Exploring the Viability of the Cell Broadband Engine for Bioinformatics Applications

Transcription

1 Explring the Viability f the Cell Bradband Engine fr Biinfrmatics Applicatins Authrs: Vipin Sachdeva, Michael Kistler, Evan Speight and Tzy-Hwa Kathy Tzeng Presentatin by: Keyur Malaviya Dept f Cmputer & Infrmatin Sciences University f Delaware

2 Overview Prblem: Genmic data/cmputatinal requirements grwing General-purpse prcessrs cannt handle this Apprach: Parallelize & prt existing applicatins t multicre Use multicre architecture like Cell (PS3) Steps: Pick applicatins, Perfrm prfiling Make necessary cde changes fr prting Publish results Gal: Validate perfrmance gain n PS3

3 Biinfrmatics Quick Intr Terminlgy: Gemnes DNA strand Prteins A typical prblem: Machine learning, Predictin, Data mining Supprt vectr based, HMM, Clustered Few examples: Determine bilgical functins f prteins Understand bichemical pathways Assemble strings t make Genme Apprach: Cmpare sequence data with knwn genmes

4 Experimental Setup Tw applicatins selected 1) Sequence alignment : FASTA (ssearch34) Smith-Waterman algrithm [ O(nm) ] Pairwise alignment f gene sequences Uses dynamic prgramming algrithm 2) Hmlgy detectin: ClustalW(clustalw) Multiple sequence alignment

5 Sequence alignment basics Pairwise alignment: Mst cmmnly perfrmed tasks in biinfrmatics T align tw sequences: i. Alignment scre Matrix (e.g: Blssm, PAM) ii. Cmparing them, assign scres iii. Insert gaps in ne r bth sequences iv. Traceback Dynamic prgramming table t Prduce an ptimal scre

6 Sequence alignment basics T align tw sequences: Alignment scre Matrix (e.g: Blssm, PAM) Cmparing them, assign scres Insert gaps in ne r bth sequences Traceback & prduce an ptimal scre Tw sequences: Scring table Aligned Sequences:

7 ClustalW basics In three steps: i. All sequences are cmpared pairwise (Smith- Waterman algrithm) ii. iii. Create a hierarchy fr alignment (guide tree) by cluster analysis (distance matrix) fr each pair Prgressive add ne sequence accrding t the guide tree t get multiple sequence alignment

8 ClustalW: Multiple seq alignment.. Guided tree r Phylgenetic tree

9 Applicatins characteristics Embarrassingly parallel cmputatin Small critical time-cnsuming cde size Regular memry accesses Vectrized cde

10 Perfrm prfiling (gprf) Mre than half the executin time is taken by a single functin: FASTA: drpgsw ClustalW: frward pass HMMER: P7Viterbi Figure: Executin prfile frm gprf fr three applicatins & the BLASTapp.

11 Prting t Cell Architecture Altivec: is a PwerPC frm AIM alliance Altivec SSE implementatins: drpgsw fr FASTA and P7Viterbi fr HMMER IBM Life Sciences: Vectrized kernel fr frward_pass f ClustalW Prted with few mdificatins t CELL

12 Prting t Cell Architecture Advantage n CELL: Used 9 cres: PPU als as a prcessing element Cmpilatin f prgrams: Fr CELL: XLC v8.1 with -O3 PwerPC G5: -O3 -mcpu=g5 -mtune=g5 Optern & Wdcrest: -O3

13 FASTA (Smith-waterman) n CELL FASTA package includes Altivec-enabled Smith- Waterman Smith_waterman_altivec_wrd kernal was prted with simple changes t CELL Altivec APIs: vec_max, vec_subs were written fr SPUs Pairwise alignment f 8 pairs f sequences, using ne SPU fr each pairwise alignment Limitatin in current implementatin: Size f bth sequences <= SPU lcal stre (256 KB) Sequence size <= 2048 characters

14 Lng sequence cmparisns: T d genme-wide r lng sequence cmparisns: Implement pipelined apprach amng SPUs Each SPU perfrms Smith-Waterman alignment fr a blck, ntifies the next SPU thrugh a mailbx message Later SPU uses bundary results f previus SPU fr its wn blck cmputatin Future research: Supprt f bigger sequences n the Cell

