change how data is accessed to minimize the need

for synchronization.

Outline

Introduction to OpenMP
Creating Threads
Synchronization
Parallel Loops
Synchronize single masters and stuff
Data environment
Schedule your for and sections
Memory model
OpenMP 3.0 and Tasks

OpenMP Programming Model:
Fork-Join Parallelism:

Master thread spawns a team of threads as needed.

Parallelism added incrementally until performance goals
are met: i.e. the sequential program evolves into a
parallel program.

Parallel Regions
Master
Thread
in red

A Nested
Parallel
region

Sequential Parts

Thread Creation: Parallel Regions

You create threads in OpenMP* with the parallel
construct.
For example, To create a 4 thread Parallel region:

double A[1000];
omp_set_num_threads(4);
#pragma omp parallel
{

int ID = omp_get_thread_num();
pooh(ID,A);

}

Each thread calls Each thread calls pooh(ID,A) for for ID = = 0 to to 3

Each thread
executes a
copy of the
code within

the
structured

block

Each thread
executes a
copy of the
code within

the
structured

block

Runtime function to
request a certain
number of threads

Runtime function
returning a thread ID

* The name “OpenMP” is the property of the OpenMP Architecture Review Board

Thread Creation: Parallel Regions
You create threads in OpenMP* with the parallel
construct.
For example, To create a 4 thread Parallel region:

double A[1000];

#pragma omp parallel num_threads(4)
{

int ID = omp_get_thread_num();
pooh(ID,A);

}

Each thread calls Each thread calls pooh(ID,A) for for ID = = 0 to to 3

Each thread
executes a
copy of the
code within

the
structured

block

Each thread
executes a
copy of the
code within

the
structured

block

clause to request a certain
number of threads

Runtime function
returning a thread ID

* The name “OpenMP” is the property of the OpenMP Architecture Review Board

Thread Creation: Parallel Regions example

Each thread executes the
same code redundantly.

double A[1000];
omp_set_num_threads(4);
#pragma omp parallel
{

int ID = omp_get_thread_num();
pooh(ID, A);

}
printf(“all done\n”);omp_set_num_threads(4)

pooh(1,A) pooh(2,A) pooh(3,A)

printf(“all done\n”);

pooh(0,A)

double A[1000];

A single
copy of A
is shared
between all
threads.

Threads wait here for all threads to
finish before proceeding (i.e. a barrier)

* The name “OpenMP” is the property of the OpenMP Architecture Review Board

Exercises 2 to 4:
Numerical Integration

∫
4.0

(1+x2) dx = π
0

∑ F(xi)Δx ≈ π
i = 0

Mathematically, we know that:

We can approximate the
integral as a sum of
rectangles:

Where each rectangle has
width Δx and height F(xi) at
the middle of interval i.

F(
x)

=
4

.0
/(1

+x
2 )

4.0

2.0

1.0
X0.0

Exercises 2 to 4: Serial PI Program

static long num_steps = 100000;
double step;
void main ()
{ int i; double x, pi, sum = 0.0;

step = 1.0/(double) num_steps;

for (i=0;i< num_steps; i++){ x = (i+0.5)*step; sum = sum + 4.0/(1.0+x*x); } pi = step * sum; }} 20 Exercise 2 Create a parallel version of the pi program using a parallel construct. Pay close attention to shared versus private variables. In addition to a parallel construct, you will need the runtime library routines int omp_get_num_threads(); int omp_get_thread_num(); double omp_get_wtime(); Time in Seconds since a fixed point in the past Thread ID or rank Number of threads in the team 21 Outline Introduction to OpenMP Creating Threads Synchronization Parallel Loops Synchronize single masters and stuff Data environment Schedule your for and sections Memory model OpenMP 3.0 and Tasks 22 Discussed later Synchronization High level synchronization: – critical – atomic – barrier – ordered Low level synchronization – flush – locks (both simple and nested) Synchronization is used to impose order constraints and to protect access to shared data 23 Synchronization: critical Mutual exclusion: Only one thread at a time can enter a critical region. float res; #pragma omp parallel { float B; int i, id, nthrds; id = omp_get_thread_num(); nthrds = omp_get_num_threads(); for(i=id;i
void main()
{ int num_threads;

omp_set_dynamic( 0 );
omp_set_num_threads( omp_num_procs() );

#pragma omp parallel
{ int id=omp_get_thread_num();

#pragma omp single
num_threads = omp_get_num_threads();

do_lots_of_stuff(id);
}

}

Protect this op since Memory
stores are not atomic

Request as many threads as
you have processors.

