High-Performance Computing Lab 4

COSC 420 - High-Performance Computing
Lab 4

1 Objectives
In this lab you will focus on the following objectives:
1. Develop familiarity with C programming language
2. Develop familiarity with parallel computing tools MPICH and OpenMPI
3. Develop familiarity with linear algebra and solving linear systems of equations.
4. Explore empirical tests for program efficiency in terms of parallel computation and resources
2 Tasks
1. You may work in groups of one or two to complete this lab. Be sure to document in comments and
your README who the group members are.
2. You will add features to your matrix operation library from Lab 3. So begin by verifying that matrix
operations operate correctly and efficiently.
3. Using MPI, implement a first-pass attempt to calculate the eigenvector corresponding to the largest
eigenvector of a matrix. You may use the na¨ıve power-method discussed in lecture:
(a) Let A ∈ R
d×d and let x be the all 1’s vector in R
d
.
(b) Perform update: x ← Ax
(c) Normalize: x ← x/kxk2
. Note: kxk2 =
p
x
2
1 + x
2
2 + · · · + x
2
d
is the Euclidean norm.
(d) From this, x will converge quite quickly to the eigenvector corresponding to the largest eigenvalue.
You may obtain an estimate of this eigenvalue by computing kAxk2
/kxk2
.
i. Make sure you test for convergence by choosing a small threshold (e.g. 10−16) and stopping
when the new x differs from the old one by less than that amount.
ii. You will also want to set an upper-bound on the number of iterations to perform and report
an error if it has not converged after that limit.
4. Implement two ways to parallelize the data-access of this algorithm:
(a) One in which the root processor reads the entire A and x data from a file and scatters them
appropriately to perform the matrix multiplication with your existing library. Then use other
collectives as appropriate to perform the update and normalization in parallel.
(b) One which uses MPI_File access methods to read/write the distributed “chunks” of A and x to
files instead of using scatter/gather.
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(c) Compare these two methods in terms of efficiency in your readme.
5. Note that for debugging output, it may be helpful to have each node write its output to a file, unique
to that process. This will be how we will gather logs when submitting to larger clusters.
6. Test the program on some large matrices (at least tens of thousands of rows/columns, perhaps more).
(a) Run each input several and measure the time to complete each.
i. You can use the MPI “Walltime” function to measure the time taken to get between two
points in a body of code:
double startTime, stopTime;
startTime = MPI_Wtime();
/* Do some work here */
stopTime = MPI_Wtime();
printf("MPI_Wtime measured: %1.2f seconds\n", stopTime-startTime);
fflush(stdout); // manually flush the buffer for safety
ii. For a simple timing method on a single processor, you can use the features of time.h, e.g.:
clock_t begin = clock();
/* here, do your time-consuming job */
clock_t end = clock();
double time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
However, be aware that this does not measure real-time. It only measures the time that the
OS spends on your process, which means it may be shorter than your real-world observation,
for instance with the time shell command.
(b) Record the averages of each, report them in a clean tabular format
(c) Be sure to use tools such as valgrind and gdb to find and fix bugs with your code. Make sure
there are not memory leaks, invalid access, or usage of undefined variables!
7. Include a README file to document your code, any interesting design choices you made, and answer the
following questions:
(a) What is the theoretical time complexity of your algorithms (best and worst case), in terms of the
input size?
(b) According to the data, does adding more nodes perfectly divide the time taken by the program?
(c) What are some real-world software examples that would need the above routines? Why? Would
they benefit greatly from using your distributed code?
(d) How could the code be improved in terms of usability, efficiency, and robustness?
3 Submission
All submitted labs must compile with mpicc and run on the COSC Linux environment. Include a Makefile
to build your code. Upload your project files to MyClasses in a single .zip file. Finally, turn in (stapled)
printouts of your source code, properly commented and formatted with your name, course number, and
complete description of the code and constituent subroutines. Also turn in printouts reflecting several
different runs of your program (you can copy/past from the terminal output window). Be sure to test
different situations, show how the program handles erroneous input and different edge cases.
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