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Assignment 2 Area of the Mandelbrot Set

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CPSC 424/524
Assignment 2

Area of the Mandelbrot Set
The Problem: The Mandelbrot Set is the set of complex numbers c for which the iteration
z ¬ z
2 + c does not diverge, starting from the initial condition z = c. To determine (approximately)
whether a point c lies in the set, a finite number of iterations are performed, and if the threshold condition
|z| 2 is satisfied at any iteration, then the point is considered to be outside the Mandelbrot Set. The
problem in this assignment is to estimate the area of the Mandelbrot Set. There is no known theoretical
value for this, but many estimates have been based on a procedure similar to the one described here.
The method used in this assignment is rather simple:
(a) Generate a grid of equal-size square cells covering a rectangular region R in the complex plane
that contains the upper half of the Mandelbrot Set. (The Set is symmetric with respect to the real
axis, so it is only necessary to work with half of it.) For this assignment, we will use the region R
with lower-left corner –2.0+0.0i and upper-right corner 0.5+1.25i. Each cell will be a square with
side length 0.001.
(b) Carry out the iteration above using as c, in turn, a randomly selected point in each cell. For this
assignment, compute up to 20,000 iterations for each c. If the threshold condition |z| 2 is
satisfied for any iterate, then terminate the iteration and mark the cell as outside the Set.
Otherwise, mark the cell as inside the Set. Let NI and NO denote, respectively, the total numbers
of cells determined to be inside and outside of the Mandelbrot Set.
(c) To estimate the area of the Mandelbrot Set, use
� = 2 ×
�&
(�& + �))
× Area of R
Task 1: Serial Program (20 points)
Create a serial program that estimates the area of the Mandelbrot set using the method described above. I
have provided a Makefile template for use in building this program and the OpenMP versions described
below. Feel free to change the program names if you wish.
For this assignment, I would like you to use a simple linear congruential pseudorandom number generator
that I have provided. (Note: I do not recommend this choice for “real” computational work, since there
are many better random number generators available in libraries. However, it is pedagogically useful for
this assignment.) I have provided the random number generator in the file drand.c in the directory
/home/fas/cpsc424/ahs3/assignment2. For this assignment, please initialize the random number
generator by calling dsrand(12345) to set an initial seed (which will actually be 12344).
Once you have created your serial program, run it several times to verify that it consistently produces the
same answer and to measure its performance. (As in Assignment 1, you may use the timing routines in
/home/fas/cpsc424/ahs3/utils/timing.) When I ran my serial program, I found the answer was
around 1.506666, and the elapsed time was around 66.8 seconds. Your results could vary somewhat
depending on the order in which you process the grid cells. (You don’t need to address this, but think
about why it might be true.)
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Task 2: OpenMP Program (Loop Directives) (40 points)
In this task, you will use OpenMP loop directives (pragmas) to create parallel, multithreaded versions of
your program.
1. Modify your program to create parallel threads using the “omp for” pragma without using either
a collapse clause or a schedule clause. When you have created your program, run it several
times using 2 threads. (To control the number of OpenMP threads, set the environment variable
OMP_NUM_THREADS . For example, to set the number of threads to 2, use the bash shell command
export OMP_NUM_THREADS=2.) Do your answers all agree with each other? If not, check/fix
the random number generator and/or the serial code to ensure that everything is thread safe. Once
you’re sure that is the case, then rerun the code several times to convince yourself that you’ve
fixed the problem. Note: Depending on how you correct the thread safety problem, you may still
see some slight differences as you vary the number of threads, or if you don’t use a static
assignment of iterations to threads.
a. Now run your corrected code for 1, 2, 4, and 8 threads. In your report, create a table
containing average times and areas for these cases and also for the serial version from
Task 1. Note: In a separate window, you can ssh to the compute node you’re using and
then run the top command to verify the number of threads that are actually running.
2. By default for parallel loops, OpenMP uses static scheduling in which the total number of
iterations is divided into OMP_NUM_THREADS contiguous blocks (sets of iterations), and each