Disable dynamic adjustment of the
number of threads.

Even in this case, the system may give you fewer
threads than requested. If the precise # of threads
matters, test for it and respond accordingly.

Environment Variables

Set the default number of threads to use.
– OMP_NUM_THREADS int_literal

Control how “omp for schedule(RUNTIME)”
loop iterations are scheduled.

– OMP_SCHEDULE “schedule[, chunk_size]”

… Plus several less commonly used environment variables.

Outline

Introduction to OpenMP
Creating Threads
Synchronization
Parallel Loops
Synchronize single masters and stuff
Data environment
Schedule your for and sections
Memory model
OpenMP 3.0 and Tasks

Data environment:
Default storage attributes

Shared Memory programming model:
– Most variables are shared by default

Global variables are SHARED among threads
– Fortran: COMMON blocks, SAVE variables, MODULE

variables
– C: File scope variables, static
– Both: dynamically allocated memory (ALLOCATE, malloc, new)

But not everything is shared…
– Stack variables in subprograms(Fortran) or functions(C) called

from parallel regions are PRIVATE
– Automatic variables within a statement block are PRIVATE.

double A[10];
int main() {
int index[10];
#pragma omp parallel

work(index);
printf(“%d\n”, index[0]);

}

extern double A[10];
void work(int *index) {
double temp[10];
static int count;
…

}

Data sharing: Examples

temp

A, index, count

temp temp

A, index, count

A, index and count are
shared by all threads.

temp is local to each
thread

A, index and count are
shared by all threads.

temp is local to each
thread

* Third party trademarks and names are the property of their respective owner.

Data sharing:
Changing storage attributes

One can selectively change storage attributes for
constructs using the following clauses*

– SHARED
– PRIVATE
– FIRSTPRIVATE

The final value of a private inside a parallel loop can be
transmitted to the shared variable outside the loop with:

– LASTPRIVATE
The default attributes can be overridden with:

– DEFAULT (PRIVATE | SHARED | NONE)

All the clauses on this page
apply to the OpenMP construct
NOT to the entire region.

All data clauses apply to parallel constructs and worksharing constructs except
“shared” which only applies to parallel constructs.

DEFAULT(PRIVATE) is Fortran only

Data Sharing: Private Clause

void wrong() {
int tmp = 0;

