Darwin: A Genomic Co-processor gives up to 15,000X speedup on long read assembly (To appear in ASPLOS 2018)

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1 Darwin: A Genomic Co-processor gives up to 15,000X speedup on long read assembly (To appear in ASPLOS 2018) Yatish Turakhia EE PhD candidate Stanford University Prof. Bill Dally (Electrical Engineering and Computer Science) Prof. Gill Bejerano (Computer Science, Developmental Biology and Pediatrics)

2 DNA sequencing costs and data explosion 1 st gen 2nd gen 3rd gen Key players: 1. Illumina/Moleculo 2. Pacific Biosciences 3. Oxford nanopore 4. Ion Torrent (ThermoFisher) 5. Roche/Genia 6. Veritas Genetics 7. 10x Genomics and many others. Since 2003, genomics data doubling every 7 months! Exabyte data by M to 2B genomes to be sequenced! Stephens, Zachary D., et al. "Big data: astronomical or genomical?." PLoS Biology (2015) Illumina Hiseq X Oxford Minion 2

3 DNA sequencing costs and data explosion [phys.org/news] Uganda Antartica Oxford Minion 3

4 Patient Diagnosis: Sample-to-answer Patient Reads 1 ATGTCGAT CGATACGA GAGTCATC ACTGACGT 2 Read assembly Genome (3 Billion base pairs) REFERENCE:--ATGTCGATGATCCAGAGGATACTAGGATAT- PATIENT: --ATGTCTATGATC--GAGGATATTAGGATAT- Mutations 3 Genome Sequencing Machine Find the causal mutation Long reads (>10Kbp) offer a better resolution of the mutation spectrum but have high error rate (15-40%) >1,300 CPU hours for reference-guided assembly of noisy long reads 14.2M CPU-years for 100M individuals >15,600 CPU hours for de novo assembly of noisy long reads 178M CPU-years for 100M individuals 4

5 Darwin: A Genomics Co-processor Query (Q) D-SOFT Reference (R) D-SOFT (filter) D-SOFT API Darwin GACT (aligner) GACT API Query (Q) GACT Software Aligner Reference (R) High speed and programmability 1. D-SOFT: Tunable speed/precision to match any error profile 2. GACT: First algorithm with O(1) memory for computeintensive step of alignment allowing arbitrarily long alignments in hardware ideal for long reads 3. First framework shown to accelerate reference-guided as well as de novo assembly of reads in hardware 5

6 6 GACT algorithm and hardware design

7 Strategies for long sequence alignment Algorithm Time Space (compute-intensive step) Optimal Smith-Waterman O(mn) O(mn) Y Hirschberg O(mn) O(m+n) Y Banded Smith- Waterman O(n) O(n) N X-drop O(n) O(n) N GACT O(n) O(1) N m, n: sequence lengths m >= n Profound hardware design implications Prior assumptions (hardware) Small upper bound on sequence length n OR Trace-back of alignment in software SLOW! 7

8 Smith-Waterman Alignment } Given: Reference (R) and size m and Query (Q) of size n } Two step procedure 1. Compute scores H of every cell in the dynamic programming (DP) matrix using Smith- Waterman equations and scoring matrix W 2. Traceback from high-scoring cells to obtain final alignments Smith-Waterman equations Scoring Matrix W A C G T A C G T gap = 1 Query (Q) Reference (R) * G G C G A C T T T * G G T C G T T T G G - C G A C T T T G G T C G - - T T T

9 Genome Alignment using Constant-memory Trace-back (GACT) 1. Initialize I curr, J curr in R, Q 2. Form tile of maximum size T around I curr, J curr in R, Q 3. Align tile with trace-back from I curr, J curr with at most (T-O) steps 4. Update I curr, J curr with traceback end coordinates 5. Repeat 2-4 till extension no longer possible Query (Q) * G G T C G T T T Reference (R) * G G C G A C T T T Tile 1 Tile 3 T = 5, O=2 Tile 2 (I curr, J curr ) (I curr, J curr ) Optimal Alignment G G - C G A C T T T G G T C G - - T T T Score = 11 Alignment G G - C G A C T T T G G T C G - - T T T Score = 11 9

