ABSTRACT
Conducting long-timescale simulations of small molecules using Molecular Dynamics (MD) is crucial in drug design. However, traditional methods to accelerate the process, including ASICs or GPUs, have limitations. ASIC solutions are not always generally available, while GPU solutions may not scale when processing small molecules. FPGAs are both communication processors and accelerators, with tight coupling between these capabilities, and so could be used to address strong scaling in this domain.
We present FASDA, the first FPGA-based MD accelerator available for community development. FASDA enables the use of FPGA enhanced clusters and clouds to execute range-limited MD, which is the most resource-intensive and computation-demanding component in MD. FASDA is built with a series of plugable components that are adjustable based on user requirements and demonstrates nearly linear scaling on an eight FPGA cluster. It outperforms the state-of-the-art GPU solution by 4.67x, with the resulting prospect of significantly reducing lead evaluation time.
- [n. d.]. FAbRIC (FPGA Research Infrastructure Cloud). https://wikis.utexas.edu/display/fabric/Home. Accessed: 2023-03-21.Google Scholar
- [n. d.]. Heterogeneous Accelerated Compute Clusters. https://www.amd-haccs.io. Accessed: 2023-08-14.Google Scholar
- Mark James Abraham, Teemu Murtola, Roland Schulz, Szilárd Páll, Jeremy C. Smith, Berk Hess, and Erik Lindahl. 2015. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1--2 (2015), 19--25. Google ScholarCross Ref
- Maral Aminpour, Carlo Montemagno, and Jack A Tuszynski. 2019. An overview of molecular modeling for drug discovery with specific illustrative examples of applications. Molecules 24, 9 (2019), 1693.Google ScholarCross Ref
- Eman Bin Khunayn, Shanika Karunasekera, Hairuo Xie, and Kotagiri Ramamohanarao. 2017. Exploiting Data Dependency to Mitigate Stragglers in Distributed Spatial Simulation. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (Redondo Beach, CA, USA) (SIGSPATIAL '17). Association for Computing Machinery, New York, NY, USA, Article 43, 10 pages. Google ScholarDigital Library
- C. Bobda, J. Mandebi, P. Chow, M. Ewais, N. Tarafdar, J.C. Vega, K. Eguro, D. Koch, S. Handagala, M. Leeser, M.C. Herbordt, H. Shahzad, P. Hofstee, B. Ringlein, J. Szefer, A. Sanaullah, and R. Tessier. 2022. The Future of FPGA Acceleration in Datacenters and the Cloud. ACM Transactions on Reconfigurable Technology and Systems 15, 3 (2022), 1--42. Google ScholarDigital Library
- Taisuke Boku, Ryohei Kobayashi, Norihisa Fujita, Hideharu Amano, Kentaro Sano, Toshihiro Hanawa, and Yoshiki Yamaguchi. 2019. Cygnus: GPU meets FPGA for HPC. In International Conference on Supercomputing. https://www.r-ccs.riken.jp/labs/lpnctrt/assets/img/lspanc2020jan_boku_light.pdf.Google Scholar
- Kevin J Bowers, Edmond Chow, Huafeng Xu, Ron O Dror, Michael P Eastwood, Brent A Gregersen, John L Klepeis, Istvan Kolossvary, Mark A Moraes, Federico D Sacerdoti, et al. 2006. Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proceedings of the 2006 ACM/IEEE Conference on Super-computing. 84--es.Google ScholarDigital Library
- D.A. Case, T.E. Cheatham III, T. Darden, H. Gohlke, R. Luo, K.M. Merz, Jr., A. Onufriev, C. Simmerling, B. Wang, and R.J. Woods. 2005. The Amber Biomolecular Simulation Programs. Journal Computational Chemistry 26 (2005), 1668--1688.Google ScholarCross Ref
