Biosequence Similarity Search on the Mercury System Praveen Krishnamurthy, Jeremy Buhler, Roger...

Biosequence Similarity Search on the Mercury System

Praveen Krishnamurthy, Jeremy Buhler, Roger Chamberlain, Mark Franklin, Kwame Gyang, and

Joseph LancasterDepartment of Computer Science and Engineering,

Washington University in Saint Louis, MO

Supported by an NIH STTR Grant &NSF Grants DBI-0237902, ITR-0313203, CCR-0217334

Washington University in St. Louis Slide # 2

Outline

• Overview of BLAST• Overview of the Mercury system• Description of BLASTN algorithm• Algorithmic changes to BLASTN• Improvement in performance• Related work• Conclusion


Basic Local Alignment Search Tool

• Biosequence comparison software– Query sequence (new genome) to large

database of known biosequences• Look for similar regions

• Exponential growth of genomic databases– Longer time for searches to complete– Solutions

• Perform comparison over multiple machines• Specialized hardware - Our Approach


The Mercury System


The Mercury System

• Proximity to disk– Simple operations performed close to disk

• Avoids CPU use– 400 Mbytes/s throughput from the disk

• Concurrent Independent operation– Does not use processor cache cycles,

memory or I/O buses• Reconfigurable logic

– Logic can be tuned to the particular need of the application


BLASTN

• BLASTN– Both the query and the database are long DNA strings– Consist of {A, C, T, G} and some unknowns

• Each stage processes lesser data• The stages become more computationally

expensive


BLASTN - Terminology

…ACTGTGTTTCACTGACGGGTGT…

…CTGTGTCCCCAACACTGCTGACGTAGAATCGTGTAG…

Query

Database

‘w-mer’ is a sequence of ‘w’ consecutive bases


BLASTN - Pipeline - Stage 1

• Matches each ‘11-mer’ in query to database– Exact string matching

• 83% of overall time is spent in this stage• Filters 92% of data entering this stage

– Only 8% of data proceeds to the next stage



• Extends the matches from stage 1…ACTGTGTTTCACTGACGGGTGT…

…GTGTCCCCAACATTTCACTGACGAGAATCGTGTAG…



• Extends the matches from stage 1– Allows mismatches of individual bases– Does not allow gaps in either the query or the database– Match score should be higher than threshold to proceed

• 16% of pipeline time is spent is this stage• Only 2/100,000 of data entering this stage proceeds to the

next stage



• Extends the matches from stage 2…ACCACTGTTTCACTGACG_GA_T_GT…

…CTGTGTCCCCAC_GTTTCACTGACGAGAATCGTGTAG…



• Extends the matches from stage 2– Scores matches with Gaps inserted in both the

sequences– Smith-Waterman dynamic programming algorithm

• <1% of pipeline time is spent is this stage


NCBI - BLASTN

• Stage 1 (word matching) is implemented as a lookup table– Efficient only for certain word lengths (w=

11)• Performance degrades dramatically for

larger query sizesQuery Size

10 Kbases

100 Kbases 1 Mbases Units

Throughput 66.9 8.76 0.657 Mbases/s

Pentium-4 2.6GHz 1Gbyte RAM


Firmware implementation - Stage 1

BloomFilters

HashLookup

RedundancyEliminator

Eliminates false-positivesfrom Bloom filters,

obtain offset in query

Discards matches that are close to one another

Matches ‘11-mers’ to query, but generates false-positives


Bloom filters operation

‘11-mer’

KHash

Functions

Programming the query into the bloom filter (processing query)

‘m-bit’ vector

1

1

1

query



database KHash

Functions

Finding matches in the database

‘m-bit’ vector

?

?

?

1: Potential match

0: Not a match

‘11-mer’



KHash

Functions

Finding matches in the database

‘m-bit’ vector

?

?

?

1*: Potential match

0: Not a match

* False positives are eliminated using a hash table

database‘11-mer’


0.0001

0.001

0.01

0.1

1

50 250 500 750 950Memory (KB)

False Positive Rate

50K Bases100K Bases200K Bases500K Bases1M Bases

Bloom filter performance


Performance analysis

Firmware Vs. Software Stage 1

Query Size

10 Kbases

100 Kbases

1 Mbases

Units

NCBI BLASTN 77.4 10.5 0.771 Mbases/s

Mercury BLASTN 800 800 80 Mbases/s

Speedup 10.3 76.1 104


Overall system throughput

Query Size 10 Kbases 100

Kbases 1 Mbases Units

NCBI BLASTN 67.0 8.76 0.657 Mbases/s

Mercury BLASTN 497 52.6 4.47 Mbases/s

Speedup 7.42 6.01 6.80

Tputoverall = min (Tput1, Tput(2&3))


Stage 2 in firmware - Throughput

1

10

100

1000

5 10 15 20 25Stage 2 speedup

Overall system throughput

(Mbases/sec)

10 Kbase query100 Kbase query1 Mbase query

A


Stage 2 in firmware - Speedup

0

20

40

60

80

100

120

140

5 10 15 20 25Stage 2 speedup

Overall system speedup

10 Kbase query100 Kbase query1 Mbase query

B


Related work• Hardware based commercial systems

– Paracel GeneMatcherTM, used ASIC, and hence is inflexible

– RDisk, FPGA based system with throughput of 60 Mbases/s for stage 1

• High-end commercial system– Paracel BLASTMachine2TM, 32 CPU linux cluster

• 2.93 Mbases/s for 2.8 Mbase query• 2 times faster than 1-node Mercury BLASTN

– TimeLogic DeCypherBLASTTM, FPGA based• 213 Kbases/s for a 16 Mbase query• Comparable to 1-node Mercury BLASTN


Conclusion

• BLASTN on the Mercury system– Bloom filters to improve performance of stage 1

• Efficient hash functions in hardware

– 7x improvement in speed with only stage 1 firmware

– >50x speedup with stage 2 implemented in firmware

• Future work– Algorithmic changes to stage 2

• Efficient use of hardware capabilities

– Other apps• BLASTP, BLASTX etc.


Thank you

Biosequence Similarity Search on the Mercury System Praveen Krishnamurthy, Jeremy Buhler, Roger...

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