SWAMP+: Enhanced Smith- Waterman Search for Parallel Models - - PowerPoint PPT Presentation

Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

U N C L A S S I F I E D

SWAMP+: Enhanced Smith- Waterman Search for Parallel Models

Shannon Steinfadt, Ph.D.

Los Alamos National Laboratory shannon@lanl.gov

Slide 2 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Outline

• Motivation for Sequence Alignment
• Smith-Waterman Local Sequence Alignment
• SWAMP
• ASC
• SWAMP using ASC Emulator
• SWAMP+
• SWAMP and SWAMP+ on Metal
• ClearSpeed
• Convey Computer
• Contributions
• Future Work
• Questions?

gcggacgctccacg-tgtc--c—-ct-cgccgcgccc-cgtctacc gggccctcctggctcccaacagcttctcagttc ccacttc ||:|:||||::|-|::|--|--||-|-|:|:|::| ||-|:||

Slide 3 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Motivation: Sequence Alignment

Given two sequences:

Align them to find the longest, most common subsequence

DNA nucelotides {A, G, T, C} Proteins {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V}

Query:IHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVA Subject:MFCVQCEQTIRTPAGNGCSYAQGMCGKTAETSDLQDLLIAALQGLSAWAVKAREYGIINHDVDSFAPRAFFST

LTNVNFDSPRIVGYAREAIALREALKAQCLAVDANARVDNPMADLQLVSDDLGELQRQAAEFTPNKDKAAIGENILGLRL LCLYGLKGAAAYMEHAHVLGQYDNDIYAQYHKIMAWLGTWPADMNALLECSMEIGQMNFKVMSILDAGETGKYGHPTPTQ VNVKATAGKCILISGHDLKDLYNLLEQTEGTGVNVYTHGEMLPAHGYPELRKFKHLVGNYGSGWQNQQVEFARFPGPIVM TSNCIIDPTVGAYDDRIWTRSIVGWPGVRHLDGDDFSAVITQAQQMAGFPYSEIPHLITVGFGRQTLLGAADTLIDLVSR EKLRHIFLLGGCDGARGERHYFTDFATSVPDDCLILTLACGKYRFNKLEFGDIEGLPRLVDAGQCNDAYSAIILAVTLAE KLGCGVNDLPLSLVLSWFEQKAIVILLTLLSLGVKNIVTGPTAPGFLTPDLLAVLNEKFGLRSITTVEEDMKQLLSA

Slide 4 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Motivation: Sequence Alignment

Given two sequences:

Align them to find the longest, most common subsequence

DNA nucelotides {A, G, T, C} Proteins {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V} One of the most common fundamental tasks is local sequence alignment

Query: VIA-EPYRE-RLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDK : : :: : :: : : : : Subject: LVSREKLRHIFLLGGCDGARGERHYFTDFATSVPDDCLILTLACGK

Slide 5 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Pairwise Local Sequence Alignment

(derived by humans) (preserved by evolution)

Similar Characters Similar Characters Similar Structure Similar Structure Similar Function Similar Function Ancestral Relationships Gene Functionality Aid in Drug Discovery Assembly of Raw Data Ancestral Relationships Gene Functionality Aid in Drug Discovery Assembly of Raw Data

Homologous Sequences

Slide 6 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm Compare all possible combinations of sequence characters against each other

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Slide 7 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Aligning using Smith-Waterman Algorithm Compare all possible combinations of sequence characters against each other

Slide 8 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Aligning using Smith-Waterman Algorithm Compare all possible combinations of sequence characters against each other

Slide 9 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations of sequence characters against each other

Slide 10 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations of sequence characters against each other

Slide 11 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 12 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 13 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 14 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 15 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 16 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 17 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Aligning using Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Compare all possible combinations - but it has dynamic programming data dependencies

Slide 18 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Smith-Waterman Recursive Matrix Equations

σ − ⎪ ⎭ ⎪ ⎬ ⎫ ⎪ ⎩ ⎪ ⎨ ⎧ − =

− − j i j i j i

D g C D

, 1 , 1 .

max

σ − ⎪ ⎭ ⎪ ⎬ ⎫ ⎪ ⎩ ⎪ ⎨ ⎧ − =

− − 1 , 1 , ,

max

j i j i j i

I g C I

( )