#pragma omp for private(tmp)
for (int j = 0; j < 1000; ++j) tmp += j; printf(“%d\n”, tmp); } private(var) creates a new local copy of var for each thread. – The value is uninitialized – In OpenMP 2.5 the value of the shared variable is undefined after the region tmp was not initialized tmp was not initialized tmp: 0 in 3.0, unspecified in 2.5 tmp: 0 in 3.0, unspecified in 2.5 51 Data Sharing: Private Clause When is the original variable valid? int tmp; void danger() { tmp = 0; #pragma omp parallel private(tmp) work(); printf(“%d\n”, tmp); } The original variable’s value is unspecified in OpenMP 2.5. In OpenMP 3.0, if it is referenced outside of the construct – Implementations may reference the original variable or a copy ….. A dangerous programming practice! extern int tmp; void work() { tmp = 5; } unspecified which copy of tmp unspecified which copy of tmptmp has unspecified value tmp has unspecified value 52 Data Sharing: Firstprivate Clause Firstprivate is a special case of private. – Initializes each private copy with the corresponding value from the master thread. tmp: 0 in 3.0, unspecified in 2.5tmp: 0 in 3.0, unspecified in 2.5 void useless() { int tmp = 0; #pragma omp for firstprivate(tmp) for (int j = 0; j < 1000; ++j) tmp += j; printf(“%d\n”, tmp); } Each thread gets its own tmp with an initial value of 0 Each thread gets its own tmp with an initial value of 0 53 Data sharing: Lastprivate Clause Lastprivate passes the value of a private from the last iteration to a global variable. tmp is defined as its value at the “last sequential” iteration (i.e., for j=999) tmp is defined as its value at the “last sequential” iteration (i.e., for j=999) void closer() { int tmp = 0; #pragma omp parallel for firstprivate(tmp) \ lastprivate(tmp) for (int j = 0; j < 1000; ++j) tmp += j; printf(“%d\n”, tmp); } Each thread gets its own tmp with an initial value of 0 Each thread gets its own tmp with an initial value of 0 54 Data Sharing: A data environment test Consider this example of PRIVATE and FIRSTPRIVATE Are A,B,C local to each thread or shared inside the parallel region? What are their initial values inside and values after the parallel region? variables A,B, and C = 1 #pragma omp parallel private(B) firstprivate(C) Inside this parallel region ... “A” is shared by all threads; equals 1 “B” and “C” are local to each thread. – B’s initial value is undefined – C’s initial value equals 1 Outside this parallel region ... The values of “B” and “C” are unspecified in OpenMP 2.5, and in OpenMP 3.0 if referenced in the region but outside the construct. 55 Data Sharing: Default Clause Note that the default storage attribute is DEFAULT(SHARED) (so no need to use it) Exception: #pragma omp task To change default: DEFAULT(PRIVATE) each variable in the construct is made private as if specified in a private clause mostly saves typing DEFAULT(NONE): no default for variables in static extent. Must list storage attribute for each variable in static extent. Good programming practice! Only the Fortran API supports default(private). C/C++ only has default(shared) or default(none). 56 Data Sharing: Default Clause Example itotal = 1000 C$OMP PARALLEL DEFAULT(PRIVATE) SHARED(itotal) np = omp_get_num_threads() each = itotal/np ……… C$OMP END PARALLEL itotal = 1000 C$OMP PARALLEL PRIVATE(np, each) np = omp_get_num_threads() each = itotal/np ……… C$OMP END PARALLEL These two code fragments are equivalent 57 Data Sharing: tasks (OpenMP 3.0) The default for tasks is usually firstprivate, because the task may not be executed until later (and variables may have gone out of scope). Variables that are shared in all constructs starting from the innermost enclosing parallel construct are shared, because the barrier guarantees task completion. #pragma omp parallel shared(A) private(B) { ... #pragma omp task { int C; compute(A, B, C); } } A is shared B is firstprivate C is private 3.0 58 Data sharing: Threadprivate Makes global data private to a thread Fortran: COMMON blocks C: File scope and static variables, static class members Different from making them PRIVATE with PRIVATE global variables are masked. THREADPRIVATE preserves global scope within each thread Threadprivate variables can be initialized using COPYIN or at time of definition (using language- defined initialization capabilities). 59 A threadprivate example (C) int counter = 0; #pragma omp threadprivate(counter) int increment_counter() { counter++; return (counter); } int counter = 0; #pragma omp threadprivate(counter) int increment_counter() { counter++; return (counter); } Use threadprivate to create a counter for each thread. 60 Data Copying: Copyin parameter (N=1000) common/buf/A(N) !$OMP THREADPRIVATE(/buf/) C Initialize the A array call init_data(N,A) !$OMP PARALLEL COPYIN(A) … Now each thread sees threadprivate array A initialied … to the global value set in the subroutine init_data() !$OMP END PARALLEL end parameter (N=1000) common/buf/A(N) !$OMP THREADPRIVATE(/buf/) C Initialize the A array call init_data(N,A) !$OMP PARALLEL COPYIN(A) … Now each thread sees threadprivate array A initialied … to the global value set in the subroutine init_data() !$OMP END PARALLEL end You initialize threadprivate data using a copyin clause. 61 Data Copying: Copyprivate #include
void input_parameters (int, int); // fetch values of input parameters
void do_work(int, int);

void main()
{

int Nsize, choice;

#pragma omp parallel private (Nsize, choice)
{

#pragma omp single copyprivate (Nsize, choice)
input_parameters (Nsize, choice);

do_work(Nsize, choice);
}

}

#include
void input_parameters (int, int); // fetch values of input parameters
void do_work(int, int);

void main()
{

int Nsize, choice;

#pragma omp parallel private (Nsize, choice)
{

#pragma omp single copyprivate (Nsize, choice)
input_parameters (Nsize, choice);

do_work(Nsize, choice);
}

}

Used with a single region to broadcast values of privates
from one member of a team to the rest of the team.

