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Author

Richard H. Larson

Other affiliations: Columbia University
Bio: Richard H. Larson is an academic researcher from D. E. Shaw Research. The author has contributed to research in topics: Massively parallel & Computation. The author has an hindex of 7, co-authored 12 publications receiving 1905 citations. Previous affiliations of Richard H. Larson include Columbia University.

Papers
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Journal ArticleDOI
01 Jul 2008
TL;DR: A massively parallel machine called Anton is described, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems and has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation.
Abstract: The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry, and medicine. A wide range of biologically interesting phenomena, however, occur over timescales on the order of a millisecond---several orders of magnitude beyond the duration of the longest current MD simulations. We describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized highspeed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods.

778 citations

Proceedings ArticleDOI
16 Nov 2014
TL;DR: The architecture of Anton 2 is tailored for fine-grained event-driven operation, which improves performance by increasing the overlap of computation with communication, and also allows a wider range of algorithms to run efficiently, enabling many new software-based optimizations.
Abstract: Anton 2 is a second-generation special-purpose supercomputer for molecular dynamics simulations that achieves significant gains in performance, programmability, and capacity compared to its predecessor, Anton 1. The architecture of Anton 2 is tailored for fine-grained event-driven operation, which improves performance by increasing the overlap of computation with communication, and also allows a wider range of algorithms to run efficiently, enabling many new software-based optimizations. A 512-node Anton 2 machine, currently in operation, is up to ten times faster than Anton 1 with the same number of nodes, greatly expanding the reach of all-atom bio molecular simulations. Anton 2 is the first platform to achieve simulation rates of multiple microseconds of physical time per day for systems with millions of atoms. Demonstrating strong scaling, the machine simulates a standard 23,558-atom benchmark system at a rate of 85 µs/day -- 180 times faster than any commodity hardware platform or general-purpose supercomputer.

509 citations

Proceedings ArticleDOI
14 Nov 2009
TL;DR: Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations is reported, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study.
Abstract: Anton is a recently completed special-purpose supercomputer designed for molecular dynamics (MD) simulations of biomolecular systems. The machine's specialized hardware dramatically increases the speed of MD calculations, making possible for the first time the simulation of biological molecules at an atomic level of detail for periods on the order of a millisecond---about two orders of magnitude beyond the previous state of the art. Anton is now running simulations on a timescale at which many critically important, but poorly understood phenomena are known to occur, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study. Here, we report Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations. We also discuss the manner in which novel algorithms have been coordinated with Anton's co-designed, application-specific hardware to achieve these results.

414 citations

Proceedings ArticleDOI
09 Jun 2007
TL;DR: A massively parallel machine called Anton is described, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems and is designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation.
Abstract: The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond--about three orders of magnitude beyond the duration of the longest current MD simulations.In this paper, we describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods.

340 citations

Proceedings ArticleDOI
13 Nov 2010
TL;DR: Key elements of Anton's approach, in addition to tightly integrated communication hardware, include formulating data transfer in terms of counted remote writes, leveraging fine-grained communication, and establishing fixed, optimized communication patterns.
Abstract: Strong scaling of scientific applications on parallel architectures is increasingly limited by communication latency. This paper describes the techniques used to mitigate latency in Anton, a massively parallel special-purpose machine that accelerates molecular dynamics (MD) simulations by orders of magnitude compared with the previous state of the art. Achieving this speedup required a combination of hardware mechanisms and software constructs to reduce network latency, sender and receiver overhead, and synchronization costs. Key elements of Anton's approach, in addition to tightly integrated communication hardware, include formulating data transfer in terms of counted remote writes, leveraging fine-grained communication, and establishing fixed, optimized communication patterns. Anton delivers software-to-software inter-node latency significantly lower than any other large-scale parallel machine, and the total critical-path communication time for an Anton MD simulation is less than 4% that of the next fastest MD platform.

