OpenMP Exercise

Exercise 1

1. Login to the homework machine

   ssh username@homework.cs.tufts.edu

2. Copy the example files

   1. In your home directory, create a subdirectory for the example codes and then cd to it.

      mkdir openMP
      cd  openMP 

   2. Then, copy either the Fortran or the C version of the parallel OpenMP exercise files to your openMP subdirectory:

      cp  /comp/140/public_html/labs/lab7/samples/*  ~/openMP

3. List the contents of your openMP subdirectory

   You should notice the following files. Note: Most of these are simple example files. Their primary purpose is to demonstrate the basics of how to parallelize a code with OpenMP. Most execute in a second or two.

   C Files	Description
   omp_hello.c	Hello world
   omp_workshare1.c	Loop work-sharing
   omp_workshare2.c	Sections work-sharing
   omp_reduction.c	Combined parallel loop reduction
   omp_orphan.c	Orphaned parallel loop reduction
   omp_mm.c	Matrix multiply
   omp_getEnvInfo.c	Get and print environment information
   omp_bug1.c omp_bug1fix.c omp_bug2.c omp_bug3.c omp_bug4.c omp_bug4fix omp_bug5.c omp_bug5fix.c omp_bug6.c	Programs with bugs

4. Create, compile and run an OpenMP "Hello world" program

   1. Using your favorite text editor (vi/vim, emacs, nedit, gedit, nano...) open a new file - call it whatever you'd like.

   2. Create a simple OpenMP program that does the following:
      • Creates a parallel region
      • Has each thread in the parallel region obtain its thread id
      • Has each thread print "Hello World" along with its unique thread id
      • Has the master thread only, obtain and then print the total number of threads

      If you need help, see the provided omp_hello.c file.

   3. Using gcc, compile your hello world OpenMP program. This may take several attempts if there are any code errors. For example:

      
      gcc -fopenmp omp_hello.c -o hello

      When you get a clean compile, proceed.

   4. Run your hello executable and notice its output.
      • Is it what you expected? As a comparison, you can compile and run the provided omp_hello.c or omp_hello.f example program.
      • How many threads were created? By default, GNU compiler will create 1 thread for each core.

   5. Vary the number of threads and re-run Hello World
      1. Explicitly set the number of threads to use by means of the OMP_NUM_THREADS environment variable:

         export OMP_NUM_THREADS=8

      2. Your output should look something like below.

         Hello World from thread = 0 Hello World from thread = 3 Hello World from thread = 2 Number of threads = 8 Hello World from thread = 6 Hello World from thread = 1 Hello World from thread = 4 Hello World from thread = 7 Hello World from thread = 5

      3. Run your program several times and observe the order of print statements. Notice that the order of output is more or less random.

   This completes Exercise 1

   ---

   Exercise 2

5. Review / Compile / Run the workshare1 example code

   This example demonstrates use of the OpenMP loop work-sharing construct. Notice that it specifies dynamic scheduling of threads and assigns a specific number of iterations to be done by each thread.

   1. First, set the number of threads to 4:

      export OMP_NUM_THREADS=4

   2. After reviewing the source code, use your preferred compiler to compile and run the executable. For example:

      gcc -fopenmp omp_workshare1.c -o workshare1
      ./workshare1 | sort

   3. Review the output. Note that it is piped through the sort utility. This will make it easier to view how loop iterations were actually scheduled across the team of threads.

   4. Run the program a couple more times and review the output. What do you see? Typically, dynamic scheduling is not deterministic. Everytime you run the program, different threads can run different chunks of work. It is even possible that a thread might not do any work because another thread is quicker and takes more work. In fact, it might be possible for one thread to do all of the work.

   5. Edit the workshare1 source file and change the dynamic scheduling to static scheduling.

   6. Recompile and run the modified program. Notice the difference in output compared to dynamic scheduling. Specifically, notice that thread 0 gets the first chunk, thread 1 the second chunk, and so on.

   7. Run the program a couple more times. Does the output change? With static scheduling, the allocation of work is deterministic and should not change between runs, and every thread gets work to do.

   8. Reflect on possible performance differences between dynamic and static scheduling.

6. Review / Compile / Run the matrix multiply example code

   This example performs a matrix multiple by distributing the iterations of the operation between available threads.

   1. After reviewing the source code, compile and run the program. For example:

      gcc -fopenmp omp_mm.c -o matmult
      matmult

   2. Review the output. It shows which thread did each iteration and the final result matrix.

   3. Run the program again, however this time sort the output to clearly see which threads execute which iterations:

      matmult | sort | grep Thread

      Do the loop iterations match the SCHEDULE(STATIC,CHUNK) directive for the matrix multiple loop in the code?

7. Review / Compile / Run the workshare2 example code

   This example demonstrates use of the OpenMP SECTIONS work-sharing construct Note how the PARALLEL region is divided into separate sections, each of which will be executed by one thread.

   1. As before, compile and execute the program after reviewing it. For example:

      gcc -fopenmp omp_workshare2.c -o workshare2
      workshare2

   2. Run the program several times and observe any differences in output. Because there are only two sections, you should notice that some threads do not do any work. You may/may not notice that the threads doing work can vary. For example, the first time thread 0 and thread 1 may do the work, and the next time it may be thread 0 and thread 3. It is even possible for one thread to do all of the work. Which thread does work is non-deterministic in this case.