Exercise 5: Monte Carlo Calculations
Using Random numbers to solve tough problems

Sample a problem domain to estimate areas, compute
probabilities, find optimal values, etc.
Example: Computing π with a digital dart board:

Throw darts at the circle/square.
Chance of falling in circle is
proportional to ratio of areas:

Ac = r2 * π
As = (2*r) * (2*r) = 4 * r2
P = Ac/As = π /4

Compute π by randomly choosing
points, count the fraction that falls in
the circle, compute pi.

2 * r

N= 10 π = 2.8

N=100 π = 3.16

N= 1000 π = 3.148

N= 10 π = 2.8

N=100 π = 3.16

N= 1000 π = 3.148

Exercise 5
We provide three files for this exercise

pi_mc.c: the monte carlo method pi program
random.c: a simple random number generator
random.h: include file for random number generator

Create a parallel version of this program without
changing the interfaces to functions in random.c

This is an exercise in modular software … why should a user
of your parallel random number generator have to know any
details of the generator or make any changes to how the
generator is called?

Extra Credit:
Make the random number generator threadsafe.
Make your random number generator numerically correct (non-
overlapping sequences of pseudo-random numbers).

Outline

Introduction to OpenMP
Creating Threads
Synchronization
Parallel Loops
Synchronize single masters and stuff
Data environment
Schedule your for and sections
Memory model
OpenMP 3.0 and Tasks

Sections worksharing Construct
The Sections worksharing construct gives a
different structured block to each thread.

#pragma omp parallel
{

#pragma omp sections
{
#pragma omp section

X_calculation();
#pragma omp section

y_calculation();
#pragma omp section

z_calculation();
}

}

#pragma omp parallel
{

#pragma omp sections
{
#pragma omp section

X_calculation();
#pragma omp section

y_calculation();
#pragma omp section

z_calculation();
}

}

By default, there is a barrier at the end of the “omp
sections”. Use the “nowait” clause to turn off the barrier.

loop worksharing constructs:
The schedule clause
The schedule clause affects how loop iterations are
mapped onto threads

schedule(static [,chunk])
– Deal-out blocks of iterations of size “chunk” to each thread.

schedule(dynamic[,chunk])
– Each thread grabs “chunk” iterations off a queue until all

iterations have been handled.

schedule(guided[,chunk])
– Threads dynamically grab blocks of iterations. The size of the

block starts large and shrinks down to size “chunk” as the
calculation proceeds.

schedule(runtime)
– Schedule and chunk size taken from the OMP_SCHEDULE

environment variable (or the runtime library … for OpenMP 3.0).

Special case of dynamic
to reduce scheduling
overhead

GUIDED

Unpredictable, highly
variable work per
iteration

DYNAMIC

Pre-determined and
predictable by the
programmer

STATIC

When To UseSchedule Clause

loop work-sharing constructs:
The schedule clauseThe schedule clause

Least work at
runtime :
scheduling
done at
compile-time

Most work at
runtime :
complex
scheduling
logic used at
run-time

Exercise 6: hard

Consider the program linked.c
Traverses a linked list computing a sequence of
Fibonacci numbers at each node.

Parallelize this program using constructs
defined in OpenMP 2.5 (loop worksharing
constructs).
Once you have a correct program, optimize it.

Exercise 6: easy

Parallelize the matrix multiplication program in
the file matmul.c
Can you optimize the program by playing with
how the loops are scheduled?

Outline

Introduction to OpenMP
Creating Threads
Synchronization
Parallel Loops
Synchronize single masters and stuff
Data environment
Schedule your for and sections
Memory model
OpenMP 3.0 and Tasks

OpenMP memory model

proc1 proc2 proc3 procN

Shared memory

cache1 cache2 cache3 cacheN

. . .

A memory model is defined in terms of:
Coherence: Behavior of the memory system when a single
address is accessed by multiple threads.
Consistency: Orderings of accesses to different addresses by
multiple threads.

