Bio-Sequence Analysis with Cradle’s 3SoC™ Software Scalable System on Chip

1. Bio-Sequence Analysis with Cradle’s 3SoC™ Software Scalable System on Chip • Xiandong Meng, Vipin Chaudhary • Parallel and Distributed Computing Lab • Wayne State University • Detroit, MI USA

2. Outline • Motivation • Smith-Waterman Algorithm • Related Works • Parallel Architecture of 3Soc Chip • Implementation Strategy on 3SoC chip • Performance Evaluation • Future works • References

3. Motivation • Genetic sequence databases are growing exponentially • Discovered sequences are analyzed by comparison with databases • Complexity of sequence comparison is proportional to the product of query size times database size •  Analysis too slow on sequential computers • Two possible approaches Heuristics, e.g. BLAST, FastA, Exhaustive, e.g.Smith-Waterman, • The implementation of the most sensitive algorithms, Smith-Waterman algorithm on the 3SOC parallel chip multiprocessor architecture could provide high quality and performance at low cost.

4. Smith- Waterman Slower FastA Search Speed BLAST Faster Lower Data Quality Higher Sequence Alignment Comparison CCA – CGAAGCTTGGCTGGAACAGGACTTCTG - GG : : : : : : : : : : : : : : : : : : : : : : : CCAGCC AAGCTTCGTGGGCA -AGGAGGCCAGCGG Heuristics, e.g. BLAST, FastA, but the more efficient the heuristics, the worse the quality of the results Exhaustive, e.g.Smith-Waterman, get high-quality results but long computation time

5. Smith-Waterman Algorithm • Optimal local alignment of two sequences • Performs an exhaustive search for the optimal local alignment • Complexity O(nm) for sequence lengths n and m • Based on the 'dynamic programming' (DP) algorithm • Fill the DP matrix • Find the maximal value (score) in the matrix • Trace back from the score until a 0 value is reached

6. Smith-Waterman Algorithm (cont.) Optimal local alignment of two sequences Performs an exhaustive search for the optimal local alignment Based on the 'dynamic programming' (DP) algorithm

7.  A T C T C G T A T G A T G  0 0 0 0 0 0 0 0 0 0 0 0 0 0 G 0 T 0 2 1 2 1 1 4 3 2 1 1 3 2 0 C 0 0 1 4 3 4 3 3 3 2 1 0 2 2 T 0 0 2 3 6 5 4 5 4 5 4 3 2 1 A 2 2 5 5 4 4 7 6 5 6 5 4 0 2 T 0 1 4 3 4 4 4 6 5 9 8 7 8 7 C 0 0 3 6 5 6 5 5 5 8 8 7 7 7 A 2 5 5 5 5 4 7 7 7 10 9 8 0 2 C 0 1 1 4 4 7 6 5 6 6 6 9 9 8 Smith-Waterman Algorithm (Example) • Align S1=ATCTCGTATGATGS2=GTCTATCAC 0 0 0 0 0 0 2 1 0 0 2 1 0 2 2 • =1, =1 4 3 5 7 9 8 10 • A T C T C G T A T G A T G • G T C T A T C A C

8. Database Sequence b1 b2 b3 b4 b5 b6 a1 a2 a3 a4 Query Sequence Data dependencies in S-W Algorithm Computational dependencies in the Smith-Waterman alignment matrix.

9. Related Works • Kestrel parallel processor • 512-Processing Elements board • at UC at Santa Cruz • Field Programmable Gate Array • PCI board with one 144-PE FPGA • at University of Tsukuba • Fuzion 150 • 1536-PE on a single chip • at Nanyang Technical University

10. Architecture of 3SOC chip • Characteristics of 3SoC • Quads • Quad is the primary unit of replication for 3SoC. A 3SoC chip has one or more Quads. • PEs • Each PE is a 32-bit processor with 16-bit instructions and thirty-two 32-bit registers. The PE is rated at approximately 90 MIPS. • DSEs • Each DSE is a 32-bit processor with 128 registers. The DSE is the primary compute engine of the UMS and is rated at approximately 350 MIPs for integer or floating point performance.

11. Input DNA/Protein Query Sequence Compare the Query Sequence with Well-Known Sequence in Sequence Database 3SoC Chips Do the S-W sequence alignment Output Results Protein Sequence search on 3SoC chips

12. Implementation Strategy on 3SoC Query Sequences 12 PEs DSE1 DB1 DSE2 DB2 DSE23 DB23 DSE24 DB24 12 PEs Output results

13. DB Seq2 DB Seq1 DSE 1 Query Seq. DSE 2 Buffer 1 Buffer 1 PE Buffer 2 Buffer 2 Inside chip MTE DRAM Outside Chip Implementation Details of S-W Algorithm

14. Performance Analysis

15. Conclusions and Future Work • Demonstrated that 3SOC chip multiprocessor architecture can be applied efficiently for Comparative Genomics. • Optimize implementation. • Apply the next generation 3SOC Architecture on High Performance Embedded Systems for Bioinformatics computing.

16. References • Smith, T.F. AND Waterman, M.S. Identification of common molecular subsequences. J. Mol. Biol., 147, (1981), 195-197 • Needleman, S. and Wunsch, C. A general method applicable to the search for similarities in the amino acid sequence of two sequences. . J. Mol. Biol., 48(3), (1970), 443-453 • Hughey, R. Parallel hardware for sequence comparison and alignment. (1996) Comput. Appl. Biosci. 12, 473-479 • Schmidt, B., Schroder, H. and Schimmler, M. Massively Parallel Solutions for Molecular Sequence Analysis, International Parallel and Distributed Processing Symposium: IPDPS Workshops (2002), p. 0186 • Yamagucchi Y., Maruyama, T. High Speed Homology Search with FPGA. Pacific Symposium on Biocomputing 2002 • Grate, L., Diekhan, M., Dahle, D. and Hughey, H. Sequence Analysis With the Kestrel SIMD parallel Processor.Pacific Symposium on Biocomputing 2001 pp.263-74

17. Questions?