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ACENET Summer School - OpenMP

Hello World

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Overview

Teaching: 15 min
Exercises: 10 min
Questions

  • How do you compile and run an OpenMP program?

  • What are OpenMP pragmas?

Objectives

  • Compiling and running an OpenMP program

Since OpenMP is an extension to the compiler, you need to be able to tell the compiler when and where to add the code necessary to create and use threads for the parallel sections. This is handled through special statements called pragmas. To a compiler that doesn’t understand OpenMP, pragmas look like comments. The basic forms are:

C/C++

#pragma omp ...

FORTRAN

!$OMP ...

Hello World

How do we add in parallelism to the basic hello world program? The very first pragma that we will look at is the parallel pragma.

#include <stdio.h>
#include <stdlib.h>

#include <omp.h>

int main(int argc, char **argv) {
   #pragma omp parallel
   printf("Hello World\n");
}

To compile it, you’ll need to add an extra flag to tell the compiler to treat the source code as an OpenMP program.

gcc -fopenmp -o hello hello.c

If you prefer Intel compilers to GCC, use:

icc -qopenmp -o hello hello.c

When you run this program, you should see the output “Hello World” multiple times. But how many?

The standard says this is implementation dependent. But the usual default is, OpenMP will look at the machine that it is running on and see how many cores there are. It will then launch a thread for each core. You can control the number of threads, however, with environment variables. If you want only 3 threads, do the following:

export OMP_NUM_THREADS=3
./hello_world

Using multiple cores

Try running the hello world program with different numbers of threads. Can you use more threads than the cores on the machine?

OpenMP with Slurm

When you wish to submit an OpenMP job to the job scheduler Slurm, you can use the following boilerplate.

#SBATCH --account=acenet-wa
#SBATCH --reservation=acenet-wr_cpu
#SBATCH --time=0:15:0
#SBATCH --cpus-per-task=3
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
./hello_world

You could also ask for an interactive session with multiple cores like so:

salloc --account=acenet-wa --reservation=acenet-wr_cpu --cpus-per-task=4 --mem=12G --time=3:0:0

Identifying threads

How can you tell which thread is doing what? The OpenMP specification includes a number of functions that are made available through the included header file “omp.h”. One of them is the function “omp_get_thread_num()”, to get an ID of the thread running the code.

#include <stdio.h>
#include <stdlib.h>

#include <omp.h>

int main(int argc, char **argv) {
   int id;
   #pragma omp parallel
   {
   id = omp_get_thread_num();
   printf("Hello World from thread %d\n", id);
   }
}

Here, you will get each thread tagging their output with their unique ID, a number between 0 and NUM_THREADS-1.

Pragmas and code blocks

An OpenMP pragma applies to the following code block in C or C++. Code blocks are either a single line, or a series of lines wrapped by curly brackets. Because Fortran doesn’t have an analogous construction, many OpenMP pragmas in Fortran are paired with an “end” pragma, such as !$omp parallel end.

Thread ordering

What order do the threads write out their messages in? Try running the program a few times. What’s going on?

Solution

You should find that the messages are emitted in random order. This is an important rule of not only OpenMP programming, but parallel programming in general: Order of execution of parallel elements is not guaranteed.

Conditional compilation

We said earlier that you should be able to use the same code for both OpenMP and serial work. Try compiling the code without the -fopenmp flag. What happens? Can you figure out how to fix it?

Hint: The compiler defines a preprocessor variable _OPENMP

Solution


#include <stdio.h>
#include <stdlib.h>
#include <omp.h>

int main(int argc, char **argv) {
   int id = 0;
   #pragma omp parallel
   {
#ifdef _OPENMP
   id = omp_get_thread_num();
#else
   id = 0;
#endif
   printf("Hello World from thread %d\n", id);
   }
}

Execution model

It’s a good idea to have a mental model of what happens when OpenMP does something in parallel. When the program begins there is only one thread active, called the master thread. When the code reaches a point where parallel operations are required, the master thread creates (or activates) additional threads. We call this “forking”. The master and the created threads work concurrently for a while. Then at the end of the parallel code the created threads die or are suspended, and the flow of control returns to the master thread. We call this “joining”.

OpenMP-execution-model

OpenMP also divides the memory into two types: Global (or shared) memory, and thread-local memory. Every thread can read and write the global memory, but each thread also gets a little slice of memory that can only be read or written by that thread.

Key Points

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