OpenMP supports a shared memory model.
All threads share an address space, but it can get complicated:

Source code

Program order

memory
a b

Commit order

private view

thread thread

private view
threadprivatethreadprivatea ab b

Wa Wb Ra Rb . . .

OpenMP Memory Model: Basic Terms

compiler

Executable code

Code order

Wb Rb Wa Ra . . .

RW’s in any
semantically

equivalent order

Consistency: Memory Access Re-ordering

Re-ordering:
Compiler re-orders program order to the code order
Machine re-orders code order to the memory
commit order

At a given point in time, the temporary view of
memory may vary from shared memory.
Consistency models based on orderings of
Reads (R), Writes (W) and Synchronizations (S):

R→R, W→W, R→W, R→S, S→S, W→S

Consistency

Sequential Consistency:
In a multi-processor, ops (R, W, S) are sequentially
consistent if:

– They remain in program order for each
processor.

– They are seen to be in the same overall order by
each of the other processors.

Program order = code order = commit order
Relaxed consistency:

Remove some of the ordering constraints for
memory ops (R, W, S).

OpenMP and Relaxed Consistency

OpenMP defines consistency as a variant of
weak consistency:

S ops must be in sequential order across threads.
Can not reorder S ops with R or W ops on the same
thread

– Weak consistency guarantees
S→W, S→R , R→S, W→S, S→S

The Synchronization operation relevant to this
discussion is flush.

Flush
Defines a sequence point at which a thread is
guaranteed to see a consistent view of memory with
respect to the “flush set”.
The flush set is:

“all thread visible variables” for a flush construct without an
argument list.
a list of variables when the “flush(list)” construct is used.

The action of Flush is to guarantee that:
– All R,W ops that overlap the flush set and occur prior to the

flush complete before the flush executes
– All R,W ops that overlap the flush set and occur after the

flush don’t execute until after the flush.
– Flushes with overlapping flush sets can not be reordered.

Memory ops: R = Read, W = write, S = synchronization

Synchronization: flush example
Flush forces data to be updated in memory so other
threads see the most recent value

double A;

A = compute();

flush(A); // flush to memory to make sure other
// threads can pick up the right value

Note: OpenMP’s flush is analogous to a fence in
other shared memory API’s.

Exercise 7: producer consumer

Parallelize the “prod_cons.c” program.
This is a well known pattern called the
producer consumer pattern

One thread produces values that another thread
consumes.
Often used with a stream of produced values to
implement “pipeline parallelism”

The key is to implement pairwise
synchronization between threads.

Exercise 7: prod_cons.c
int main()
{
double *A, sum, runtime; int flag = 0;

A = (double *)malloc(N*sizeof(double));

runtime = omp_get_wtime();

fill_rand(N, A); // Producer: fill an array of data

sum = Sum_array(N, A); // Consumer: sum the array

runtime = omp_get_wtime() – runtime;

printf(” In %lf seconds, The sum is %lf \n”,runtime,sum);
}

I need to put the
prod/cons pair

inside a loop so its
true pipeline
parallelism.

What is the Big Deal with Flush?
Compilers routinely reorder instructions implementing
a program

This helps better exploit the functional units, keep machine
busy, hide memory latencies, etc.

Compiler generally cannot move instructions:
past a barrier
past a flush on all variables

But it can move them past a flush with a list of
variables so long as those variables are not accessed
Keeping track of consistency when flushes are used
can be confusing … especially if “flush(list)” is used.

Note: the flush operation does not actually synchronize
different threads. It just ensures that a thread’s values

are made consistent with main memory.

Outline

Introduction to OpenMP
Creating Threads
Synchronization
Parallel Loops
Synchronize single masters and stuff
Data environment
Schedule your for and sections
Memory model
OpenMP 3.0 and Tasks

OpenMP pre-history

OpenMP based upon SMP directive
standardization efforts PCF and aborted ANSI
X3H5 – late 80’s

Nobody fully implemented either standard
Only a couple of partial implementations

Vendors considered proprietary API’s to be a
competitive feature:

Every vendor had proprietary directives sets
Even KAP, a “portable” multi-platform parallelization
tool used different directives on each platform

PCF – Parallel computing forum KAP – parallelization tool from KAI.