15 Perfrmance f Smith- Waterman Alignment executin time fr different prcessrs: 1: Sequence length = : Sequence length = 2048

16 Prting ClustalW n CELL pairalign functin: All-t-all pairwise cmparisns fr n sequences perfrms n(n 1)/2 alignments Takes 60%-80% f the executin time pairalign functin is made f 4 functins 1) Frward_pass cmputes the maximum scre and is the mst time-cnsuming step f pairalign

17 Applicatin s Architecture Input t ClustalW: n sequences in a query sequence file n(n 1)/2 cmputatins Mfc DMA in/ut frm 16-byte bundaries: Pack all sequences in a single array Each sequence begin at a multiple f 16- bytes

18 Applicatin s Architecture PPU creates threads, passes max sequence size SPUs wait fr PPU t send a message t pull in the cntext data & begin cmputatin Wrk distributin Rund-rbin strategy: Each SPU is assigned a number frm 0 t 7 if i md 8=k SPU k cmpares seq n. i against all sequences i+1 t n [ i=9 => k=0 ] [i=15 => k=7] [ s n ]

19 Issues in prting ClustalW and bttlenecks IBM Life Sciences vectrized versin f frward_pass Altivec APIs: vec_max, vec_adds written fr SPUs SPUs dn t supprt 16-bit: Altivec used vectr status & cntrl register t detect verflw n it SPU use f 32-bit (int) lwers vectr cmputing efficiency (nly 4 values can be packed in a vectr) SPU has nly vectr registers: Reading Alignment matrix scre a scalar peratin suffers n SPU

20 Issues in prting ClustalW and bttlenecks SPUs nly have static branch predictin: Fails n a branch with multiple cnditins r Mre lp variables handling bundary cases Such branches are difficult t predict fr the SPU Slutin: Make branch depend n a single lp variable Break inner alignment lp int several lps Explicit handling f bundary cases 2X perfrmance gains

21 Cde changes behavir Imprvement f perfrmance f ClustalW alignment functin with different cde changes. Best implementatin: Using integer datatypes with n branches

22 Perfrmance f frward_pass (ClustalW) Cmparisn f Cell Perfrmance with ther prcessrs fr nly alignment functin with simple rund-rbin strategy Tw inputs frm BiPerf suite: 1) 1290.seq has 66 sequences f average length ) 6000.seq has 318 sequences f average length 1043 NOTE: Optern and the Wdcrest perfrmance is nn-vectrized

23 Perfrmance f ClustalW Cmparisn f Cell Perfrmance with ther prcessrs fr ttal time f executin Tw inputs frm BiPerf suite: 1) 1290.seq has 66 sequences f average length ) 6000.seq has 318 sequences f average length 1043 NOTE: Optern and the Wdcrest perfrmance is nn-vectrized

24 Cmputing the final alignment Perfrmance gain in Frward_pass Final step executing n PPU is much slwer cmpared t ther superscalar prcessrs Tw appraches t this prblem: Execute mre cde n the SPUs, r Use Cell as an acceleratr, alng with a superscalar prcessr Future wrk: Increasing PPU perfrmance (RadRunner prject is explring such hybrid architectures)

25 PPU penalty: Cell perfrmance is marginally better in verall executin time due t perfrmance f the PPU Frward_pass Frward_pass: Cell (8 SPUs) 7.5 times t Pwer G5 Overall: Cell (8 SPUs) 1.2 times t Pwer G5

26 Current prgress in prting Many biinfrmatics applicatins are being prted t the Cell prcessr 2500 playstatins used t parallelize gene-finding and sequence alignment sftwares Prtein flding n distributed cmputing and GPUs Charm++ runtime system, used fr NAMD simulatins FASTA, ClustalW, and HMMER

27 Cnclusins & Future Wrk Cell s ttal pwer cnsumptin < half f a superscalar prcessr Cell is price & pwer-efficient platfrm fr future biinfrmatics cmputing Future Wrk: Remving limitatins & increasing ptimizatin Prting HMMER t Cell prcessr Handling size that exceeds 256 KB Use f partitining input amng 8 SPUs Prting prtein dcking, RNA interference, medical imaging and few mre applicatins

28 QUESTIONS