10 GACT empirically provides optimal alignments } GACT compared to optimal Smith-Waterman for 200,000 10Kbp sequences with 3 error profiles: PacBio (15% error), ONT_2D (30% error) and ONT_1D (40% error) } Simple scoring (match: +1, mismatch: -1, gap: -1) } At T=320, O=128, all observed alignments were optimal Optimal (T, O) settings Hardware throughput 10

11 GACT Hardware-acceleration Reference A C T A A G G T C G G T A T = 9 PE 0 PE 1 PE 2 PE 3 G C T G A G T Query Block 1 SRAM SRAM SRAM SRAM Query C A C T Query Block 2 A TB Logic T Query Block 3 } Systolic array of N pe (= 4) processing elements (PEs) solve Smith-Waterma equations } Tile with size T > N pe, query divided into blocks, reference streamed through each block } Computation exploits wave-front parallelism } On-chip SRAM for storing trace-back state (4-bit per cell) } Total SRAM size = 4-bit x (T max ) 2 => For T max = 512, SRAM size = 128KB 11

12 Darwin: GACT Performance #Arrays = 64 #PEs/array (N pe ) = 64 Runtime scales linearly to sequence length X faster than Edlib (fastest software alignment library) >75,000X faster than software implementation of GACT 12

13 13 D-SOFT algorithm and hardware design

14 Seed Position table based exact matching R = AGCTATACTA Seed Positions AA AC 6 AG 0 AT 4 CA CC CG CT 2 7 GA GC 1 GG GT Q = GCTA Q GC:1 CT: 2, 7 TA: 3, 5, 8 Slope= R TA TC TG For human genome, seed position table size > 12GB (4B x 3 x 10 9 ) TT 14

15 Diagonal-band Seed Overlapping based Filtration Technique (D-SOFT) Query (Q) Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Reference (R) N B = 6 N = 10 k = 4 h = 7 } Divide R into N B bins (diagonal bands) } Use N seeds of size k bp from different offsets in Q } Lookup positions of seeds in R and assign each seed hit to corresponding bin (diagonal band) } Count non-overlapping Q base-pairs covered by seed hits for each bin and filter based on threshold h (same as DALIGNER) 15

16 Tuning D-SOFT settings Sensitivity (k=12, N=1000, h=25) False Hit Rate (k=12, N=1000, h=25) Data shown for ONT_2D reads with GraphMap used for Baseline sensitivity 16

17 D-SOFT hardware-acceleration design Random accesses to update bins using on-chip SRAM (bin count SRAM) Area and power both dominated by 64MB Bin count SRAM Hardware exploits DRAM channel parallelism for seed position lookup 17

18 Darwin: TSMC 40nm ASIC configuration Component Configuration Area Power 18 GACT D-SOFT 64 arrays x 64PEs 2KB memory/pe 2SPL + NoC + 16 UBL 64MB Bin-count SRAM 4MB NZ-bin SRAM W W DRAM 4 x 32GB W TOTAL W

19 D-SOFT hardware-acceleration throughput k Avg. hits per seed (Human Genome) Throughput (10 3 seeds/sec) Software Darwin Darwin speedup , X , X , X , X , , X } ~4X speedup from parallel DRAM channels } ~3X reduction in number of memory accesses to the DRAM } ~3-8X speedup by using predominantly sequential memory accesses to DRAM 19

20 20 Long read assembly on Darwin

21 Darwin: Read assembly Reference-guided De novo (Overlap step) 21

22 Darwin: Performance Results Reference-guided (54X human genome) Read Error Rate D-SOFT settings (k, N, h) Baseline Sensitivity Darwin Speedup 15% (14, 750, 24) 95.95% 99.91% 9,916X 30% (12, 1000, 25) 98.11% 98.40% 15,062X 40% (11, 1300, 22) 97.10% 97.40% 1,244X Baseline: BWA-MEM (15%), GraphMap (30%, 40%) De novo (54X human genome) Read Error Rate D-SOFT settings (k, N, h) Baseline Sensitivity Darwin Speedup (Bottleneck) 15% (14, 1300, 24) 99.80% 99.89% 710X Baseline: DALIGNER 22

23 Darwin: Hardware-Software Co-design 23

24 Thank you! Questions 24