History of OpenMP

SGI

Cray

Merged,
needed
commonality
across
products

KAI ISV – needed
larger market

was tired of
recoding for
SMPs. Urged
vendors to
standardize.

ASCI

Wrote a
rough draft
straw man
SMP API

DEC

IBM

Intel

Other vendors
invited to join

1997

OpenMP Release History

OpenMP
Fortran 1.1
OpenMP

Fortran 1.1

OpenMP
C/C++ 1.0
OpenMP

C/C++ 1.0

OpenMP
Fortran 2.0
OpenMP

Fortran 2.0

OpenMP
C/C++ 2.0
OpenMP

C/C++ 2.0

1998

20001999

2002

OpenMP
Fortran 1.0
OpenMP

Fortran 1.0

1997

OpenMP
2.5

2005

A single
specification
for Fortran, C
and C++

OpenMP
3.0

tasking,
other new
features

2008

Tasks

Adding tasking is the biggest addition for 3.0

Worked on by a separate subcommittee
led by Jay Hoeflinger at Intel

Re-examined issue from ground up
quite different from Intel taskq’s

3.0

General task characteristics

A task has
Code to execute
A data environment (it owns its data)
An assigned thread that executes the code and
uses the data

Two activities: packaging and execution
Each encountering thread packages a new instance
of a task (code and data)
Some thread in the team executes the task at some
later time

3.0

Definitions
Task construct – task directive plus structured
block
Task – the package of code and instructions
for allocating data created when a thread
encounters a task construct
Task region – the dynamic sequence of
instructions produced by the execution of a
task by a thread

3.0

Tasks and OpenMP
Tasks have been fully integrated into OpenMP
Key concept: OpenMP has always had tasks, we just
never called them that.

Thread encountering parallel construct packages
up a set of implicit tasks, one per thread.
Team of threads is created.
Each thread in team is assigned to one of the tasks
(and tied to it).
Barrier holds original master thread until all implicit
tasks are finished.

We have simply added a way to create a task explicitly
for the team to execute.
Every part of an OpenMP program is part of one task or
another!

3.0

task Construct

#pragma omp task [clause[[,]clause] …]
structured-block

if (expression)
untied
shared (list)
private (list)
firstprivate (list)
default( shared | none )

where clause can be one of:

3.0

The if clause

When the if clause argument is false
The task is executed immediately by the encountering
thread.
The data environment is still local to the new task…
…and it’s still a different task with respect to
synchronization.

It’s a user directed optimization
when the cost of deferring the task is too great
compared to the cost of executing the task code
to control cache and memory affinity

3.0

When/where are tasks complete?

At thread barriers, explicit or implicit
applies to all tasks generated in the current parallel
region up to the barrier
matches user expectation

At task barriers
i.e. Wait until all tasks defined in the current task have
completed.
#pragma omp taskwait

Note: applies only to tasks generated in the current task,
not to “descendants” .

3.0

Example – parallel pointer chasing
using tasks

#pragma omp parallel
{

#pragma omp single private(p)
{
p = listhead ;
while (p) {

#pragma omp task
process (p)

p=next (p) ;
}

}
}

p is firstprivate inside
this task

3.0

Example – parallel pointer chasing on
multiple lists using tasks

#pragma omp parallel
{

#pragma omp for private(p)
for ( int i =0; i left)

#pragma omp task
postorder(p->left);

if (p->right)
#pragma omp task

postorder(p->right);
#pragma omp taskwait // wait for descendants

process(p->data);
}

Parent task suspended until children tasks complete

Task scheduling point

3.0

Task switching

Certain constructs have task scheduling points
at defined locations within them
When a thread encounters a task scheduling
point, it is allowed to suspend the current task
and execute another (called task switching)
It can then return to the original task and
resume

3.0

Task switching example

#pragma omp single
{

for (i=0; i
static long num_steps = 100000; double step;
#define NUM_THREADS 2
void main ()
{ int i, nthreads; double pi, sum[NUM_THREADS];

step = 1.0/(double) num_steps;
omp_set_num_threads(NUM_THREADS);

