Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer

By D. E. Shaw, J. P. Grossman, Joseph A. Bank, Brannon Batson, J. A. Butts, Jack C. Chao, Martin M. Deneroff, Ron O. Dror, Amos Even, Christopher H. Fenton and 35 more. International Conference for High Performance Computing, Networking, Storage and Analysis, 2014

529 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Hardware and Architecture, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/20e4eadb02bd2307ed1e47f9defce1cfb6c3b53f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. John M. Shalf

In Hardware and Architecture

1. Charles Siegel
2. Abhinav Vishnu
3. John M. Shalf
4. Garrett B. Goh
5. Nathan Oken Hodas

In Molecular Biology

1. Robin Pearce
2. Charles Siegel
3. Abhinav Vishnu
4. Garrett B. Goh
5. Nathan Oken Hodas
6. Wonpil Im
7. Sunhwan Jo
8. Yang Zhang

Citations in the corpus

• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017, background): “…underlying chemical concepts. first factor is being addressed with development of GPU-accelerated code for molecular modeling [36] and special-purpose chemistry supercomputers [34], but the second factor requires solutions grounded in further fundamental research, which is often a slower process.”
• Molecular dynamics simulation for all (2018, unknown): “Highly specialized hardware (Shaw et al., 2008, 2014) has led to a major increase in maximum achievable speed, allowing certain simulations to reach millisecond timescales.”
• CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates (2019, methodology): “For a sufficient sampling of glycan conformations, each system was simulated for 2 μs using Anton 2 (Shaw et al. 2014) (see Materials and methods for simulation details; see also Supporting Movies, HIVenv_cryst.”
• Toward the solution of the protein structure prediction problem (2021, background): “Since then, technology has progressed through the use of more advanced computer architectures (122, 123) and force fi elds (124 – 130), but molecular dynamics-based structure prediction still remains impractical for proteins of typical length.”
• The future of computing beyond Moore’s Law (2020, background): “…demonstrated successes in creating science-targeted accelerators such as D.E. Shaw’s Anton, which accelerates molecular dynamics (MD) simulations nearly 180 × over contemporary high-performance computing (HPC) systems [12], and the GRAPE series of specialized accelerators for cosmology and MD [13].”