⎪ ⎩ ⎪ ⎨ ⎧ ≠ = =

j i j i j i

S2 S1 miss_cost S2 S1 match_cost S2 , S1 if if d Ci, j = max Di .j Ii, j Ci −1

, j −1+ d S1i ,S2j

( )

⎧ ⎨ ⎪ ⎪ ⎩ ⎪ ⎪ ⎫ ⎬ ⎪ ⎪ ⎭ ⎪ ⎪

g : gap extension cost σ: gap opening cost

g

Slide 19 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Traceback in the Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

1) Find the maximum computed value

Slide 20 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Traceback in the Smith-Waterman Algorithm

Cost Key

Match +10 Miss -3 Insert a Gap -3 Extend a Gap -1

Alignment:

CATTG C - -TG

1) Find the maximum computed value 2) Traceback until you reach ‘0’s

Slide 21 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Smith-Waterman Vectorization Approaches

• Parallel Processing
• Allows high-quality results in less time using the Smith-Waterman algorithm
• Rognes described four basic approaches:
• Vectors along the anti-diagonal (a wavefront) approach described by Wozniak
• Vectors along the query (a single column split downward) described by Rognes

and Seeberg

• A striped approach introduced by Farrar
• Multi-sequence vectors described by Alpern et. al. and again by Rognes

Slide 22 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Parallelizing the Smith-Waterman Algorithm

Sequential matrix

• f computed

values

Slide 23 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Parallelizing the Smith-Waterman Algorithm

Tilted data arrangement to parallelize and process a diagonal at a time.

Slide 24 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Parallelizing the Algorithm: “Tilting” the Matrix

Slide 25 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Parallelizing the Algorithm: “Tilting” the Matrix

Slide 26 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Parallelizing the Algorithm: “Tilting” the Matrix Smith-Waterman using Associative Massive Parallelism (SWAMP)

Slide 27 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP (Smith-Waterman using Associative Massive Parallelism)

Used PEs Unused PEs Order of Computations

Slide 28 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

ASC: Associative Architecture

SIMD with special associative features Fine-grained parallelism Designed for fast associative searches

Content-based searches, not memory address

Slide 29 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

ASC Advantages

• Quick data movement in SIMD
• Move raw data in parallel
• At each step, PEs follow the algorithmic steps for data movement in lock step
• No message passing like MPI/PVM
• No store/forward
• No headers
• No explicit synchronizing
• Very fast operations for
• Finding Maximum / Minimum
• Finding if there are “Any Responders”
• “Pick One” active PE

Slide 30 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

ASC - SWAMP Algorithm

1) Read in S1 and S2 In Active PEs (those with data values for S1 or S2): 2) Initialize the two-dimension variables D[$], I[$], C[$] to zeros. 3) “Shift” or slide string S2 to create a titled matrix 4) For every anti_diagonal (a_d) from 2 to m+n-1 do in parallel { 5) If S2[$,a_d] is valid (S2 [$,a_d] ≠ “@” and S2[$,a_d] ≠ “-”) then {

6.1) Calculate score for deletion for D[$,a_d] 6.2) Calculate score for a insertion for I[$,a_d] 6.3) Calculate matrix score for C[$,a_d] } 7) local_maxPE=MAXDEX(C[$, a_d]) 8) if C[local_maxPE, a_d] > max_Val then { 9.1) max_PE = local_maxPE 9.2) max_Val = C[local_maxPE, a_d]) } }

10) Return max_Val, max_PE

Slide 31 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP (Smith-Waterman using Associative Massive Parallelism)

Used PEs Unused PEs Order of Computations

Slide 32 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP on ASC Analysis

• Computation takes O(m+n) time with m+1 PEs
• Sequential Smith-Waterman (Gotoh)
• O(m*n) time, m*n space
• When |S1| = |S2|, it becomes an O(n2) algorithm
• SWAMP parallel algorithm
• If actual number of PEs < m+1, assign {(m+1) / # PEs} work to each PE

Slide 33 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP on ASC - Performance

• Based on actual

measurements using ASC language and emulator.

• Predictions

shown with the dashed line.

Predictions calculated using linear regression and the least squares method.

