Assignment #4 Parallel Programming with OpenMP


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ECE 565
Assignment #4
Parallel Programming with OpenMP
Important General Instructions
1. This is assignment is to be done in groups of 2. Each group must work
2. This assignment consists of 2 sub-problems. In each sub-problem, you will parallelize
existing code using OpenMP and conduct performance experiments that will be written
up into a report.
3. Performance experiments: Your performance experiments must be done using an 8-
core VM (running Ubuntu Linux) that you will have access to for assignment #4 and #5.
You should run your performance experiments.
4. Program development: Your code development, testing, debugging, etc. may be
performed either on your own machine, the Intel server that you were given access to
previously ( or, or the 8-core Ubuntu VM.
5. Note, as you work through these sub-problems, that bugs in parallel code are often
timing dependent, i.e. incorrect parallel code may (quite often) result in correct
execution due to the absence of certain timing conditions in which the bugs can
manifest. Therefore, manual code correctness analysis is very important, and your code
will be graded based on code correctness rather than on output correctness from
single test runs.
6. Please follow the spec carefully and turn in everything that is asked. Be sure to start
early and make steady progress.
7. Parallel programming requires significant design effort although the amount of code you
will write will not be extremely large. You should not underestimate the effort and time to
get to the right solution. The best way to do well in this project is to start early.
8. As you work through the two sub-problems of this assignment you will create a writeup
(named report.pdf). You will submit this report as well as modified code as described in
the submission instructions. Be sure to submit all source code files and report.pdf
inside of a file exactly as described, or points will be deducted.
First Steps – You should take care of this ASAP
First, form your team of 2. Feel free to use Piazza to search for a teammate, and don’t
hesitate to let me know if you need help to find a partner.
Problem 1: Histogram
Download the histogram.tgz file and extract it with the following command: ‘tar xzvf
histogram.tgz’. In the included source code, focus on histogram() function. In the loop nest
inside that function, you will notice a statement that has a loop-carried dependence, depending
on the content of the image’s pixels across different iterations:
Your task is to parallelize this loop nest using OpenMP. Do not parallelize the outer ‘for m’ loop.
That is only included so that the histogram generation is done a number of times. You should
parallelize the ‘for i’ and/or ‘for j’ loops (you can experiment to see what parallelization works
best). The test prints out histogram results which you can use to ensure your parallel program
is correct (the ‘diff’ Linux command will be useful for this). The code also prints the runtime (in
seconds) that you should report.
In order to remove the parallelization limitation imposed by the loop-carried dependence,
implement you should implement 3 different approaches:
1. Use an array of locks. For help in how to declare and use OpenMP locks, refer to the
OpenMP standards document (e.g. omp_init_lock(), omp_set_lock(), omp_unset_lock()).
Put this version in a code file named histo_locks.c and edit the makefile to generate an
additional executable named ‘histo_locks’.
2. Use atomic operation to protect the update to the histogram element, instead of anarray
of locks. Before implementing your solution, read about #pragma omp atomic in the
OpenMP standards document. Put this version in a code file named histo_atomic.c and
edit the makefile to generate an additional executable named ‘histo_atomic.
3. Find your own creative solution that does not rely on atomic operations or locks. Put this
version in a code file named histo_creative.c and edit the makefile to generate an
additional executable named ‘histo_creative’.
For each of these 3 versions:
a) Run the program and verify your results against the sequential version.
b) Measure and record the execution time reported by the program when running with 2, 4,
and 8 threads. Then, plot the speedup curve by taking the ratio of the sequential
execution time to the parallel execution time.
c) Write your observations about which version performs best, and discuss why you think
that is the case. Also, describe how your own creative solution removes the need for
using locks and atomic operations while still producing correct results.
The histogram program is executed, for example, as “./histogram <input_file>”. There are two
image input files included in the Histogram/ directory. The first one is uiuc.pgm, a small input
image that can be used for testing your code. The second one is uiuc-large.pgm, a large input
image that should be used for running your performance experiments. Due to the size of uiuclarge.pgm, do *not* include it along with your final files that you submit.
Part 2: AMG
Download the amgmk.tgz file and extract it with the following command: ‘tar xzf
amgmk.tgz’. This code is a microkernel that represents important portions of an
algebraic multigrid solver application. This microbenchmark exercises 3 computations:
1) compressed sparse row (CSR) matrix vector multiplication, 2) algebraic multigrid
(AMG) mesh relaxation, and 3) a simple a * X + Y vector operation.
The provided makefile will compile the baseline sequential code. The benchmark can
be run with no arguments (i.e. ./AMGMk) after compilation. The output will print a
measured Wall time for each of the 3 computations, labeled “MATVEC”, “Relax”, and
“Axpy”. The benchmark also self-checks results. An “error” message will be printed for
each computation where an incorrect result is produced.
Your task is to use OpenMP to parallelize the code for each of these 3 computations to
improve performance as much as possible compared to the baseline sequential code
(don’t forget that profiling is the way to find out which code is most important). You may
also alter / restructure the baseline C code if needed to implement your OpenMP
Note, your OpenMP parallelization should not be done within the main.c code file
– it should be done within the code of the 3 specific microkernels mentioned
You should include the following items in your report.pdf writeup: 1) A clear description
of what changes you make to the code (e.g. describe your OpenMP directives and any
code changes you make). You can refer to source files, line numbers, etc. and show
code snippets. 2) You should summarize your baseline sequential vs. optimized
parallel performance across 1, 2, 4, and 8 threads. You will include your final
parallelized code in directory within your submission. This directory should be
named amgmk/
Machine Usage Guide
1. How to login to the machines
As described earlier, you will be running your programs on a shared memory system. Only SSH
access is permitted. If you are using Windows, you will need an SSH client such as PuTTy (free
software). If you are using a UNIX/Linux machine, you can login using the command below
(depending on which machine you are assigned):
ssh -k -X <user_id>@<machine>
To copy files from or to your accounts, you can use a secure copy program such as scp.
2. How to obtain the program
You can download the tar file containing the benchmark you are supposed to work on from the
class webpage. To un-tar the file, use:
tar xzvf <file>.tgz
3. How to compile a program
You will use the gcc compiler which provides support for compiling OpenMP programs.
To compile code with OpenMP directives:
$ gcc -O3 -fopenmp -o <outputfile> <source_file>
Where <outputfile> is the output file name (executable) and <sourcefile> is your .c file.
The -fopenmp option informs the compiler that the program is a shared memory program with
OpenMP directives.
4. How to run a program
If you want to run an OpenMP program named “test” with 4 threads you can type:
$ OMP_NUM_THREADS=4 ./test > result.out
5. You need to Submit
You will submit this project as a single zip file named to your Sakai drop box. Only 1
teammate needs to submit. You are responsible to submit the following contents in the zip file:
1) The report document (report.pdf) that addresses all parts described above.
2) 2 sub-directories (named histo/ and amgmk/) with your source code for the parts
described above.

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