HUG + Nomica: a scalable FPGA-based architecture for variant-calling
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In the coming years, genome research will likely transform medical and agricultural practices. The unique genetic profile of a species is leading to the development of customized treatments, from personalized medicine to agrigenomics, but the exponential growth of available genomic data requires a computational effort that may limit the progress of these fields. Within this context, we propose the development of a novel hardware and software advanced support for genomics research, named HUGenomics. The framework aims at facilitating genome assembly process by means of both hardware accelerated algorithms and scientific data visualization tools. Indeed, the system raises the level of abstraction allowing users to easily integrate custom algorithms into the hardware pipeline without any knowledge of the underneath architecture. In the context of this tool we present also Nomica, an acceleration of the variant-calling pipelines, that allow to spot potentially harmful variants in a target genome. In particular, this pipelines employ the PairHMM Forward Algorithm, which emerged as bottleneck of the procedure as well as the step that would benefit the most from parallelization. The goal of nomica is exploring the paradigm of HSA (Heterogeneous System Architectures) and integrating a hardware accelerator based on FPGA into GATK, a state-of-the-art software solution that supports research in the genomic field.
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HUG + Nomica: a scalable FPGA-based architecture for variant-calling
- 1. Intel PSG, San Jose - CA HUGenomics A Support to Genomics Research Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano Lorenzo Di Tucci, Giulia Guidi, Marco Rabozzi, Sara Notargiacomo, Marco D. Santambrogio 06/01/2018 lorenzo.ditucci@polimi.it
- 2. Genomic research Recent advancements in genomic research allow to perform multiple analysis on DNA affecting different fields However, in order to extract biological meaning from secondary genome analysis a complex process has to be performed 2
- 3. Genome sequencing Given a biological sample, genome sequencing is the process of determining the precise order of nucleotides within a DNA molecule This process produces short DNA fragments which need to be assembled to reconstruct the original sequence ACGTAGCTCGGACCATAGCA CCGCCGTAGCTCGGACCATAGCACATG AGTTTTGGGGGACCATAGCACATGGACACATGC GGACCATAGCACATGGACACATGC GGTCAAAAATAGCACATGGACACATGC ATTGTATCGGACCATATTGCTTAGCATGTATTTGC CATGGACACATGC CGTAACCATAGCACATGGACACATGC TTTTAGGTAATTGCCATAGCACATGGACACAT 3
- 4. Genome assembly Genome assembly: reconstruct a genome from a set of shorter reads Reference-based assembly ACGTAGCTCGGACCATAGCA GGACCATAGCACATGGACACATGC ACGTAGCTCGGACCATAGCAGGACCATAGCACATGGACATGGACACATGCT TA CATGGACACATGC 4
- 5. Genome assembly Genome assembly: reconstruct a genome from a set of shorter reads De novo assembly ACGTAGCTCGGACCATAGCAGGACCATAGCACATGGACATGGACACATGCTTA Applications are limited to species with available reference genomes 5
- 6. De novo assembly Issue: • General purpose architectures are inefficient Solution: • In such scenario, hardware accelerators proved to be effective in optimizing the performance over power consumption ratio Genomics algorithms are usually: • compute-intensive • massive amount of data • fast-changing Solution: • In such scenario, hardware accelerators proved to be effective in optimizing the performance over power consumption ratio 6
- 7. Hardware architectures Learning curve for multiple architectures 7
- 8. Objective An advanced support to genomic research exploiting heterogeneous hardware architectures 8
- 9. Genomics Hardware Pipeline PIPELINE CREATION DATA UPLOAD PROCESSING DATA VISUALIZATION HUG has exactly what I need! YESLINE 9
- 14. Custom Code Integration HETEROGENEOUS ARCHITECTURE HUG I’d like to integrate my own algorithm RAW READ CONTIGGING SCAFFOLDING RE-SCAFFOLDING ANNOTATIO N 14
- 15. Genomics Hardware Pipeline FAST PROTOTYPING CUSTOM HARDWARE ALGORITHM PIPELINE CREATION OR INTEGRATION DATA UPLOAD PROCESSING DATA VISUALIZATION YESLINE NOLINE Is the algorithm available on HUG? 15