- A.M. Caulfield, E.S. Chung, A. Putnam, H. Angepat, Jeremy Fowers, Michael Haselman, Stephen Heil, Matt Humphrey, Puneet Kaur, Joo-Young Kim, Daniel Lo, Todd Massengill, Kalin Ovtcharov, Michael Papamichael, Lisa Woods, Sitaram Lanka, Derek Chiou, and Doug Burger. 2016. A Cloud-Scale Acceleration Architecture. In 49th IEEE/ACM Int. Symp. Microarchitecture. 1--13.Google ScholarDigital Library
- P.H. Chen, P. Haghi, J.Y. Chung, T. Geng, R. West, A. Skjellum, and M.C. Herbordt. 2022. The Viability of Using Online Prediction to Perform Extra Work while Executing BSP Applications. In IEEE High Performance Extreme Computing Conference.Google Scholar
- M. Chiu and M.C. Herbordt. 2009. Efficient Filtering for Molecular Dynamics Simulations. In Proceedings of the IEEE Conference on Field Programmable Logic and Applications.Google Scholar
- M. Chiu and M.C. Herbordt. 2010. Molecular Dynamics Simulations on High Performance Reconfigurable Computing Systems. ACM Transactions Reconfigurable Technology and Systems 3, 4 (2010), 1--37.Google ScholarDigital Library
- M. Chiu, M.A. Khan, and M.C. Herbordt. 2011. Efficient Calculation of Pairwise Nonbonded Forces. In 2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines. Google ScholarDigital Library
- T. Darden, D. York, and L. Pedersen. 1993. Particle Mesh Ewald: an N log(N) method for Ewald sums in large systems. 98 (1993), 10089--10092.Google Scholar
- P. Eastman and V.S. Pande. 2010. OpenMM: A Hardware-Independent Framework for Molecular Simulations. Computing in Science and Engineering 4 (2010), 34--39.Google ScholarDigital Library
- Toshiyuki Fukushige, Makoto Taiji, Junichiro Makino, Toshikazu Ebisuzaki, and Daiichiro Sugimoto. 1996. A highly parallelized special-purpose computer for many-body simulations with an arbitrary central force: MD-GRAPE. The Astro-physical Journal 468 (1996), 51.Google ScholarCross Ref
- Aravindhan Ganesan, Michelle L. Coote, and Khaled Barakat. 2017. Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug Discovery Today 22, 2 (2017), 249--269. Google ScholarCross Ref
- Jens Glaser, Trung Dac Nguyen, Joshua A. Anderson, Pak Lui, Filippo Spiga, Jaime A. Millan, David C. Morse, and Sharon C. Glotzer. 2015. Strong scaling of general-purpose molecular dynamics simulations on GPUs. Computer Physics Communications 192 (2015), 97--107. Google ScholarCross Ref
- Y. Gu, T. VanCourt, and M.C. Herbordt. 2006. Accelerating Molecular Dynamics Simulations with Configurable Circuits. IEE Proceedings on Computers and Digital Technology 153, 3 (2006), 189--195. Google ScholarCross Ref
- Y. Gu, T. VanCourt, and M.C. Herbordt. 2006. Improved Interpolation and System Integration for FPGA-Based Molecular Dynamics Simulations. In 2006 International Conference on Field Programmable Logic and Applications. 21--28. Google ScholarCross Ref
- Y. Gu, T. VanCourt, and M.C. Herbordt. 2008. Explicit Design of FPGA-Based Coprocessors for Short-Range Force Computation in Molecular Dynamics Simulations. Parallel Comput. 34, 4--5 (2008), 261--271. Google ScholarDigital Library
- S. Handagala, M.C. Herbordt, and M. Leeser. 2021. OCT: The Open Cloud FPGA Testbed. In 31st International Conference on Field Programmable Logic and Applications (FPL). doi: TBD.Google Scholar
- Derek Jones, Jonathan E Allen, Yue Yang, William F Drew Bennett, Maya Gokhale, Niema Moshiri, and Tajana S Rosing. 2022. Accelerators for classical molecular dynamics simulations of biomolecules. Journal of chemical theory and computation 18, 7 (2022), 4047--4069.Google ScholarCross Ref
