Hybrid Parallel Programming for Massive Graph Analysis

More documents

Recommendations

Info

Eulerian path‐based strategies •Break up the (short) reads into overlapping strings ti of f length l thkk. k = 5 ACGTTATATATTCTA ACGTT CGTTA GTTAT TTATA ….. TTCTA CCATGATATATTCTA CCATG CATGA ATGAT TGATA ….. TTCTA • CConstruct t ta de d BBruijngraph ij h ( (a di directed t d graph h representing overlap between strings)
de Bruijn graphs • Each (k‐1)‐mer represents a node in the graph • Edge exists between node a to b iff there exists a k‐mer such that its prefix is a and suffix is b. AAGACTCCGACTGGGACTTT ACTCCGACTGGGACTTTGAC CGA CCG TCC CTC TGA AAG AGA GAC ACT CTT TTT • TTraverse th the graph h (if possible, ibl identifying id tif i an EEulerian l i path) th) tto <strong>for</strong>m contigs. GGA GGG • However, correct assembly is just one of the many possible , y j y p Eulerian paths. TGG CTG
Page 1 and 2: Hybrid Parallel Programming for Mas
Page 3 and 4: Why hybrid programming? •Traditio
Page 5 and 6: Characterizing Large‐scale graph
Page 7 and 8: Minimizing Communication • Irregu
Page 9 and 10: “2D” Graph Partitioning Strateg
Page 11 and 12: Parallel BFS Implementation • Con
Page 13 and 14: Parallel Performance • 32 nodes o
Page 15: De novo Genome Assembly • Genome
Page 19 and 20: Graph construction •Store edges o
Page 21 and 22: Summary of various steps and Analys
Page 23 and 24: (secondss) Executtion time Parallel
Page 25 and 26: Acknowledgments BERKELEY PAR LAB

Hybrid Parallel Programming for Massive Graph Analysis

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?