- 16. HUG Today Smith-Waterman [1] Lorenzo Di Tucci, Giulia Guidi, Sara Notargiacomo, Luca Cerina, Alberto Scolari, and Marco D. Santambrogio. "HUGenomics: A support to personalized medicine research." In Research and Technologies for Society and Industry (RTSI), 2017 IEEE 3rd International Forum on, pp. 1-5. IEEE, 2017. [2] Lorenzo Di Tucci, Davide Conficconi, Alessandro Comodi, Steven Hofmeyr, David Donofrio and Marco Domenico Santambrogio. "A Parallel, Energy Efficient Hardware Architecture for the merAligner on FPGA using Chisel HDL " In Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018 IEEE International, t.b.p. IEEE, 2018. [1] [2] 16
- 17. HUG Today [1] Lorenzo Di Tucci, Giulia Guidi, Sara Notargiacomo, Luca Cerina, Alberto Scolari, and Marco D. Santambrogio. "HUGenomics: A support to personalized medicine research." In Research and Technologies for Society and Industry (RTSI), 2017 IEEE 3rd International Forum on, pp. 1-5. IEEE, 2017. [2] Lorenzo Di Tucci, Davide Conficconi, Alessandro Comodi, Steven Hofmeyr, David Donofrio and Marco Domenico Santambrogio. "A Parallel, Energy Efficient Hardware Architecture for the merAligner on FPGA using Chisel HDL " In Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018 IEEE International, t.b.p. IEEE, 2018. [1] RAW READ CONTIGGING SCAFFOLDING RE-SCAFFOLDING ANNOTATION [2] Sequence Alignment via Smith-Waterman Algorithm [2] 17
- 18. Smith-Waterman Platform Performance [GCUPS] Power Efficiency [GCUPS/W] AWS-VU9P (3 queries in parallel) 110.0 4.400 Tesla K20 45.0 0.200 ADM-PCIE-KU3 42.5 1.699 Nvidia GeForce GTX 295 30.0 0.104 Xtreme Data XD1000 25.6 0.430 Altera Stratix V on Nallatech PCIe-385 24.7 0.988 Nvidia GeForce GTX 295 16.1 0.056 ADM-PCIE-7V3 14.8 0.594 Dual-core Nvidia 9800 GX2 14.5 0.074 Nvidia GeForce GTX 280 9.7 0.041 Xtreme Data XD2000i 9.0 0.150 2XNvidia GeForce 8800 3.6 0.017 18
- 19. Smith-Waterman Platform Performance [GCUPS] Power Efficiency [GCUPS/W] AWS-VU9P (3 queries in parallel) 110.0 4.400 Tesla K20 45.0 0.200 ADM-PCIE-KU3 42.5 1.699 Nvidia GeForce GTX 295 30.0 0.104 Xtreme Data XD1000 25.6 0.430 Altera Stratix V on Nallatech PCIe-385 24.7 0.988 Nvidia GeForce GTX 295 16.1 0.056 ADM-PCIE-7V3 14.8 0.594 Dual-core Nvidia 9800 GX2 14.5 0.074 Nvidia GeForce GTX 280 9.7 0.041 Xtreme Data XD2000i 9.0 0.150 2XNvidia GeForce 8800 3.6 0.017 19
- 20. Smith-Waterman Platform Performance [GCUPS] Power Efficiency [GCUPS/W] AWS-VU9P (3 queries in parallel) 110.0 4.400 Tesla K20 45.0 0.200 ADM-PCIE-KU3 42.5 1.699 Nvidia GeForce GTX 295 30.0 0.104 Xtreme Data XD1000 25.6 0.430 Altera Stratix V on Nallatech PCIe-385 24.7 0.988 Nvidia GeForce GTX 295 16.1 0.056 ADM-PCIE-7V3 14.8 0.594 Dual-core Nvidia 9800 GX2 14.5 0.074 Nvidia GeForce GTX 280 9.7 0.041 Xtreme Data XD2000i 9.0 0.150 2XNvidia GeForce 8800 3.6 0.017 20
- 21. HUG Today [1] Lorenzo Di Tucci, Giulia Guidi, Sara Notargiacomo, Luca Cerina, Alberto Scolari, and Marco D. Santambrogio. "HUGenomics: A support to personalized medicine research." In Research and Technologies for Society and Industry (RTSI), 2017 IEEE 3rd International Forum on, pp. 1-5. IEEE, 2017. [2] Lorenzo Di Tucci, Davide Conficconi, Alessandro Comodi, Steven Hofmeyr, David Donofrio and Marco Domenico Santambrogio. "A Parallel, Energy Efficient Hardware Architecture for the merAligner on FPGA using Chisel HDL " In Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018 IEEE International, t.b.p. IEEE, 2018. RAW READ CONTIGGING SCAFFOLDING RE-SCAFFOLDING ANNOTATION Pattern Matching to identify gene motifs during Gene Annotation [1] [2] 21
- 22. Pattern Matching via TiREX Regular Expression Flex* 16-core† (VC707) Speedup ACCGTGGA 271 µs 2.07 µs 130.90X (TTT)+CT 121 µs 4.54 µs 26.65X (CAGT)|(GGGG)|(TTGG)TGCA(C|G)+ 263 µs 3.36 µs 78.27X * running on a Intel i7 with a peak frequency of 2.8GHz † running at 130 MHz+ 22
- 23. HUG Today [1] Lorenzo Di Tucci, Giulia Guidi, Sara Notargiacomo, Luca Cerina, Alberto Scolari, and Marco D. Santambrogio. "HUGenomics: A support to personalized medicine research." In Research and Technologies for Society and Industry (RTSI), 2017 IEEE 3rd International Forum on, pp. 1-5. IEEE, 2017. [2] Lorenzo Di Tucci, Davide Conficconi, Alessandro Comodi, Steven Hofmeyr, David Donofrio and Marco Domenico Santambrogio. "A Parallel, Energy Efficient Hardware Architecture for the merAligner on FPGA using Chisel HDL " In Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018 IEEE International, t.b.p. IEEE, 2018. RAW READ CONTIGGING SCAFFOLDING RE-SCAFFOLDING ANNOTATION [1] [2] PairHMM for gene prediction/finding in the Gene Annotation phase 23