- Kate Keahey, Jason Anderson, Zhuo Zhen, Pierre Riteau, Paul Ruth, Dan Stanzione, Mert Cevik, Jacob Colleran, Haryadi S. Gunawi, Cody Hammock, Joe Mambretti, Alexander Barnes, François Halbach, Alex Rocha, and Joe Stubbs. 2020. Lessons Learned from the Chameleon Testbed. In Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC '20). USENIX Association.Google Scholar
- Rafał Kiełbik, Krzysztof Hałagan, Witold Zatorski, Jarosław Jung, Jacek Ulański, Andrzej Napieralski, Kamil Rudnicki, Piotr Amrozik, Grzegorz Jabłoński, Dominik StoŻek, Piotr Polanowski, Zbigniew Mudza, Joanna Kupis, and Przemysław Panek. 2018. ARUZ --- Large-scale, massively parallel FPGA-based analyzer of real complex systems. Computer Physics Communications 232 (2018), 22--34. Google ScholarCross Ref
- V. Kindratenko and D. Pointer. 2006. A Case Study in Porting a Production Scientific Supercomputing Application to a Reconfigurable Computer. In Proceedings of the IEEE Symposium on Field Programmable Custom Computing Machines. 13--22.Google Scholar
- V. Krishnan, O. Serres, and M. Blocksome. 2021. Configurable Network Protocol Accelerator (COPA). IEEE Micro 41, 1 (2021).Google Scholar
- A. Lawande, A. George, and H. Lam. 2016. Novo-G#: a multidimensional torus-based reconfigurable cluster for molecular dynamics. Concurrency and Computation: Practice and Experience 28, 8 (2016).Google Scholar
- Xuewei Liu, Danfeng Shi, Shuangyan Zhou, Hongli Liu, Huanxiang Liu, and Xiaojun Yao. 2018. Molecular dynamics simulations and novel drug discovery. Expert Opinion on Drug Discovery 13, 1 (2018), 23--37. arXiv:https://doi.org/10.1080/17460441.2018.1403419 PMID: 29139324. Google ScholarCross Ref
- Johannes Menzel, Christian Plessl, and Tobias Kenter. 2021. The Strong Scaling Advantage of FPGAs in HPC for N-Body Simulations. ACM Transactions Reconfigurable Technology and Systems 15, 1, Article 10 (nov 2021), 30 pages. Google ScholarDigital Library
- A. Mondigo, T. Ueno, K. Sano, and H. Takizawa. 2020. Comparison of Direct and Indirect Networks for High-Performance FPGA Clusters. In ARC 2020. Lecture Notes in Computer Science, vol 12083, F. Rincon, J. Barba, H. So, P. Diniz, and J. Caba (Eds.). Springer. Google ScholarDigital Library
- Gentaro Morimoto, Yohei M Koyama, Hao Zhang, Teruhisa S Komatsu, Yousuke Ohno, Keigo Nishida, Itta Ohmura, Hiroshi Koyama, and Makoto Taiji. 2021. Hardware acceleration of tensor-structured multilevel ewald summation method on MDGRAPE-4A, a special-purpose computer system for molecular dynamics simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1--15.Google ScholarDigital Library
- Jérémie Mortier, Christin Rakers, Marcel Bermudez, Manuela S. Murgueitio, Sereina Riniker, and Gerhard Wolber. 2015. The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discovery Today 20, 6 (2015), 686--702. Google ScholarCross Ref
- Asher Mullard. 2014. New drugs cost US $2.6 billion to develop. Nature reviews. Drug discovery 13, 12 (2014), 877.Google Scholar
- Tetsu Narumi, Yousuke Ohno, Noriaki Okimoto, Atsushi Suenaga, Ryoko Yanai, and Makoto Taiji. 2006. A high-speed special-purpose computer for molecular dynamics simulations: MDGRAPE-3. In NIC Workshop, Vol. 34. 29--36.Google Scholar
- Itta Ohmura, Gentaro Morimoto, Yousuke Ohno, Aki Hasegawa, and Makoto Taiji. 2014. MDGRAPE-4: a special-purpose computer system for molecular dynamics simulations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, 2021 (2014), 20130387.Google ScholarCross Ref
