A MillionFold Speed Improvement in Genomic Repeats Detection

1
A Million-Fold Speed Improvementin Genomic
Repeats Detection

• John W. Romein
• Jaap Heringa
• Henri E. Bal

• Vrije Universiteit
• Faculty of Sciences, Department of Computer
  Science
• Bio-Informatics Group Computer Systems Group
• Amsterdam, the Netherlands

Vrije Universiteit, Amsterdam
2
repeats in bio sequences

• important to detect
• essential for evolution
• protein structure function
• diseases
• hard to detect
• any length
• mutations
• insertions/deletions ? different fragment sizes
• tandem and distant

3
repro

• delineates repeats
• sensitive
• two phases
• find top alignments (slow)?
• find repeats
• replaced phase 1
• old algorithm
• O(n4) ? n lt 2,000
• new algorithm
• O(n3) ? n lt 60,000
• 3-level parallel SIMD, SMP, cluster

4
sidestep sequence alignment

• superpose two sequences (TATGCAG, TCTGAG)?
• match symbols vertically (good 2, bad -1)?
• allow gaps (-2-1length)?
• maximize score
• compute matrix using dynamic programming

5
sidestep local alignment

• Find sub-sequences that match well
• Ignores non-matching values before and after the
  subsequence (by disallowing negative values)
• Construct actual alignment O(n3) time
• Computing only the scores O(n2) time
• (see paper)

6
summary

• (TATGCAG, TCTGAG) gt 6
• takes O(n2) time
• (TATGCAG, TCTGAG) gt
• takes O(n3) time
• Matching ltjunk1gt TATGCAG ltjunk2gt with
  ltjunk3gt TCTGAG ltjunk4gtgives same result as
  matching only the substrings TATGCAG and TCTGAG

7
finding topalignments

• red lines top alignments
• split sequence every possible way
• align subsequence-pair
• best is first top alignment
• trick find next best (top) alignment using
  O(n2) algorithm n times construct topalignment
  using O(n3) algorithm
• repeat while avoiding found top alignments
• user typically wants 5-30 top alignments
• ordered list, do most promising alignments first
• realign 3-10

8
performance old vs. new

• sequence longest known protein (titin)?
• speed improvement increases with sequence length

9
parallel alignment

• parallelism within alignment
• loop-carried dependency
• concurrent alignments
• speculative parallelism
• good performance
• three-level parallelism
• SSE/SSE2 multimedia extensions (SIMD)
• shared memory MIMD
• distributed memory MIMD

10
SIMD parallelism

• multimedia extensions
• 4 (SSE) or 8 (SSE2) parallel operations on
  consecutive 2-byte words
• compiler intrinsics
• compute 4 (or 8) neighboring matrices
  concurrently
• interleaved memory layout
• use fine-grained hardware for coarse-grained
  computation
• applicable to any program that does many
  alignments

11
SSE/SSE2 performance

• speedups w.r.t. new algorithm
• superlinear speedups
• MAX operator
• 8 extra mmx/xmm registers
• scheduling
• cache-aware alignment 4 6.5 times faster

12
MIMD parallelism

• SIMD (SSE) parallelism is speculative
• If a matrix (alignment) is promising, its
  neighbors probably also are promising
• MIMD parallelism
• use dynamic task scheduling, selecting most
  promising tasks from a job queue
• Shared memory (SMP) easy
• Distributed memory MPI, master/worker

13
total parallel performance

• SMP 2 CPUs ? 2 2 times faster
• cluster 642 CPUs ? 548 889-fold speedup
• Up to 125x faster than SSE version on 1 CPU

14
conclusions

• new algorithm gtgt 100 times faster
• much more for longer sequences
• parallel SSE(2), SMP, cluster
• SSE(2) parallelism yields superlinear speedups
• 128 CPUs 548 889-fold speedup
• 1,000,000-fold speed improvement