Undergraduate ProjectAdvisor : Prof. Subodh Kumar

Chirag Jain (2010CS10215)IIT Delhi

Parallelizing Smith Waterman Algorithm on

GPUs

OUTLINE

• Smith Waterman Problem

• Parallelization [A. Khajeh-Saeed et al. ]

• Reproduced Results

SW Problem

Local Alignment:

A T C A G A G T CG T C A G T C A

SW Problem

A T C A G A G T CG T C A G - - T C A

Local Alignment:

A T C A G A G T CG T C A G T C A

SW Problem

SW ProblemRecursion :

Hi,j = Score at cell (i,j)Si,j = Match / Mismatch ScoreGs = Gap opening penalty

Ge = Gap extension penalty

SW Problem

Time Complexity : O(L1L2(L1+L2))

L1 = Length of Database SequenceL2 = Length of Database Sequence

SW Problem

Time Complexity : O(L1L2)

L1 = Length of Database SequenceL2 = Length of Database Sequence

Store three values at each cell :

Ei,j = Max {Ei,j-1, Hi,j-1 - Gs} - Ge

Fi,j = Max {Fi-1,j, Hi-1,j - Gs} - Ge

Hi,j = Score at cell (i,j) = Max {Hi-1,j-1 + Si,j, Ei,j, Fi,j}

SW Problem

Result:GCC- UCGCGCCAUUGC

SW Problem

Optimization

Parallel Scan Approach

[A. Khajeh-Saeed et al. 2010]

Parallel Scan Approach

Step 1

Fi,j = Max {Fi-1,j, Hi-1,j - Gs} - Ge~Hi,j = Score at cell (i,j) = Max {Hi-1,j-1 + Si,j, Fi,j}

Parallel Scan Approach

Step 2

~Ei,j = Maxi<k<j (~Hi,j-k - kGe)

Parallel Scan Approach

Step 3

Hi,j = Max (~Hi,j , ~Ei,j - Ge)

Parallel Scan ApproachStep 2 (Revisited)

Parallel Scan ApproachStep 2 (Revisited)

Results

• SSCA Benchmark

• Kernel 1 : Pairwise Local Alignment of Sequences

• Kernel 3 : Locating similar sequences

Results

• Using:

• Single Core of Intel Xeon CPU

• GPUs : Tesla M2070

1.E+00&1.E+01&1.E+02&1.E+03&1.E+04&1.E+05&1.E+06&1.E+07&

1.E+07&

3.E+07&

6.E+07&

2.E+08&

4.E+08&

1.E+09&

3.E+09&

6.E+09&

2.E+10&

Time%(m

s)%

Input%Size%

Time%taken%by%Kernel%1%

CPU&2.67GHz&

GPU&M2070&

Results

0"5"10"15"20"25"30"35"40"

1.E+07"

3.E+07"

6.E+07"

2.E+08"

4.E+08"

1.E+09"

3.E+09"

6.E+09"

2.E+10"

Factor'

Input'Size'

Speed'Up(over'CPU)':'Kernel'1'

GPU"M2070"

Results

1.E+00&1.E+01&1.E+02&1.E+03&1.E+04&1.E+05&1.E+06&1.E+07&

1.E+07&

3.E+07&

6.E+07&

2.E+08&

4.E+08&

1.E+09&

3.E+09&

6.E+09&

2.E+10&

Time%(m

s)%

Input%Size%

Time%taken%by%Kernel%3%

CPU&2.67GHz&

GPU&M2070&

Results

0"

5"

10"

15"

20"

25"

1.E+07"

3.E+07"

6.E+07"

2.E+08"

4.E+08"

1.E+09"

3.E+09"

6.E+09"

2.E+10"

Factor'

Input'Size'

Speed'Up(over'CPU)':'Kernel'3'

GPU"M2070"

Results

Results

Over multiple GPUs

0"1"2"3"4"5"6"7"8"

1.E+07"

3.E+07"

6.E+07"

2.E+08"

4.E+08"

1.E+09"

3.E+09"

6.E+09"

2.E+10"

Factor'

Input'Size'

Kernel'1:'Speed'Up'over'Single'GPU'

Two"M2070"

Four"M2070"

Eight"M2070"

Sixteen"M2070"

Results

0"1"2"3"4"5"6"7"8"

1.E+07"

3.E+07"

6.E+07"

2.E+08"

4.E+08"

1.E+09"

3.E+09"

6.E+09"

2.E+10"

Factor'

Input'Size'

Kernel'3:'Speed'Up'over'Single'GPU'

Two"M2070"

Four"M2070"

Eight"M2070"

Sixteeen"M2070"

Results

References

• A. Khajeh-Saeed et al. , Acceleration of the Smith–Waterman algorithm using single and multiple graphics processors, J. Computational Physics 229 (2010) 4247–4258

• "Designing Scalable Synthetic Compact Applications for Benchmarking High Productivity Computing Systems ," CTWatch Quarterly, Volume 2, Number 4B, November 2006 B.

Thank You