Skip to main content

Challenges and Opportunities for RISC-V Architectures Towards Genomics-Based Workloads

  • Conference paper
  • First Online:
High Performance Computing (ISC High Performance 2023)


The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. Accelerated European cloud. Accessed 16 Mar 2023

  2. Android open source project ports to RISC-V. Accessed 06 Mar 2023

  3. Fedora/RISC-V project homepage. Accessed 23 Mar 2023

  4. Genomics RISC-V open-data repository. Accessed 23 Mar 2023

  5. Hifive unmatched RISC-V board. Accessed 23 Mar 2023

  6. OpenCube open-source cloud-based services on epi systems. Accessed 16 Mar 2023

  7. Project vitamin-v virtual environment and tool-boxing for trustworthy development of RISC-V based cloud services. Accessed 16 Mar 2023

  8. RISC-V shines at embedded world with new specs and processors. Accessed 23 Mar 2023

  9. Riser RISC-V for cloud services. Accessed 16 Mar 2023

  10. Transaction processing performance council. Accessed 16 Mar 2023

  11. Abella, J., et al.: An academic RISC-V silicon implementation based on open-source components. In: 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS), pp. 1–6 (2020)

    Google Scholar 

  12. Cantor, R.M., et al.: Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 86(1), 6–22 (2010)

    Article  Google Scholar 

  13. Eisenstein, M.: Big data: the power of petabytes. Nature 527(7576), S2–S4 (2015).

    Article  Google Scholar 

  14. Fell, A., et al.: The marenostrum experimental exascale platform (MEEP). Supercomput. Front. Innov. 8(1), 62–81 (2021).

  15. Ince, M.N., Ledet, J., Gunay, M.: Building an open source Linux computing system on RISC-V, pp. 1–4 (2019)

    Google Scholar 

  16. Kooperberg, C., Ruczinski, I.: Identifying interacting SNPS using Monte Carlo logic regression. Genet. Epidemiol.: Off. Publ. Int. Genet. Epidemiol. Soc. 28(2), 157–170 (2005)

    Article  Google Scholar 

  17. Kovač, M.: European processor initiative: the industrial cornerstone of EuroHPC for exascale era. In: Proceedings of the 16th ACM International Conference on Computing Frontiers, CF 2019, p. 319. Association for Computing Machinery, New York (2019).

  18. Krzywinski, M., et al.: Circos: an information aesthetic for comparative genomics. Genome Res. 19(9), 1639–1645 (2009)

    Article  Google Scholar 

  19. Luszczek, P., et al.: Introduction to the HPC Challenge Benchmark Suite (2005).

  20. Moore, J.H.: A global view of epistasis. Nat. Genet. 37(1), 13–14 (2005)

    Article  Google Scholar 

  21. Niel, C., Sinoquet, C., Dina, C., Rocheleau, G.: A survey about methods dedicated to epistasis detection. Front. Genet. 6, 285 (2015)

    Article  Google Scholar 

  22. Gottesman, O., et al.: The electronic medical records and genomics (emerge) network: past, present, and future. Genet. Med. 15, 761–771 (2013)

    Article  Google Scholar 

  23. Ramírez, C., Hernández, C.A., Palomar, O., Unsal, O., Ramírez, M.A., Cristal, A.: A RISC-V simulator and benchmark suite for designing and evaluating vector architectures. ACM Trans. Archit. Code Optim. 17(4) (2020).

  24. Ritchie, M.D., et al.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69(1), 138–147 (2001)

    Article  Google Scholar 

  25. Uffelmann, E., et al.: Genome-wide association studies. Nat. Rev. Methods Primers 1(1), 59 (2021)

    Article  Google Scholar 

  26. Wood, A.R., et al.: Another explanation for apparent epistasis. Nature 514, E3–E5 (2014)

    Article  Google Scholar 

  27. Wu, Z., Hammad, K., Beyene, A., Dawji, Y., Ghafar-Zadeh, E., Magierowski, S.: An FPGA implementation of a portable DNA sequencing device based on RISC-V. In: 2022 20th IEEE Interregional NEWCAS Conference (NEWCAS), pp. 417–420 (2022)

    Google Scholar 

  28. Zaharia, M., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)

    Article  Google Scholar 

  29. Zhang, Y., Liu, J.S.: Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39(9), 1167–1173 (2007)

    Article  Google Scholar 

Download references


This work has been partially financed by the European Commission (EU-HORIZON NEARDATA GA.101092644, VITAMIN-V GA.101093062), the MEEP Project whichreceived funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 946002. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Spain, Croatia and Turkey. Also by the Spanish Ministry of Science (MICINN) under scholarship BES-2017-081635, the Research State Agency (AEI) and European Regional Development Funds (ERDF/FEDER) under DALEST grant agreement PID2021-126248OB-I00, MCIN/AEI/10.13039/ 501100011033/FEDER and PID GA PID2019-107255GB-C21, and the Generalitat de Catalunya (AGAUR) under grant agreements 2021-SGR-00478, 2021-SGR-01626 and “FSE Invertint en el teu futur”.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Gonzalo Gómez-Sánchez or Aaron Call .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gómez-Sánchez, G. et al. (2023). Challenges and Opportunities for RISC-V Architectures Towards Genomics-Based Workloads. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40842-7

  • Online ISBN: 978-3-031-40843-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics