ProtoMol, an object-oriented framework for prototyping novel algorithms for molecular dynamics

Matthey, Thierry; Cickovski, Trevor; Hampton, Scott; Ko, A.; Ma, Qun; Nyerges, Matthew; Raeder, Troy; Slabach, Thomas; Izaguirre, Jesús A.

doi:10.1145/1024074.1024075

Cited by 72 publications

(56 citation statements)

References 32 publications

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“…This effort lead to the design of two efficient software tools facilitating the process of setting up an IMS : NAMD and VMD [Phillips et al (2005)] [Nelson et al (1995))]. The underlying exchange protocol is also supported by ProtoMol [Matthey et al (2004)], LAMMPS [Plimpton (1995)], HOOMD-blue [Anderson et al (2008)] and any software using the MDDriver library [Delalande et al (2009)]. Similar projects proposing an interactive display for molecular simulations exist, such as the Java3D interface proposed in Knoll & Mirzaei (2003) and Vormoor (2001), or the Protein Interactive Theater [Prins et al (1999)].…”

Section: Introductionmentioning

confidence: 99%

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

Tek¹,

Chavent²,

Baaden³

et al. 2012

Protein-Protein Interactions - Computational and Experimental Tools

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Section: Introductionmentioning

confidence: 99%

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

Tek¹,

Chavent²,

Baaden³

et al. 2012

Protein-Protein Interactions - Computational and Experimental Tools

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“…8 Another example is the ProtoMol framework, which was designed especially for computational experimentation and so has well-defined partitions among computations. 9 We have found that the acceleratable kernel not only comprises more than 90 percent of execution time with ProtoMol, but the modularity enables straightforward integration of an FPGA accelerator. 10 …”

Section: Methods 4: Living With Amdahl's Lawmentioning

confidence: 95%

Achieving High Performance with FPGA-Based Computing

et al. 2007

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Numerous application areas, including bioinformatics and computational biology, demand increasing amounts of processing capability. In many cases, the computation cores and data types are suited to field-programmable gate arrays. The challenge is identifying the design techniques that can extract high performance potential from the FPGA fabric.Accelerating high-performance computing (HPC) applications with field-programmable gate arrays (FPGAs) can potentially deliver enormous performance. A thousand-fold parallelism is possible, especially for low-precision computations. Moreover, since control is configured into the logic itself, overhead instructions-such as array indexing and loop computationsneed not be emulated, and every operation can deliver payload.At the same time, using FPGAs presents significant challenges 1 including low operating frequency-an FPGA clocks at one-tenth that of a high-end microprocessor. Another is simply Amdahl's law: To achieve the speedup factors required for user acceptance of a new technology (preferably 50 times), 2 at least 98 percent of the target application must lend itself to substantial acceleration. As a result, HPC/FPGA application performance is unusually sensitive to the implementation's quality.The problem of achieving significant speedups on a new architecture without expending exorbitant development effort, and while retaining flexibility, portability, and maintainability, is a classic one. In this case, accelerating HPC applications with FPGAs is similar to that of porting uniprocessor applications to massively parallel processors, with two key distinctions:• FPGAs are far more different from uniprocessors than MPPs are from uniprocessors, and• the process of parallelizing code for MPPs, while challenging, is still better understood and supported than porting codes to FPGAs.Lawrence Snyder stated the three basic parameters for the MPP portability problem. 3 First, a parallel solution using P processors can improve the best sequential solution by a factor of P, at most. Second, HPC problems tend to have third-or fourth-order complexity, and so parallel computation, while essential, offers only modest benefits. Third, "the whole force of parallelism must be transferred to the problem, not converted to 'heat' of implementational overhead."Researchers have addressed the portability problem periodically over the past 30 years, with well-known approaches involving language design, optimizing compilers, emulation, software engineering tools and methods, and function and application libraries. It is generally agreed that compromises are required: Either restrict the variety of architectures or scope of application, or bound expectations of performance or ease of implementation.

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“…A C + + implementation of S2HMC is provided in the program PROTOMOL 2.1. 13 Here we use a Python implementation for easier understanding. The reader interested in implementation details will find them in this section and can safely skip the next two sections.…”

Section: Separable Shadow Hybrid Monte Carlomentioning

confidence: 99%

“…Tests were performed with the open-source simulator PROTOMOL, 13 which uses the CHARMM 22 force field. 22,23 We tested the method using five boxes of flexible TIP3P ͑Ref.…”

Section: Numerical Testsmentioning

confidence: 99%

A separable shadow Hamiltonian hybrid Monte Carlo method

Sweet

Hampton

Skeel

et al. 2009

The Journal of Chemical Physics

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Hybrid Monte Carlo ͑HMC͒ is a rigorous sampling method that uses molecular dynamics ͑MD͒ as a global Monte Carlo move. The acceptance rate of HMC decays exponentially with system size. The shadow hybrid Monte Carlo ͑SHMC͒ was previously introduced to reduce this performance degradation by sampling instead from the shadow Hamiltonian defined for MD when using a symplectic integrator. SHMC's performance is limited by the need to generate momenta for the MD step from a nonseparable shadow Hamiltonian. We introduce the separable shadow Hamiltonian hybrid Monte Carlo ͑S2HMC͒ method based on a formulation of the leapfrog/Verlet integrator that corresponds to a separable shadow Hamiltonian, which allows efficient generation of momenta. S2HMC gives the acceptance rate of a fourth order integrator at the cost of a second-order integrator. Through numerical experiments we show that S2HMC consistently gives a speedup greater than two over HMC for systems with more than 4000 atoms for the same variance. By comparison, SHMC gave a maximum speedup of only 1.6 over HMC. S2HMC has the additional advantage of not requiring any user parameters beyond those of HMC. S2HMC is available in the program PROTOMOL 2.1. A Python version, adequate for didactic purposes, is also in MDL ͑http://mdlab.sourceforge.net/s2hmc͒.

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ProtoMol, an object-oriented framework for prototyping novel algorithms for molecular dynamics

Cited by 72 publications

References 32 publications

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

Achieving High Performance with FPGA-Based Computing

A separable shadow Hamiltonian hybrid Monte Carlo method

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