- 24. Davide Sampietro Chiara Crippa Lorenzo Di Tucci Emanuele Del Sozzo Marco D. Santambrogio {davide.sampietro, chiara2.crippa}@mail.polimi.it {lorenzo.ditucci, emanuele.delsozzo, marco.santambrogio}@polimi.it 24
- 25. 25
- 30. PairHMM A statistical model that allows to establish the correlation of two strings. It serves as a means to correct sequencing errors Observable process (pair of words) ATACGCCACT ATCCTGACTRead: Hap: Underlying model (PairHMM) 30
- 31. Some alignments look very unlikely ATACGC--CCACT AT-C-CTGA--CT PairHMM A statistical model that allows to establish the correlation of two strings. It serves as a means to correct sequencing errors Observable process (pair of words) 31
- 32. ATAC-GCCACT ATCCTG--ACT Some other make much more sense PairHMM A statistical model that allows to establish the correlation of two strings. It serves as a means to correct sequencing errors Observable process (pair of words) 32
- 33. ATAC-GCCACT ATCCTG--ACT Even though they are not perfect PairHMM A statistical model that allows to establish the correlation of two strings. It serves as a means to correct sequencing errors Observable process (pair of words) 33
- 34. ATAC-GCCACT ATCCTG--ACT PairHMM A statistical model that allows to establish the correlation of two strings. It serves as a means to correct sequencing errors Observable process (pair of words) Underlying model (PairHMM) 34
- 35. Serves as a measure to understand correlation between two strings PairHMM Forward Algorithm Takes into account every possible alignment 35
- 36. M XY MM MY MX GM GM X XYY The problem is reformulated through dynamic programming PairHMM Forward Algorithm 36
- 43. FA – datapath X[i-1][j-1] Y[i-1][j-1] gm[j] mm[j] M[i-1][j-1] X[i][j] X[i][j-1] M[i][j-1]xx[j] mx[j] Y[i-1][j] M[i-1][j]yy[j] my[j] Y[i][j] prior M[i][j] 43
- 44. Tree height reduction [S. Huang et al., Hardware Acceleration of the Pair-HMM Algorithm for DNA Variant Calling] X[i-1][j-1] Y[i-1][j-1] gm[j] mm[j] M[i-1][j-1] prior M[i][j] 44
- 45. X[i][j-1] Y[i][j-1] gm[j] XY[i][j] XY[i-1][j] mm[j] M[i-1][j-1] prior M[i][j] Tree height reduction [S. Huang et al., Hardware Acceleration of the Pair-HMM Algorithm for DNA Variant Calling] 45
- 46. X[i][j-1] Y[i][j-1] gm[j] XY[i][j] mm[j] MMM[i][j] M[i][j-1] MMM[i-1][j]XY[i-1][j] prior M[i][j] Tree height reduction [S. Huang et al., Hardware Acceleration of the Pair-HMM Algorithm for DNA Variant Calling] 46
- 47. Limitations of Systolic Arrays Systolic Array size tuning ▪ Longer reads cannot be processed ▪ Shorter reads leave the PEs idle Solution: instantiate fixed size PE Array ▪ Longer reads are chunked ▪ Upper bound on idle PEs 47
- 48. e.g. 2 PEs instead of 5 Our flavour: PE Chunks read PE haplotype PE 48
- 49. PEPEPEPE e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 49
- 50. PEPE e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 50
- 51. PEPEPEPE e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 51
- 52. PEPE e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 52
- 53. PEPEPEPE e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 53
- 54. e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 54
- 55. e.g. 2 PEs instead of 5 Our flavour: PE Chunks read haplotype 55
- 56. Results Platform #PEs Frequency (MHz) Time (ms) Speedup Java on CPU N/A N/A 10800 1X Intel FPGA Arria 10 128 180 89 121X Nvidia K40 N/A N/A 70 154X Stratix V D8 (Intel FPGA) 64 207 8.3 1301X PE Ring (Stratix V) 64 200 5.3 2038X Arria 10 (Intel FPGA) 208 213 2.8 3857X PE Ring (Arria 10) 128 231 2.6 4154X 56
- 57. Pipeline filling latency Dead times when switching to the next haplotype Solution: concatenate several haplotypes Limitations of Systolic Arrays Systolic Array size tuning ▪ Longer reads cannot be processed ▪ Shorter reads leave the PEs idle Solution: instantiate fixed size PE Array ▪ Longer reads are chunked ▪ Upper bound on idle PEs 57
- 73. Our Solution Input data Output data 73
- 74. GATK workflow (Java) C++ Library Our Solution Input data Output data 74
- 75. Thanks for your attention Lorenzo Di Tucci, Giulia Guidi, Marco Rabozzi, Davide Sampietro, Emanuele Del Sozzo, Chiara Crippa, Sara Notargiacomo, Marco D. Santambrogio lorenzo.ditucci@polimi.it davide.sampietro@mail.polimi.it