block is assigned to a single thread. Modify your code to try alternative schedule options and
report average timings for each case you try using 2, 4, and 8 threads. At least, try the following:
a. schedule(static,1)
b. schedule(static,10)
c. schedule(dynamic)
d. schedule(dynamic,10)
e. schedule(guided)
3. Experiment with the use of the collapse clause and report on a few experiments including at
least one using the guided option. Should/does the collapse clause make much of a
performance difference in this case? Explain your answer in the context of your particular source
code, since the answer may vary depending on how you designed the code.
Task 3: OpenMP Program (Tasks) (40 points)
Modify your Task 2 program to use OpenMP tasks.
1. To begin with, create a code in which the processing of each cell constitutes a task, and one
thread is dedicated to creating all the tasks. Run your code with 1, 2, 4, and 8 threads and report
the average area and average time for each case.
2. Now modify your program so that it treats each row of cells as a task. Again, run your code with
1, 2, 4, and 8 threads and report the average area and average time for each case.
3. Finally, modify your program so that task creation is shared by all the threads. Again, run your
code with 1, 2, 4, and 8 threads and report the average area and average time for each case.
4. In your report, discuss the observed performance for the various task-based implementations, and
compare those versions with the versions from Task 2 using loop directives. (Be concise; just
highlight the most important observations.)
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Task 4: Parallel Random Number Generation (Extra Credit: 10 points)
A shortcoming of the random number generator I provided is that all the threads use the same sequence of
random numbers. Search on line for simple approaches to cure this particular problem and modify
drand.c to implement one of them in your best-performing program from Task 2. Run your modified
code several times using 8 threads and compare the results (average area and time) to the unmodified
version from Task 2. Does it make a significant difference? (Note: I’m not asking you to create a better
sequence of pseudo-random numbers; all that’s required is to ensure that each thread has a distinct
sequence of numbers with essentially the same statistics as the original sequence.)
Procedures for Programming Assignments
For this class, we will use the Canvas website to submit solutions to programming assignments.
Remember: While you may discuss the assignment with me, a ULA, or your classmates, the source
code you turn in must be yours alone and should not represent collaborations or the ideas of others!
What should you include in your solution package?
1. All source code files, Makefiles, and scripts that you developed, used, or modified. All source
code files should contain proper attributions and suitable comments to explain your code.
2. A report in PDF format containing:
a. Your name, the assignment number, and course name/number.
b. Information on building and running the code:
i. A brief description of the software/development environment used. For example, you
should list the module files you’ve loaded.
ii. Steps/commands used to compile, link, and run the submitted code. Best is to use a
Makefile for building the code and to submit an sbatch script for executing it. (If you ran
your code interactively, then you’ll need to list the commands required to run it.)
iii. Outputs from executing your program.
c. Any other information required for the assignment, including any questions you were asked to
answer.
How should you submit your solution?
1. On the cluster, create a directory named “NetID_ps2_cpsc424”. (For me, that would be
“ahs3_ps2_cpsc424”. Put into it all the files you need to submit.
2. Create a compressed tar file of your directory by running the following in its parent directory:
tar -cvzf NetID_ps2_cpsc424.tar.gz NetID_ps2_cpsc424
3. To submit your solution, click on the “Assignments” button on the Canvas website and select this
assignment from the list. Then click on the “Submit Assignment” button and upload your solution file
NetID_ps2_cpsc424.tar.gz. (Canvas will only accept files with a “gz” or “tgz” extension.)You may add
additional comments to your submission, but your report should be included in the attachment. You
can use scp or rsync or various GUI tools (e.g., CyberDuck) to move files back and forth to Omega.
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Due Date and Late Policy
Due Date: Sunday, October 7, 2018 by 11:59 p.m.
Late Policy: On time submission: Full credit
Up to 24 hours late: 90% credit
Up to 72 hours late: 75% credit
Up to 1 week late: 50% credit
More than 1 week late: 35% credit

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