Slide 34 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP+

• SWAMP+ returns multiple non-overlapping sequences
• Search and process with SWAMP multiple times
• Return top k non-overlapping, non-intersecting sequences
• Reveal additional information

—

Spatial information

—

Length of comparisons

—

Identify regulatory regions and motifs

Slide 35 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP+ - 3 variations

Slide 36 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Moving SWAMP+ to Hardware Have These Have These Want This Want This Used This Used This

Associative SIMD Model - ASC

ctcgccgcgc ggcggacgct ccacgtgtcc cccgtctacc gggccctcct ggctcccaac agcttctcag ttcccacttc

Slide 37 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Hardware Analysis: ClearSpeed Have These Have These Want This Want This Use This Use This

ClearSpeed Advance 620 PCI-X board

50 GFLOPS peak performance 25W average power dissipation

ctcgccgcgc ggcggacgct ccacgtgtcc cccgtctacc gggccctcct ggctcccaac agcttctcag ttcccacttc

Slide 38 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP+ on ClearSpeed

• Implemented SWAMP and SWAMP+ on the ClearSpeed board
• Used the software equivalent of

—

Maximum

—

Any Responders

—

Pick One

• Allows accurate, deterministic timings for algorithms

Slide 39 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

ClearSpeed – SWAMP+ Algorithm

1) Read in S1 and S2 In Active PEs (those with data values for S1 or S2): 2) Initialize Row 0, Col 0 variables D[$], I[$], C[$] to zeros. 3) For each PE, shift S2 down 1, copy entire string 4) For every a_d from 2 to m+n-1 do in parallel { 5) If S2[$,a_d] ≠ “@” and S2[$,a_d] ≠ “-” then { 6.1) Calculate score for deletion and insertion for D[$,a_d]; Calculate matrix score for C[$,a_d] } 7) local_maxPE=getPE(max_int(C[$, a_d])) 8) if C[local_maxPE] > max_Val then { 9.1) max_PE = local_maxPE 9.2) max_Val = C[local_maxPE]) } } 10) Return max_Val, max_PE 11) Perform traceback 12) Mark aligned values 13) Run alignment for all k values (2-12)

Slide 40 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

ClearSpeed SWAMP+ CUPS Performance

Slide 41 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Average Calculation w ith Eight Highest Outliers Rem ovedCycle Counts

10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 10 20 30 40 50 60 70 80 90 96 Sequence Lengths 1 Alignment 2 Alignments (1st) 2 Alignments (2nd) 3 Alignments (1st) 3 Alignments (2nd) 3 Alignments (3rd) 4 Alignments (1st) 4 Alignments (2nd) 4 Alignments (3rd) 4 Alignments (4th) 5 Alignments (1st) 5 Alignments (2nd) 5 Alignments (3rd) 5 Alignments (4th) 5 Alignments (5th)

ClearSpeed Calculation Times

Slide 42 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Average Traceback Cycle Counts for all Alignm ents

50000 100000 150000 200000 250000 300000 350000 400000 10 20 30 40 50 60 70 80 90 96 Sequence Lengths 1 Alignment 2 Alignments (1st) 2 Alignments (2nd) 3 Alignments (1st) 3 Alignments (2nd) 3 Alignments (3rd) 4 Alignments (1st) 4 Alignments (2nd) 4 Alignmens (3rd) 4 Alignments (4th) 5 Alignments (1st) 5 Alignments (2nd) 5 Alignments (3rd) 5 Alignments (4th) 5 Alignments (5th)

First (Longest) Traceback Cycle Counts Shorter (2 through k)

• r Second to Fifth

Alignments in this instance

Clearspeed Traceback Timings

Slide 43 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

ClearSpeed Computation Time Comparison

Slide 44 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP+ on ClearSpeed Analysis

• Computation was O(m+n) using m+1 PEs
• Showed similar performance for theoretical speedup as the ASC code
• When m = n, the running time of O(n) with a coefficient of 2

Slide 45 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Hardware Analysis: Convey

ctcgccgcgc ggcggacgct ccacgtgtcc cccgtctacc gggccctcct ggctcccaac agcttctcag ttcccacttc

Have These Have These Want This Want This Use This Use This

Convey Computer HC-1 FPGA + x86 Hybrid

Consists of a personality & application 768 GCUPS peak from single machine

Slide 46 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP+ on Convey Computer System

• FPGA + x86 Hybrid system
• Daughter FPGA board and an x86 closely married hardware
• The Smith-Waterman alignment application has multiple components
• Product consists of a personality (SW02) and an aligner application (cnysws)
• Implements multiple systolic arrays on the AEs to perform parallel Smith-Waterman

searches

Slide 47 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Convey Smith-Waterman Personality‡