#pragma omp parallel
{

int i, id,nthrds;
double x;
id = omp_get_thread_num();
nthrds = omp_get_num_threads();
if (id == 0) nthreads = nthrds;
for (i=id, sum[id]=0.0;i< num_steps; i=i+nthrds) { x = (i+0.5)*step; sum[id] += 4.0/(1.0+x*x); } } for(i=0, pi=0.0;i
static long num_steps = 100000; double step;
#define NUM_THREADS 2
void main ()
{ double pi; step = 1.0/(double) num_steps;

omp_set_num_threads(NUM_THREADS);
#pragma omp parallel
{

int i, id,nthrds; double x, sum;
id = omp_get_thread_num();
nthrds = omp_get_num_threads();
if (id == 0) nthreads = nthrds;
id = omp_get_thread_num();
nthrds = omp_get_num_threads();
for (i=id, sum=0.0;i< num_steps; i=i+nthreads){ x = (i+0.5)*step; sum += 4.0/(1.0+x*x); } #pragma omp critical pi += sum * step; } } Exercise 3: SPMD Pi without false sharing Sum goes “out of scope” beyond the parallel region … so you must sum it in here. Must protect summation into pi in a critical region so updates don’t conflict Sum goes “out of scope” beyond the parallel region … so you must sum it in here. Must protect summation into pi in a critical region so updates don’t conflict No array, so no false sharing. No array, so no false sharing. Create a scalar local to each thread to accumulate partial sums. Create a scalar local to each thread to accumulate partial sums. 122 Appendix: Solutions to exercises Exercise 1: hello world Exercise 2: Simple SPMD Pi program Exercise 3: SPMD Pi without false sharing Exercise 4: Loop level Pi Exercise 5: Producer-consumer Exercise 6: Monte Carlo Pi and random numbers Exercise 7: hard, linked lists without tasks Exercise 7: easy, matrix multiplication Exercise 8: linked lists with tasks 123 Exercise 4: solution #include
static long num_steps = 100000; double step;
#define NUM_THREADS 2
void main ()
{ int i; double x, pi, sum = 0.0;

step = 1.0/(double) num_steps;
omp_set_num_threads(NUM_THREADS);

#pragma omp parallel for private(x) reduction(+:sum)
for (i=0;i< num_steps; i++){ x = (i+0.5)*step; sum = sum + 4.0/(1.0+x*x); } pi = step * sum; } Note: we created a parallel program without changing any code and by adding 4 simple lines! Note: we created a parallel program without changing any code and by adding 4 simple lines! i private by default i private by default For good OpenMP implementations, reduction is more scalable than critical. For good OpenMP implementations, reduction is more scalable than critical. 124 Appendix: Solutions to exercises Exercise 1: hello world Exercise 2: Simple SPMD Pi program Exercise 3: SPMD Pi without false sharing Exercise 4: Loop level Pi Exercise 5: Monte Carlo Pi and random numbers Exercise 6: hard, linked lists without tasks Exercise 6: easy, matrix multiplication Exercise 7: Producer-consumer Exercise 8: linked lists with tasks 125 Computers and random numbers We use “dice” to make random numbers: Given previous values, you cannot predict the next value. There are no patterns in the series … and it goes on forever. Computers are deterministic machines … set an initial state, run a sequence of predefined instructions, and you get a deterministic answer By design, computers are not random and cannot produce random numbers. However, with some very clever programming, we can make “pseudo random” numbers that are as random as you need them to be … but only if you are very careful. Why do I care? Random numbers drive statistical methods used in countless applications: Sample a large space of alternatives to find statistically good answers (Monte Carlo methods). 126 Monte Carlo Calculations: Using Random numbers to solve tough problems Sample a problem domain to estimate areas, compute probabilities, find optimal values, etc. Example: Computing π with a digital dart board: Throw darts at the circle/square. Chance of falling in circle is proportional to ratio of areas: Ac = r2 * π As = (2*r) * (2*r) = 4 * r2 P = Ac/As = π /4 Compute π by randomly choosing points, count the fraction that falls in the circle, compute pi. 2 * r N= 10 π = 2.8 N=100 π = 3.16 N= 1000 π = 3.148 N= 10 π = 2.8 N=100 π = 3.16 N= 1000 π = 3.148 127 Parallel Programmers love Monte Carlo algorithms #include “omp.h” static long num_trials = 10000; int main () { long i; long Ncirc = 0; double pi, x, y; double r = 1.0; // radius of circle. Side of squrare is 2*r seed(0,-r, r); // The circle and square are centered at the origin #pragma omp parallel for private (x, y) reduction (+:Ncirc) for(i=0;inext;
count++;

