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High-throughput drug discovery on the Fujitsu A64FX architecture

Published: 11 January 2024 Publication History

Abstract

High-performance computational kernels that optimally exploit modern vector-capable processors are critical in running large-scale drug discovery campaigns efficiently and promptly compatible with the constraints posed by urgent computing needs. Yet, state-of-the-art virtual screening workflows focus either on the broadness of features provided to the drug researcher or performance on high-throughput accelerators, leaving the task of deploying efficient CPU kernels to the compiler. We ported the key parts of the LiGen drug discovery pipeline, based on molecular docking, to the Fujitsu A64FX platform and leveraged its vector processing capabilities via an industry-proven retargetable SIMD programming model. By rethinking and optimizing key geometrical docking algorithms to leverage SVE instructions, we are able to provide efficient, high throughput execution on SVE-capable platforms.

References

[1]
2023. Highway: Performance-portable, length-agnostic SIMD with runtime dispatch. https://github.com/google/highway
[2]
Andrea R Beccari, Carlo Cavazzoni, Claudia Beato, and Gabriele Costantino. 2013. LiGen: a High Performance workflow for chemistry driven de novo design. Journal of Chemical Information and Modeling 53, 6 (2013), 1518–1527.
[3]
Andrea R. Beccari, Marica Gemei, Matteo Lo Monte, Nazareno Menegatti, Marco Fanton, Alessandro Pedretti, Silvia Bovolenta, Cinzia Nucci, Angela Molteni, Andrea Rossignoli, Laura Brandolini, Alessandro Taddei, Lorena Za, Chiara Liberati, and Giulio Vistoli. 2017. Novel selective, potent naphthyl TRPM8 antagonists identified through a combined ligand- and structure-based virtual screening approach. In Scientific reports.
[4]
David E Clark. 2008. What has virtual screening ever done for drug discovery?Expert Opinion on Drug Discovery 3, 8 (2008), 841–851. https://doi.org/10.1517/17460441.3.8.841 arXiv:https://doi.org/10.1517/17460441.3.8.841PMID: 23484962.
[5]
Mengran Fan, Jian Wang, Huaipan Jiang, Yilin Feng, Mehrdad Mahdavi, Kamesh Madduri, Mahmut T. Kandemir, and Nikolay V. Dokholyan. 2021. GPU-Accelerated Flexible Molecular Docking. The Journal of Physical Chemistry B 125, 4 (2021), 1049–1060. https://doi.org/10.1021/acs.jpcb.0c09051 arXiv:https://doi.org/10.1021/acs.jpcb.0c09051PMID: 33497567.
[6]
Fujitsu. 2023. Fujitsu A64FX datasheet. https://www.fujitsu.com/downloads/JP/jsuper/a64fx/a64fx_datasheet.pdf
[7]
Davide Gadioli, Emanuele Vitali, Federico Ficarelli, Chiara Latini, Candida Manelfi, Carmine Talarico, Cristina Silvano, Carlo Cavazzoni, Gianluca Palermo, and Andrea Rosario Beccari. 2022. EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2. IEEE Transactions on Emerging Topics in Computing (2022), 1–12. https://doi.org/10.1109/TETC.2022.3187134
[8]
Enrico Glaab. 2016. Building a virtual ligand screening pipeline using free software: a survey. Briefings in bioinformatics 17, 2 (March 2016), 352—366. https://doi.org/10.1093/bib/bbv037
[9]
Jens Glaser, Josh V Vermaas, David M Rogers, Jeff Larkin, Scott LeGrand, Swen Boehm, Matthew B Baker, Aaron Scheinberg, Andreas F Tillack, Mathialakan Thavappiragasam, 2021. High-throughput virtual laboratory for drug discovery using massive datasets. The International Journal of High Performance Computing Applications (2021), 10943420211001565.
[10]
Brendan Gregg. 2016. The flame graph. Commun. ACM 59, 6 (May 2016), 48–57. https://doi.org/10.1145/2909476
[11]
C. Lattner and V. Adve. 2004. LLVM: A compilation framework for lifelong program analysis & transformation. In International Symposium on Code Generation and Optimization, 2004. CGO 2004.IEEE, San Jose, CA, USA, 75–86. https://doi.org/10.1109/CGO.2004.1281665
[12]
Scott LeGrand, Aaron Scheinberg, Andreas F Tillack, Mathialakan Thavappiragasam, Josh V Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, 2020. GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 1–10.
[13]
Evanthia Lionta, George Spyrou, Demetrios K. Vassilatis, and Zoe Cournia. 2014. Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Current Topics in Medicinal Chemistry 14, 16 (2014), 1923–1938.
[14]
