Improving performance of Multiple
Sequence Alignment in Multi-client
Environments
Aaron Zollman
CMSC 838 Presentation
Overview
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Overview of talk
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CLUSTALW algorithm, speedup opportunities
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Problems with caching

Parallelizing technique

Weaknesses

Applying technique to other bioinformatics problems
CMSC 838T – Presentation
Motivation
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
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Query overlap in queries submitted to MSA tools
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Single researcher: new sequences vs. database
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Multiple researchers: similar subsets
CLUSTALW: Progressive algorithm
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Three steps

Progressive refinement
Opportunities for speedup

Caching

Query ordering
CMSC 838T – Presentation
CLUSTALW: Progressive global alignment
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Step 1: Pairwise alignment, distance matrix
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Fast technique calculates distance between two scores
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Calculated for all sequence pairs
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Cost: O(q2l2)
Step 2: Guide tree
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Group nearest first
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Build tree sequentially
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Cost: O(q3)
Step 3: Progressive alignment
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Align, starting at leaves of tree
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Cost: O(ql2)
* q sequences – mean length l
CMSC 838T – Presentation
Optimization: Query caching
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Step 1: Pairwise alignment, building distance matrix
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Many requests partially duplicated
Individual distance calculation not dependent on rest of query
Observation: Dominant step in execution time
MLI…
MLISHSDLNQ…
GISRETSS…
GIS…
MLI…
MLISHSDLNQ…
0.0
0.0
GISRETSS…
Steps 2, 3:

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Output dependent on results of entire query
Results less reusable
Technique: cache output of step 1
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Individual distances
CMSC 838T – Presentation
GIS…
MST…
0.0
MSTVTKYFYKGE…
Query 1

Query 2
0.0
QPAKKTYTW…

QPA…
0.0
0.0
Challenges to cache implementation
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I/O and filesystem overhead
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Large cache vs. 2GB file size limit
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High seek times within single file
Search and insertion overhead
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Sequence: lengthy key

Keyed on each pair of sequences
CMSC 838T – Presentation
Technique: 2-level B-Tree cache
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Level 1: Map sequence text to sequence ID
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Hash of sequence?
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Sequentially assigned number
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Cache size: O(ql)
Level 2: Map ID pairs to calculated distance
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Concatenate IDs from level 1
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Lower Level 1 ID -> upper half of Level 2 key
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Cache size: O(q2)
Distribute level 2 cache across bins
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Round robin or block allocated
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Distribute bins across machines
* q sequences – mean length l
CMSC 838T – Presentation
SMP

Parallelizable:
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Pairwise searches performed independently
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Farmed out to query threads
Web server
Query
Thread
Query
Thread
Query
Thread
Level 1 maps
(per-machine)
Level 2 bins
(distributed)
CMSC 838T – Presentation
Query
Thread
SMP

Challenge: Cache coherence
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Read-only?
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Requires advance knowledge of query details
Online update and serialization?
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Locking, duplicate updates
Offline updates?



Per-thread list of cache changes
Query
Thread
Query
Thread
Query
Thread
Level 1 maps
(per-machine)
Level 2 bins
(distributed)
CMSC 838T – Presentation
Query
Thread
Evaluation: Implementation
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Public B-Tree implementation: GIST library
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First evaluation on Intel PC
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
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(Pentium III 650, 75GB disks)
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q = 25-1000 sequences
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l = 450 amino acids per sequence
Second evaluation on Sun Fire
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(Sun Fire 6800, 48*750MHz CPUs, 48GB main memory)
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l = 417 amino acids per sequence
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q = 2-200 sequences
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Seeded cache with dummy values
Future work: architectural impact
CMSC 838T – Presentation
Evaluation: Results
CMSC 838T – Presentation
Observations
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Simple technique
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Cheap and easy to implement
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Cheap and easy to deploy
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Unsupported claim: Are queries really similar?
Concern about distribution across processors
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Paper mentions latency, workload balancing
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Also reliability of distributed bins
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Cache lifetimes?
Proposed solution “component-based system”
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“Hand-wavey”; would like to see more.
CMSC 838T – Presentation