- Szilárd Páll, Artem Zhmurov, Paul Bauer, Mark Abraham, Magnus Lundborg, Alan Gray, Berk Hess, and Erik Lindahl. 2020. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. The Journal of Chemical Physics 153, 13 (2020), 134110.Google ScholarCross Ref
- C. Pascoe, L. Stewart, B.W. Sherman, V. Sachdeva, and M.C. Herbordt. 2020. Execution of Complete Molecular Dynamics Simulations on Multiple FPGAs. In IEEE High Performance Extreme Computing Conference.Google Scholar
- J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, and K. Schulten. 2005. Scalable Molecular Dynamics with NAMD. Journal Computational Chemistry 26 (2005), 1781--1802.Google ScholarCross Ref
- C. Plessl. 2018. Bringing FPGAs to HPC Production Systems and Codes. In H2RC'18 workshop at Supercomputing (SC'18). Google ScholarCross Ref
- Outi MH Salo-Ahen, Ida Alanko, Rajendra Bhadane, Alexandre MJJ Bonvin, Rodrigo Vargas Honorato, Shakhawath Hossain, André H Juffer, Aleksei Kabedev, Maija Lahtela-Kakkonen, Anders Støttrup Larsen, et al. 2020. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 9, 1 (2020), 71.Google ScholarCross Ref
- Outi M. H. Salo-Ahen, Ida Alanko, Rajendra Bhadane, Alexandre M. J. J. Bonvin, Rodrigo Vargas Honorato, Shakhawath Hossain, André H. Juffer, Aleksei Kabedev, Maija Lahtela-Kakkonen, Anders Støttrup Larsen, Eveline Lescrinier, Parthiban Marimuthu, Muhammad Usman Mirza, Ghulam Mustafa, Ariane Nunes-Alves, Tatu Pantsar, Atefeh Saadabadi, Kalaimathy Singaravelu, and Michiel Vanmeert. 2021. Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes 9, 1 (2021). Google ScholarCross Ref
- M. Schaffner and L. Benini. 2018. On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations. Technical Report. ArXiv:1808.04201.Google Scholar
- R. Scrofano, M.B. Gokhale, F. Trouw, and V.K. Prasanna. 2008. Accelerating Molecular Dynamics Simulations with Reconfigurable Computers. IEEE Trans. Parallel and Distributed Systems 19, 6 (2008), 764--778.Google ScholarDigital Library
- D.E. Shaw. 2005. A Fast, Scalable Method for the Parallel Evaluation of Distance-Limited Pairwise Particle Interactions. Journal Computational Chemistry 26, 13 (2005), 1318--1328.Google ScholarCross Ref
- David E Shaw, Peter J Adams, Asaph Azaria, Joseph A Bank, Brannon Batson, Alistair Bell, Michael Bergdorf, Jhanvi Bhatt, J Adam Butts, Timothy Correia, et al. 2021. Anton 3: twenty microseconds of molecular dynamics simulation before lunch. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1--11.Google ScholarDigital Library
- David E Shaw, Martin M Deneroff, Ron O Dror, Jeffrey S Kuskin, Richard H Larson, John K Salmon, Cliff Young, Brannon Batson, Kevin J Bowers, Jack C Chao, et al. 2008. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 7 (2008), 91--97.Google ScholarDigital Library
- David E Shaw, JP Grossman, Joseph A Bank, Brannon Batson, J Adam Butts, Jack C Chao, Martin M Deneroff, Ron O Dror, Amos Even, Christopher H Fenton, et al. 2014. Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In SC'14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 41--53.Google ScholarDigital Library
- J. Sheng, B. Humphries, H. Zhang, and M.C. Herbordt. 2014. Design of 3D FFTs with FPGA Clusters. In IEEE High Performance Extreme Computing Conference. Google ScholarCross Ref
- J. Sheng, C. Yang, A. Caulfield, M. Papamichael, and M.C. Herbordt. 2017. HPC on FPGA Clouds: 3D FFTs and Implications for Molecular Dynamics. In 27th International Conference on Field Programmable Logic and Applications. Google ScholarCross Ref