4 AEs each contain 4 tiles

• Query loaded into each tile
• May join 2 or 4 tiles
• Up to 1280 query length
• Continuously process reference DB
• Efficient for searching a database with many

short strings

‡Slide courtesy of Convey Computer

Slide 48 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Convey Computer Smith-Waterman Performance‡

‡Graph courtesy of Convey Computer

Slide 49 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Convey Computer SWAMP+ on Non-Associative Parallel System

1) Run parallel Convey cnysws application with traceback for two files containing sequences (S1^) and database queries (S2^) 2) Capture alignment information for all query and database matches For each query: For each database match: 3) Copy query and sequence into altered{Query,Database}_n_n.faa 4) Mark aligned bases for each query-database match While # of iterations <k-1: For each pair of files created in instruction 3: 5) Run cnysws 6) Repeat Step 2 7) If score for hit * δ < current score: 7.1) Track match 7.2) Mark aligned bases as matched for query-database match 8) Output the k sub-alignments

Slide 50 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Convey Computer SWAMP+ Run Times

2 4 6 8 10 12 14 16 Tim e ( seconds) Sequence Length

Aver age Com putation Tim es

• Avg. Program Tim e Reported
• Avg. System Seconds Recorded

Externally

*Other files are single sequence to sequence

• comparison. AA consists of

2 amino acid (AA) queries

Slide 51 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

SWAMP+ on Convey Analysis

• Smith-Waterman execution efficient
• X86 version uses a combination of the most efficient Smith-Waterman alignment

—

Makes use Farrar extensions with SSE as well as North neighbor approximations

• Will use the x86 for larger comparisons and traceback
• Computation was O(m+n) using m+1 PEs
• Handles largest datasets of the implementations
• Uses the BLOSUM 25 and BLOSUM 50 substitution lookup tables for

evolutionary models of similarity

• Maximum GCUPS values for hardware
• HC-2: 1920 cells, 259 GCUPS
• HC-2ex: 5120 cells, 768 GCUPS

Slide 52 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Contributions

• Created a new algorithm for the ASC platform
• Implemented, tested SWAMP in the ASC language and emulator
• Showed promising results and good scaling
• Extended SWAMP to find more information between the sequences
• Created the SWAMP+ suite of algorithms

—

Single-to-multiple

—

Multiple-to-single

—

Multiple-to-multiple

• Analyzed different hardware for best fit for ASC algorithms
• Implemented the SWAMP and SWAMP+ algorithms on ClearSpeed Co-Processor
• Designed and implemented SWAMP+ adaptation on Convey Hybrid System

Slide 53 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Future Work

• Convey Computer
• Extend and update output to be more comprehensive
• Quantitative / Qualitative comparison of BLAST results against SWAMP+ results
• Timings and work analysis
• Running against MPI implementations
• Communication and system overhead impacts on cluster vs. more tightly coupled

system in addition to compute time

Slide 54 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

References

• O. Gotoh, "An improved algorithm for matching biological sequences," Journal of Molecular

Biology, vol. 162, pp. 705-708, Dec 15 1982.

• S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, "Basic Local Alignment

Search Tool," Journal of Molecular Biology, vol. 215, pp. 403-410, 1990.

• T. Rognes, "Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation,"

BMC Bioinformatics, vol. 12, 2011.

• A. Wozniak, "Using video-oriented instructions to speed up sequence comparison," Computer

Applications in the Biosciences (CABIOS), vol. 13, pp. 145 - 150, 1997.

• M. Farrar, "Striped Smith-Waterman speeds database searches six times over other SIMD

implementations," Bioinformatics (Oxford, England), vol. 23, pp. 156-161, Jan 15 2007.

• T. Rognes and E. Seeberg, "Six-fold speed-up of Smith-Waterman sequence database searches

using parallel processing on common microprocessors," Bioinformatics (Oxford, England), vol. 16,

• pp. 699-706, Aug 2000.

…Please see paper for full reference list

Slide 55 Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA - LA-UR-12-20189

U N C L A S S I F I E D

Q & A

Contact Info:

• Dr. Shannon Steinfadt

shannon@lanl.gov http://www.SwampAlign.com