}
p = head;
for(i=0; inext;

}
#pragma omp parallel
{

#pragma omp for schedule(static,1)
for(i=0; i nodelist;
for (p = head; p != NULL; p = p->next)

nodelist.push_back(p);

int j = (int)nodelist.size();
#pragma omp parallel for schedule(static,1)

for (int i = 0; i < j; ++i) processwork(nodelist[i]); 47 seconds 37 seconds C++, default sched. 28 seconds32 secondsTwo Threads 45 seconds49 secondsOne Thread C, (static,1)C++, (static,1) Copy pointer to each node into an array Count number of items in the linked list Process nodes in parallel with a for loop Results on an Intel dual core 1.83 GHz CPU, Intel IA-32 compiler 10.1 build 2 143 Appendix: Solutions to exercises Exercise 1: hello world Exercise 2: Simple SPMD Pi program Exercise 3: SPMD Pi without false sharing Exercise 4: Loop level Pi Exercise 5: Monte Carlo Pi and random numbers Exercise 6: hard, linked lists without tasks Exercise 6: easy, matrix multiplication Exercise 7: Producer-consumer Exercise 8: linked lists with tasks 144 Matrix multiplication #pragma omp parallel for private(tmp, i, j, k) for (i=0; inext;
}

}
}

30 seconds28 secondsTwo Threads
48 seconds45 secondsOne Thread
Intel taskqArray, Static, 1

Results on an Intel dual core 1.83 GHz CPU, Intel IA-32 compiler 10.1 build 2

150

Linked lists with tasks (OpenMP 3)
See the file Linked_intel_taskq.c

#pragma omp parallel
{

#pragma omp single
{

p=head;
while (p) {

#pragma omp task firstprivate(p)
processwork(p);

p = p->next;
}

}
}

Creates a task with
its own copy of “p”

initialized to the
value of “p” when
the task is defined

151

Compiler notes: Intel on Windows
Intel compiler:

Launch SW dev environment … on my laptop I use:
– start/intel software development tools/intel C++

compiler 10.1/C+ build environment for 32 bit
apps

cd to the directory that holds your source code
Build software for program foo.c

– icl /Qopenmp foo.c
Set number of threads environment variable

– set OMP_NUM_THREADS=4
Run your program

– foo.exe To get rid of the pwd on the
prompt, type

prompt = %

152

Compiler notes: Visual Studio
Start “new project”
Select win 32 console project

Set name and path
On the next panel, Click “next” instead of finish so you can
select an empty project on the following panel.
Drag and drop your source file into the source folder on the
visual studio solution explorer
Activate OpenMP

– Go to project properties/configuration
properties/C.C++/language … and activate OpenMP

Set number of threads inside the program
Build the project
Run “without debug” from the debug menu.

153

Notes from the SC08 tutorial
It seemed to go very well. We had over 50 people who stuck it out
throughout the day.
Most people brought their laptops (only 7 loaner laptops were used). And
of those with laptops, most had preloaded an OS.
The chaos at the beginning was much less than I expected. I had fears of
an hour or longer to get everyone setup. But thanks to PGI providing a
license key in a temp file, we were able to get everyone installed in short
order.
Having a team of 4 (two speakers and two assistants) worked well. It
would have been messier without a hardcore compiler person such as
Larry. With dozens of different configurations, he had to do some serious
trouble-shooting to get the most difficult cases up and running.
The exercises used early in the course were good. The ones after lunch
were too hard. I need to refine the exercise set. One idea is to for each
slot have an “easy” exercise and a “hard” exercise. This will help me
keep the group’s experience more balanced.
Most people didn’t get the threadprivate exercise. The few people who
tried the linked-list exercise were amazingly creative … one even gettting
a single/nowait version to work.

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