1. Performance Profiling of NGS Genome Assembly Algorithms Alex Ropelewski Pittsburgh Supercomputing Center ropelews@psc.edu 412-268-4960

2. NGS: Assembly Algorithm ALIGNED 3-MERS 1.ATG 2. TGG 3. GGC 4. GCG 5. CGT 6. GTG 7. TGC 8. GCA 9. CAA 10. AAT Genome: ATGGCGTGCAAT ATTG AA CA GTCG GC GG 10.AAT 1.ATG 2.TGG 3.GGC 6.GTG 5.CGT 4.GCG 9.CAA 8.GCA 7.TGC Assembled Genome via Eulerian Cycle (reads represented as edges) de Bruijn Graph

3. Program characteristics 2 codes of interest: – Allpaths-LG: designed for assembling large genomes (Mostly C++, pipeline uses make) – Velvet: used frequently for small genomes (written in C; uses some OpenMP) Both codes are: – memory intensive – time intensive – have some parallelization

4. Desired Profile Information For each program/step in the assembly pipeline: – Time and Memory consumption – Identification of serial and parallel steps – Quantify I/O characteristics – Quantify how many times each step is run For the most time consuming and most called programs/steps: – Time consumed by each function – How many times is each function called – Quantify I/O characteristics – Identify parallel steps and examine scaling – Describe the main memory consumers

5. General Outcome Where should the optimization effort be focused? – Are there serial optimizations? – Additional candidates for parallelization? – Can the existing parallelization be improved? – Can the IO be improved? – Memory performance issues to address? – Something else?