GenAx: A Genome Sequence Accelerator Daichi Fujiki et al Presented - - PowerPoint PPT Presentation

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EECS 573 GenAx Paper Presentation

GenAx: A Genome Sequence Accelerator

Daichi Fujiki et al Presented by: Amani Alkayyali Ben Cyr

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EECS 573 GenAx Paper Presentation

Genome Sequencing

• DNA: Thymine, Cytosine, Adenine, Guanine
• Genome Sequencing:

Determining T,C,A,G Order

• Genome Sequencing Goals:

○ Understanding entire DNA sequence as system

Thymine Adenine Cytosine Guanine

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• fdinnerplwosiucnd

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EECS 573 GenAx Paper Presentation

Uses

• Individualized treatment

and personalized medicine

○ Understanding an individual’s cancer cell mutations

• Understanding causes of

diseases

https://rnsights.com/the-push-for-personalized-medicine/

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EECS 573 GenAx Paper Presentation

Methods

• Steps of Genome sequencing:

○ Break into small pieces (reads) at random positions ○ Determine the sequence ○ Figure out which pieces fit together (read alignment)

• Two approaches:

○ Clone-by-Clone ○ Whole Genome Sequencing

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EECS 573 GenAx Paper Presentation

Current State: Genome Sequencing and Computing

• Expensive

○ 2001: $3 billion - first human genome sequencing

• Requires several hundreds to thousands of CPU hours
• Large output

○ Data from 1 mill genomes produces over 300 Petabytes of data

• Moore’s Law tapering leads to hardware acceleration
• BWA-MEM: Burrows-Wheeler Aligner

○ Broad Institute’s standard software for read alignment

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EECS 573 GenAx Paper Presentation

Goals

• Smaller seeds → More parallelism
• Improving locality of data access
• Improve upon Smith-Waterman and Levenshtein

Automata (LA) ○ Improve scaling

• Accelerator for read alignment
• Resolve issues from variants and sequencing errors

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EECS 573 GenAx Paper Presentation

Sequence Aligners

• Edit distance: number of deletions, insertions, or

substitutions

• Seeding: finding potential matches
• Seed-Extensions: finding best match

Read Alignment

Reference Genome

Seeding Seed Extension

1 2

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EECS 573 GenAx Paper Presentation

Seeding Algorithm

• Seeding locates the potential match locations
• Finds the “seeds” for seed extension phase

○ “k-mers”: string matches of k length ○ Super Maximal Exact Matches (SMEMs): Maximum length match extending from k-mer

• Key Idea: Intersect sets of k-mers until the longest

match is found.

Seeding Seed Extension

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EECS 573 GenAx Paper Presentation

Seeding Algorithm

K = 4

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EECS 573 GenAx Paper Presentation

Seeding Algorithm

K = 4

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EECS 573 GenAx Paper Presentation

Seeding Algorithm

K = 4

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EECS 573 GenAx Paper Presentation

Seeding Accelerator

• Index and Position Tables are kept in large SRAM blocks
• Intersection computation w/ Content Addressable Memory (CAM)

○ CAMs tell you very quickly if certain data is in the CAM block ○ Small 512 index CAM table ○ When k = 12 (avg case), matches usually < 500

• If larger than 512 indices, use binary search

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EECS 573 GenAx Paper Presentation

Silla: String Independent Local Levenshtein Automata

• Seed extension algorithm
• Finite-state automata
• Traceback: trace of edits needed to align
• Scored using an affine gap function
• Insertions, deletions, substitutions
• 3D vs 2D Silla
• Merging confluence paths

Seeding Seed Extension

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EECS 573 GenAx Paper Presentation

Silla: String Independent Local Levenshtein Automata

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EECS 573 GenAx Paper Presentation

Silla: String Independent Local Levenshtein Automata

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EECS 573 GenAx Paper Presentation

SillaX: Silla Accelerator

• Edit distance, affine gap penalty, traceback
• State = processing element, communicates

with neighbor

• Retro comparison = two shift registers
• Scoring → Clipping
• Composable Subgrids
• Verified on human genome
• 62.9x speedup over Smith-Waterman

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EECS 573 GenAx Paper Presentation

GenAx

• Combine seeding accelerator and SillaX
• Direct replacement to BWA-MEM software sequence aligner

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EECS 573 GenAx Paper Presentation

GenAx Architecture

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EECS 573 GenAx Paper Presentation

GenAx Performance Test

• Compared with two other sequence aligners

○ Intel Xeon Processor running BWA-MEM (128 GB DDR4) ○ Nvidia TITAN Xp running CUSHAW2

• Synthesized and simulated GenAx with 28nm process
• Used real human genome reference from dataset

○ 800 Million reads at 101 base pairs / read

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EECS 573 GenAx Paper Presentation

Performance Results

• GenAx vs BWA-MEM

○ 31.7x Speedup ○ 12x less power ○ ~10 Hrs vs. ~300 Hrs

• Even better vs GPU

○ 72.4x Speedup

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EECS 573 GenAx Paper Presentation

Conclusion and Contributions

• Silla:

○ Computes edit distance between two strings ○ String independent and local communication

• SillaX:

○ Accelerator for Silla supporting traceback

• GenAx:

○ SillaX + Seeding Accelerator ○ Drop-In replacement for BWA-MEM software

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EECS 573 GenAx Paper Presentation

Discussion Questions

• GenAx might take large performance hits when handling certain inputs (i.e.

large K-edit distances, many “k-mer” seeds). Is it worth using GenAx even if it is not flexible enough to handle these edge cases?

• Are composable systems (many small systems to form one large system) a

good solution for scaling?

• The authors ran the performance test on one specific genome and read
• configuration. Do you think this is enough to show the usefulness of GenAx?