Accelerating Genomic Computations 1000X with Hardware

Transcription

1 Accelerating Genomic Computations 1000X with Hardware 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 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) 2nd gen 3rd gen Storing and processing genome data will exceed the computing challenges of running YouTube and Twitter, biologists warn. [Nature News, 2015] The decreasing cost of sequencing and the increasing number of sequence reads being generated are placing greater demand on the computational resources and knowledge necessary to handle sequence data. [Genome Biology, 2016] 2

3 Genomic Granular Computing Applications Neonatal ICU 4 million newborns per year in the US alone 1 in 33 newborns with rare genetic conditions admitted to NICU Time of essence for genome-based diagnosis Non-invasively diagnose for over 3,000 rare genetic conditions (e.g. Down Syndrome) Free-floating DNA in blood enormous volume! Prenatal ICU and IVF clinics 3 Liquid Biopsy Early cancer detection life-saving application for millions of individuals Non-invasive circulating tumor DNA Periodic sequencing of healthy individuals - enormous volume!

4 Patient Diagnosis: Sample-to-answer Patient Reads 1 2 ATGTCGAT CGATACGA GAGTCATC ACTGACGT 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 Darwin: 40nm ASIC configuration LPDDR4 (32GB) LPDDR4 (32GB) Software D-SOFT API GACT API Darwin D-SOFT GACT GACT GACT GACT GACT GACT GACT GACT Software (Intel Xeon E5) Algorithm Power (1 thread) BWA-MEM 9.2W GraphMap 10.7W DALIGNER 8.8W Area: 300mm 2 Power: 9W 6

7 7 GACT algorithm and hardware design

8 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! 8

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-D) 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, D=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 tile size T=400 } GACT compared to optimal Smith-Waterman for 200,000 10Kbp sequences with 4 different error rates: 10%, 20%, 30% and 40% } Simple scoring (match: +1, mismatch: -1, gap: -1) } At D=120, all observed alignments were optimal D (in bp) 10 Fraction alignments nonoptimal Worst-case score loss 10% 20% 30% 40% 10% 20% 30% 40% % 61.0% 83.0% 94.7% 0.29% 0.67% 1.26% 2.38% % 0.02% 0.55% 55.3% 0.0% 0.35% 0.63% 1.59% % 0.0% 0.01% 1.38% 0.0% 0.0% 0.34% 0.81% % 0.0% 0.0% 0.05% 0.0% 0.0% 0.0% 0.33% % 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

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-Waterman-Gotoh } 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 => 128KB for T max =

12 Darwin: GACT Performance K GACT (Software) Edlib GACT (Darwin) X 108K 54K Alignments/sec X X 19X 986X 11X Sequence length (Kbp) Runtime scales linearly to sequence length X faster than Edlib 10,000X 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 D-SOFT hardware-acceleration design Area: 264 mm 2 Power: 7.3W 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 16

17 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 } ~2X speedup from parallel DRAM channels } ~3X reduction in number of memory accesses to the DRAM } All random memory accesses to update bins using on-chip SRAM (64MB) } On-chip updates completely hide off-chip (DRAM) bandwidth 17

18 18 Long read assembly on Darwin

19 Darwin: Read assembly Reference-guided De novo 19

20 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% 4,110X 30% (12, 1000, 25) 98.11% 98.40% 4,088X 40% (11, 1300, 22) 97.10% 97.40% 128X 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% 264X Baseline: DALIGNER 20

21 Thank you! Questions or feedback? 21