- Fadi N. Sibai. 1998. The hyper-ring network: a cost-efficient topology for scalable multicomputers. In ACM Symposium on Applied Computing.Google ScholarDigital Library
- M. Snir. 2004. A Note on N-Body Computations with Cutoffs. Theory of Computing Systems 37 (2004), 295--318.Google ScholarCross Ref
- Ryutaro Susukita, Toshikazu Ebisuzaki, Bruce G Elmegreen, Hideaki Furusawa, Kenya Kato, Atsushi Kawai, Yoshinao Kobayashi, Takahiro Koishi, Geoffrey D McNiven, Tetsu Narumi, et al. 2003. Hardware accelerator for molecular dynamics: MDGRAPE-2. Computer Physics Communications 155, 2 (2003), 115--131.Google ScholarCross Ref
- A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton. 2022. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm. 271 (2022), 108171. Google ScholarCross Ref
- C. Wu, S. Bandara, T. Geng, A. Guo, P. Haghi, W. Sherman, V. Sachdeva, and M.C. Herbordt. 2022. Optimized Mappings for Symmetric Range-Limited Molecular Force Calculations on FPGAs. In International Conference on Field-Programmable Logic and Applications. Google ScholarCross Ref
- C. Wu, T. Geng, S. Bandara, C. Yang, V. Sachdeva, W. Sherman, and M.C. Herbordt. 2021. Upgrade of FPGA Range-Limited Molecular Dynamics to Handle Hundreds of Processors. In 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). Google ScholarCross Ref
- C. Wu, T. Geng, V. Sachdeva, W. Sherman, and M.C. Herbordt. 2020. A Communication-Efficient Multi-Chip Design for Range-Limited Molecular Dynamics. In 2020 IEEE High Performance extreme Computing Conference (HPEC).Google Scholar
- Q. Xiong and M.C. Herbordt. 2017. Bonded Force Computations on FPGAs. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). 72--75. Google ScholarCross Ref
- C. Yang, T. Geng, T. Wang, R. Patel, Q. Xiong, A. Sanaullah, C. Lin, V. Sachdeva, W. Sherman, and M.C. Herbordt. 2019. Fully Integrated FPGA Molecular Dynamics Simulations. In International Conference for High Performance Computing, Networking, Storage and Analysis. 1--31. Google ScholarDigital Library
- C. Yang, T. Geng, T. Wang, J. Sheng, C. Lin, V. Sachdeva, W. Sherman, and M.C. Herbordt. 2019. Molecular Dynamics Range-Limited Force Evaluation Optimized for FPGA. In 2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP). 263--271. Google ScholarCross Ref
- C. Yang, J. Sheng, R. Patel, A. Sanaullah, V. Sachdeva, and M.C. Herbordt. 2017. OpenCL for HPC with FPGAs: Case Study in Molecular Electrostatics. In 2017 IEEE High Performance Extreme Computing Conference (HPEC). 1--8. 8091078. Google ScholarCross Ref
- Ming Yuan, Qiang Liu, Quan Deng, Shengye Xiang, Lin Gan, Jinzhe Yang, Xiaohui Duan, Haohuan Fu, and Guangwen Yang. 2022. FPGA-Accelerated Tersoff Multi-body Potential for Molecular Dynamics Simulations. In Applied Reconfigurable Computing. Architectures, Tools, and Applications, Lin Gan, Yu Wang, Wei Xue, and Thomas Chau (Eds.). Springer Nature Switzerland, Cham, 17--31.Google Scholar
- Jiansong Zhang, Yongqiang Xiong, Ningyi Xu, Ran Shu, Bojie Li, Peng Cheng, Guo Chen, and Thomas Moscibroda. 2017. The Feniks FPGA operating system for cloud computing. In Proceedings of the 8th Asia-Pacific Workshop on Systems. 1--7.Google ScholarDigital Library
- Hongtao Zhao and Amedeo Caflisch. 2015. Molecular dynamics in drug design. European journal of medicinal chemistry 91 (2015), 4--14.Google Scholar
Index Terms
- FASDA: An FPGA-Aided, Scalable and Distributed Accelerator for Range-Limited Molecular Dynamics
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