1. ParAlign: with OpenMP Peter Reetz

2. Overview n Simple algorithm for finding un-gapped alignments n 4 to 5 times faster than Smith- Waterman algorithm & almost as sensitive n Easy to see the data parallelism n OpenMP is an obvious choice

3. Design n parAlign( int sub[], int sLen, int que[], int qLen ) n Calculation along diagonals is based on previous (row-1, col-1) value and the substitution matrix n Splitting up the diagonals across the processor elements = load balancing n Shared memory: Substitution Matrix, Protein sequences

4. …Design n Starting position of diagonals are based on the index that each thread is working on n Parallelizing the final loop to get the result had no affect

5. Speedup

6. …Speedup n Only able to test with 4 PE n After initial speedup, each successive PE had only a small impact n 1000x1000 is pushing it for protein sequences!

7. Pause… for reflection n Lesson I have still NOT learned: –waiting until the last minute to complete a project n Lesson I HAVE learned: –don’t be too ambitious and get into something I won’t be able to finish –I probably could have done more with this

8. New Lessons n Look at documentation FIRST when a problem comes up - especially when doing reduction in OpenMP n Pay close attention to what I am learning the first time around n Always initialize variables in OpenMP

9. If only I had more time n Implement a parallel version of the Smith-Waterman algorithm to compare speed n Implement the confusing aspect of ParAlign that will account for gapped alignment n Implement the function in assembly language to see how parallelism can work at a low level

10. Summary n Implementing ParAlign was a straight forward problem easily lending itself to parallelization with OpenMP n There isn’t much information available concerning speed issues of this algorithm to compare my results to