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Performance Optimization of Clustal W:
Parallel Clustal W, HT Clustal and
MULTICLUSTAL
Arunesh Mishra
CMSC 838 Presentation
Authors : Dmitri Mikhailov, Haruna Cofer, Roberto Gomperts
SGI
Problem Statement
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Multiple Sequence Alignment (MSA)
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Goal : Parallelize Clustal W
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Basis for phylogenetic analysis - Infer homology relationships
Building protein families - conserved region may imply common function
Aids in function/structure prediction of new proteins
Global MSA – Clustal W
Is it computationally expensive ? Yes, for 100 sequences.
Clustal W takes hours for 100 or more sequences
Parallelization possible for the algorithm
Contribution of the paper
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Parallel Clustal W
 Parallel version of basic Clustal W
HT Clustal
 Parallelize heterogeneous Multiple Sequence Alignment problems
MULTICLUSTAL
 Parallel version of an optimization on Clustal W
CMSC 838T – Presentation
Talk Overview
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Overview of talk
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Motivation
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Background
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Sequential Clustal W
Parallel Clustal W
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HT Clustal
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Problem Statement
 Optimizations
MULTICLUSTAL
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Sequential Algorithm
 Optimizations
Observations
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CMSC 838T – Presentation
Introduction
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Sequential Clustal W Algorithm
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Given N sequences of length M each
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Pairwise Alignment (PA)
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Creates distance matrix N x N based on pairwise alignment
scores
 Evolutionary distance
Guide Tree (GT) construction (Phylogenetic tree)
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Use Neighbor-joining algorithm
Progressive Multiple Alignment (PA)
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Use guide tree to align closely related pairs of sequences
Progressively align next sequence to existing alignment
CMSC 838T – Presentation
Parallel Clustal W
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Problem Statement
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Parallelize the Sequential Clustal W
Execution time breakup
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PW = pairwise alignment, GT = guide tree, PA = progressive alignment
CMSC 838T – Presentation
Parallel Clustal W
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Pairwise Alignment Stage
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N(N-1)/2 pairwise alignments
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Send them randomly to different processors
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Random – as jobs of different load
 Random also produces statistically uniform distribution
(over a large set of jobs)
1.8X speedup achieved on a 1000 sequence MSA with 8 CPUs
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Guide Tree Stage
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Parallelize “find closest neighbors from distance matrix”
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Used in the neighbor joining algorithm
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Find minimum element of each row concurrently
Use this to find minimum element of matrix
CMSC 838T – Presentation
Parallel Clustal W
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Progressive Alignment Stage
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Computation of a function score(I,J) precomputed in parallel
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Alignment score of sequence I and J
Not much parallelization in the third stage
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Overall Speedup
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Speedup of 10x for 600 MA sequences using 16 CPUs
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Time reduced from 1 hr 7
minutes to 6.5 minutes
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Relative scaling is
better for larger inputs
CMSC 838T – Presentation
HT Clustal
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Problem Statement
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Calculate large numbers of MSAs of various sizes (independent
problems)
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Such problems seen in high-throughput (HT) research
environments
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Representative Problem (from paper) :
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Perform independent MSA over
100 sets of sequences
Each set has between 20 to
100 sequences with average
of 60 sequences
Average Length
of sequence = 390
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CMSC 838T – Presentation
HT Clustal - Optimizations
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Basic Idea
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Each MSA operation (on one set of sequences) is independent
of the other
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Run ClustalW as a uniprocessor job on one MSA problem
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Launch multiple Clustal W jobs on different processors
Job Scheduling
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Jobs of different duration – depends on sequence set
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Two scheduling options explored:
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Schedule dynamically – if processor is free, schedule an
MSA job – chosen randomly
Schedule dynamically – Sequences are presorted (based on
filesize)
CMSC 838T – Presentation
HT Clustal – Performance Numbers
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Speedups
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Almost linear speedups
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31x on 32 CPUs for the representative MSA problem
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116X on 128 CPUs for a larger test case
Solution time reduced from 18.5 hours to 9.5 minutes
Speedup shown for the example MSA set:
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CMSC 838T – Presentation
HT Clustal – Effect of Presorting
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Effect of presorting
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Figure shows effect of
presorting for the example
MSA set
32 CPUs, 100 sets,
~3 jobs per CPU
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If average number of
jobs per CPU < 5
presorting helps
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For larger number of jobs
per CPU statistical averaging
reduces load imbalance
CMSC 838T – Presentation
MULTICLUSTAL
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MULTICLUSTAL Algorithm
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A Perl script to generate high quality MSA with little user intervention
Searches for best combination of Clustal W input parameters
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To reduce gaps, increase clustering
Parameters to vary :
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Scoring matrices : pairwise and multiple
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Gap open and extension penalties (pairwise and multiple)
Sequential Algorithm :
Till all parameters are sufficiently varied {
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alignment = Run Clustal W ()
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Calculate quality of alignment
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Change Parameters }
Quality of alignment
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A numerical quantity based on
1.
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identitical amino acid matches
Conservative amino acid substitutions
Gap events, amino acid islands I.e. –X-, -XX-, -XXX-, -XXXX-
CMSC 838T – Presentation
MULTICLUSTAL Optimizations
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Optimization on MULTICLUSTAL
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Run Clustal W once
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Reuse tree generated in the PW/GT Stages
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Guide tree calculated only once for multiple runs
 Results in speedups from 1.5X to 3X
Use Parallel Clustal W for each run of Clustal W
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CMSC 838T – Presentation
Observations
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Parallelizability
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First (pairwise alignment) and second (guide tree) stages are
parallelizable
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Third stage is mostly sequential – speedup limited
100 sequence MSAs possible ?
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PIR at NBRF (Georgetown University) takes maximum of 20 sequences
for MSA
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Speedup improves user response, for 20 sequences a PC would be
sufficient
Probable applications:
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Research Environments ?
PIR servers ?
Speedup only on shared memory SGI 3000 workstation ?
CMSC 838T – Presentation