28 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
01 Jun 2010-Proteins
TL;DR: A new force field, which is termed Amber ff99SB‐ILDN, exhibits considerably better agreement with the NMR data and is validated against a large set of experimental NMR measurements that directly probe side‐chain conformations.
Abstract: Recent advances in hardware and software have enabled increasingly long molecular dynamics (MD) simulations of biomolecules, exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, we further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, we used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, we optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mechanical calculations. Finally, we used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of experimental NMR measurements that directly probe side-chain conformations. The new force field, which we have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data. Proteins 2010. © 2010 Wiley-Liss, Inc.

4,590 citations

Journal ArticleDOI
28 Oct 2011-Science
TL;DR: Results of atomic-level molecular dynamics simulations of 12 proteins reveal a set of common principles underlying the folding of 12 structurally diverse proteins that spontaneously and repeatedly fold to their experimentally determined native structures.
Abstract: An outstanding challenge in the field of molecular biology has been to understand the process by which proteins fold into their characteristic three-dimensional structures. Here, we report the results of atomic-level molecular dynamics simulations, over periods ranging between 100 μs and 1 ms, that reveal a set of common principles underlying the folding of 12 structurally diverse proteins. In simulations conducted with a single physics-based energy function, the proteins, representing all three major structural classes, spontaneously and repeatedly fold to their experimentally determined native structures. Early in the folding process, the protein backbone adopts a nativelike topology while certain secondary structure elements and a small number of nonlocal contacts form. In most cases, folding follows a single dominant route in which elements of the native structure appear in an order highly correlated with their propensity to form in the unfolded state.

1,673 citations

Journal ArticleDOI
15 Oct 2010-Science
TL;DR: Simulation of the folding of a WW domain showed a well-defined folding pathway and simulation of the dynamics of bovine pancreatic trypsin inhibitor showed interconversion between distinct conformational states.
Abstract: Molecular dynamics (MD) simulations are widely used to study protein motions at an atomic level of detail, but they have been limited to time scales shorter than those of many biologically critical conformational changes. We examined two fundamental processes in protein dynamics—protein folding and conformational change within the folded state—by means of extremely long all-atom MD simulations conducted on a special-purpose machine. Equilibrium simulations of a WW protein domain captured multiple folding and unfolding events that consistently follow a well-defined folding pathway; separate simulations of the protein’s constituent substructures shed light on possible determinants of this pathway. A 1-millisecond simulation of the folded protein BPTI reveals a small number of structurally distinct conformational states whose reversible interconversion is slower than local relaxations within those states by a factor of more than 1000.

1,650 citations

Journal ArticleDOI
TL;DR: An implementation of generalized Born implicit solvent all-atom classical molecular dynamics within the AMBER program package that runs entirely on CUDA enabled NVIDIA graphics processing units (GPUs) and shows performance that is on par with, and in some cases exceeds, that of traditional supercomputers.
Abstract: We present an implementation of generalized Born implicit solvent all-atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA enabled NVIDIA graphics processing units (GPUs). We discuss the algorithms that are used to exploit the processing power of the GPUs and show the performance that can be achieved in comparison to simulations on conventional CPU clusters. The implementation supports three different precision models in which the contributions to the forces are calculated in single precision floating point arithmetic but accumulated in double precision (SPDP), or everything is computed in single precision (SPSP) or double precision (DPDP). In addition to performance, we have focused on understanding the implications of the different precision models on the outcome of implicit solvent MD simulations. We show results for a range of tests including the accuracy of single point force evaluations and energy conservation as well as structural properties pertainining to protein dynamics. The numerical noise due to rounding errors within the SPSP precision model is sufficiently large to lead to an accumulation of errors which can result in unphysical trajectories for long time scale simulations. We recommend the use of the mixed-precision SPDP model since the numerical results obtained are comparable with those of the full double precision DPDP model and the reference double precision CPU implementation but at significantly reduced computational cost. Our implementation provides performance for GB simulations on a single desktop that is on par with, and in some cases exceeds, that of traditional supercomputers.

1,645 citations