A.M. MacConnachie. 1999. Zanamivir (Relenza®) — A new treatment for influenza. Intensive and Critical Care Nursing 15, 6 (1999), 369–370. https://doi.org/10.1016/S0964-3397(99)80031-7
[15]
Stefano Markidis, Davide Gadioli, Emanuele Vitali, and Gianluca Palermo. 2021. Understanding the I/O Impact on the Performance of High-Throughput Molecular Docking. In 2021 IEEE/ACM Sixth International Parallel Data Systems Workshop (PDSW). 9–14. https://doi.org/10.1109/PDSW54622.2021.00007
[16]
John D. McCalpin. 1995. Memory Bandwidth and Machine Balance in Current High Performance Computers. IEEE Computer Society Technical Committee on Computer Architecture (TCCA) Newsletter (Dec. 1995), 19–25.
[17]
Natarajan Arul Murugan, Artur Podobas, Davide Gadioli, Emanuele Vitali, Gianluca Palermo, and Stefano Markidis. 2022. A Review on Parallel Virtual Screening Softwares for High-Performance Computers. Pharmaceuticals 15, 1 (2022). https://doi.org/10.3390/ph15010063
[18]
Julie R. Schames, Richard H. Henchman, Jay S. Siegel, Christoph A. Sotriffer, Haihong Ni, and J. Andrew McCammon. 2004. Discovery of a Novel Binding Trench in HIV Integrase. Journal of Medicinal Chemistry 47, 8 (2004), 1879–1881. https://doi.org/10.1021/jm0341913 arXiv:https://doi.org/10.1021/jm0341913PMID: 15055986.
[19]
Toshiyuki Shimizu. 2020. Supercomputer Fugaku: Co-designed with application developers/researchers. In 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC). 1–4. https://doi.org/10.1109/A-SSCC48613.2020.9336127
[20]
Leonardo Solis-Vasquez, Erich Focht, and Andreas Koch. 2021. Mapping Irregular Computations for Molecular Docking to the SX-Aurora TSUBASA Vector Engine. In 2021 IEEE/ACM 11th Workshop on Irregular Applications: Architectures and Algorithms (IA3). IEEE, St. Louis, MO, USA, 1–10. https://doi.org/10.1109/IA354616.2021.00008
[21]
TGCC. 2023. Configuration of Irene. https://www-hpc.cea.fr/tgcc-public/en/html/toc/fulldoc/Supercomputer_architecture.html
[22]
TOP500.org. Accessed: 2021-09-27. The TOP500 list. https://top500.org.
[23]
Jan Treibig, Georg Hager, and Gerhard Wellein. 2010. LIKWID: A lightweight performance-oriented tool suite for x86 multicore environments. arxiv:1004.4431 [cs.DC]
[24]
Josh Vincent Vermaas, Ada Sedova, Matthew B. Baker, Swen Boehm, David M. Rogers, Jeff Larkin, Jens Glaser, Micholas D. Smith, Oscar Hernandez, and Jeremy C. Smith. 2021. Supercomputing Pipelines Search for Therapeutics Against COVID-19. Computing in Science Engineering 23, 1 (2021), 7–16. https://doi.org/10.1109/MCSE.2020.3036540
[25]
Giulio Vistoli, Candida Manelfi, Carmine Talarico, Anna Fava, Arieh Warshel, Igor V. Tetko, Rossen Apostolov, Yang Ye, Chiara Latini, Federico Ficarelli, Gianluca Palermo, Davide Gadioli, Emanuele Vitali, Gaetano Varriale, Vincenzo Pisapia, Marco Scaturro, Silvano Coletti, Daniele Gregori, Daniel Gruffat, Edgardo Leija, Sam Hessenauer, Alberto Delbianco, Marcello Allegretti, and Andrea R. Beccari. 2023. MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions. Expert Opinion on Drug Discovery (July 2023), 1–13. https://doi.org/10.1080/17460441.2023.2221025
[26]
Emanuele Vitali, Davide Gadioli, Gianluca Palermo, Andrea Beccari, Carlo Cavazzoni, and Cristina Silvano. 2019. Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes. The Journal of Supercomputing 75, 7 (July 2019), 3374–3396. https://doi.org/10.1007/s11227-019-02875-w
[27]
Hiroshi Watanabe and Koh M. Nakagawa. 2019. SIMD vectorization for the Lennard-Jones potential with AVX2 and AVX-512 instructions. Computer Physics Communications 237 (April 2019), 1–7. https://doi.org/10.1016/j.cpc.2018.10.028

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cover image ACM Other conferences
HPCAsia '24 Workshops: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region Workshops
January 2024
134 pages
ISBN:9798400716522
DOI:10.1145/3636480
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2024

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Author Tags

  1. ARM
  2. Drug Discovery
  3. HPC
  4. Molecular Docking
  5. SIMD
  6. SVE

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  • EUPEX

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HPCAsiaWS 2024

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Overall Acceptance Rate 69 of 143 submissions, 48%

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