Parallelizing DNA Read Mapping Sunny Nahar What is DNA Sequencing? - - PowerPoint PPT Presentation

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Parallelizing DNA Read Mapping Sunny Nahar What is DNA Sequencing? - - PowerPoint PPT Presentation

Oct 02, 2022 145 likes •432 views

Parallelizing DNA Read Mapping Sunny Nahar What is DNA Sequencing? Finding the base-pairs for the genome. Why is this useful? Tracing evolution. Correlating genes with diseases. Forensics and identification. Current Technology

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Parallelizing DNA Read Mapping

Sunny Nahar

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What is DNA Sequencing?

Finding the base-pairs for the genome.

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Why is this useful?

  • Tracing evolution.
  • Correlating genes

with diseases.

  • Forensics and

identification.

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Current Technology (Shotgun)

  • Split DNA into small pieces (reads).
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Read Mapping

  • Have access to a reference genome.
  • Align reads to reference.
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Computationally Challenging

  • Billions of reads.
  • Fuzzy matching.

○ Handle insertions, deletions, mutations, errors.

  • Multiple mapping locations.
  • Assemble with high probability.
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How do we map a read? (Seed-and-extend Method)

  • Match substrings (seeds) exactly

to the reference.

○ Possible locations.

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How do we map a read? (Seed-and-extend Method)

  • Use edit distance to

determine quality.

○ Dynamic Programming (score based)

■ Needleman-Wunsch ■ Smith-Waterman

  • Choosing less frequent

substrings is important.

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Research Project

  • Improve the speed of the mapper (aligning reads).
  • Develop novel algorithms and heuristics.
  • Low complexity, memory efficient, cache efficient.
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This Presentation

  • Focuses on one part of the pipeline: Seed Selection.

○ Given set of reads, output seeds. ○ These are used to find potential mapping locations.

  • Discuss parallel optimizations and improvements to runtime.
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Parallelizing the Infrastructure

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DNA Read Processing Pipeline

  • 1. Generating the HashTree representation of genome.
  • 2. Building a frequency predictor.
  • 3. Performing seed selection.
  • 4. Pipe results into next stage (edit distance).
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Machine Specs

Test machine:

  • 4 sockets.
  • 10 cores and 256GB RAM (NUMA) per socket.
  • 2 hardware threads per core (Intel Hyperthreading).

○ Memory latency.

  • In total, 1TB RAM with 80 logical threads.
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Generating the HashTree

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Genome Representation

  • Hashtable of Frequency Tries.

○ Each node stores a character and frequency. ○ Hashtable on first 10 letters.

  • Bounded (length 30)
  • ~80GB on disk.
  • String frequency queries.
  • L cache misses.
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Generation in Parallel

  • Reading from disk is sequential.

○ Threads take turn reading. ○ Memory mapped IO removes need for explicit copying. ○ Copied to Kernel page cache as opposed to user memory. ○ Incur TLB misses vs cache misses.

  • Each trie can be independently generated.

○ Only issue is memory allocator. ○ Traditional malloc has a lock. ○ Only need alloc (not free). ○ Implement own allocator which is locality aware. ○ Was initial bottleneck. (2hrs to 10 minutes)

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Generation in Parallel

  • Dynamic work scheduling + greedy allocation.

○ Trie sizes are highly nonuniform. ○ Schedule largest tries first to balance workload. ■ Estimate from filesize.

  • Kernel aware access policies.

○ TLB linear access. ○ File linear access.

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Speedup Graph

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Frequency Predictor

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What is the Frequency Predictor?

  • Access to HashTree is costly (L cache misses)

○ Instead, give an estimated frequency.

  • Reduces to 1 cache miss.
  • Store a table:

○ table[base][L][R] -> base (10 letters) extended to left by L letters, right by R letters.

  • Example: AGCTGACG ATGCTAGCTA GCTCG

○ Lookup table[ATGCTAGCTA][8][5]

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Construction of Predictor

  • Requires traversing through the entire HashTree.
  • Updating a large table

○ Synchronization at same entries. ○ Accomplished with atomic writes.

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Speedup Graph

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Seed Selection

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What is Seed Selection?

  • Given input set of reads, output set of seeds for each read.

○ Based on input parameters. ○ GCAGTCAGTCGATCGATCGATCGTACGTACGTACAGCTAGC TA

  • Algorithms use mix of accesses to HashTree and predictor to

determine seeds.

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Parallelization

  • Selection is parallelized over reads.

○ Per thread data structures, reduced at end.

  • Both the HashTree and Predictor are loaded in memory.

○ Generally sparse accesses. ○ Cache write coherence is not an issue, since only read access to memory. ○ Cache reads create coherence traffic (costly on socket architecture).

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Parallelization

  • Stack memory vs malloc.
  • NUMA degrades performance.

○ Threads closest to HashTree, Predictor perform much faster. ○ Observed up to 2x overhead.

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Speedup Graph

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Questions