G. De Fabritiis

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

The high arithmetic performance and intrinsic parallelism of recent graphical processing units (GPUs) can offer a technological edge for molecular dynamics simulations. ACEMD is a production-class biomolecular dynamics (MD) engine supporting CHARMM and AMBER force fields. Designed specifically for GPUs it is able to achieve supercomputing scale performance of 40 ns/day for all-atom protein systems with over 23 000 atoms. We provide a validation and performance evaluation of the code and run a microsecond-long trajectory for an all-atom molecular system in explicit TIP3P water on a single workstation computer equipped with just 3 GPUs. We believe that microsecond time scale molecular dynamics on cost-effective hardware will have important methodological and scientific implications.

An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware

The smooth particle mesh Ewald summation method is widely used to efficiently compute long-range electrostatic force terms in molecular dynamics simulations, and there has been considerable work in developing optimized implementations for a variety of parallel computer architectures. We describe an implementation for Nvidia graphical processing units (GPUs) which are general purpose computing devices with a high degree of intrinsic parallelism and arithmetic performance. We find that, for typical biomolecular simulations (e.g., DHFR, 26K atoms), a single GPU equipped workstation is able to provide sufficient performance to permit simulation rates of ≈50 ns/day when used in conjunction with the ACEMD molecular dynamics package (1) and exhibits an accuracy comparable to that of a reference double-precision CPU implementation.

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

Although molecular dynamics simulation methods are useful in the modeling of macromolecular systems, they remain computationally expensive, with production work requiring costly high-performance computing (HPC) resources. We review recent innovations in accelerating molecular dynamics on graphics processing units (GPUs), and we describe GPUGRID, a volunteer computing project that uses the GPU resources of nondedicated desktop and workstation computers. In particular, we demonstrate the capability of simulating thousands of all-atom molecular trajectories generated at an average of 20 ns/day each (for systems of approximately 30 000-80 000 atoms). In conjunction with a potential of mean force (PMF) protocol for computing binding free energies, we demonstrate the use of GPUGRID in the computation of accurate binding affinities of the Src SH2 domain/pYEEI ligand complex by reconstructing the PMF over 373 umbrella sampling windows of 55 ns each (20.5 mus of total data). We obtain a standard free energy of binding of -8.7 +/- 0.4 kcal/mol within 0.7 kcal/mol from experimental results. This infrastructure will provide the basis for a robust system for high-throughput accurate binding affinity prediction.

Multiscale modeling of liquids with molecular specificity

The separation between molecular and mesoscopic length and time scales poses a severe limit to molecular simulations of mesoscale phenomena. We describe a hybrid multiscale computational technique which addresses this problem by keeping the full molecular nature of the system where it is of interest and coarse graining it elsewhere. This is made possible by coupling molecular dynamics with a mesoscopic description of realistic liquids based on Landaus fluctuating hydrodynamics. We show that our scheme correctly couples hydrodynamics and that fluctuations, at both the molecular and continuum levels, are thermodynamically consistent. Hybrid simulations of sound waves in bulk water and reflected by a lipid monolayer are presented as illustrations of the scheme.

On Size and Growth of Business Firms

We study size and growth distributions of products and business firms in the context of a given industry. Firm size growth is analyzed in terms of two basic mechanisms, i.e., the increase of the number of new elementary business units and their size growth. We find a power-law relationship between size and the variance of growth rates for both firms and products, with an exponent between −0.17 and −0.15, with a remarkable stability upon aggregation. We then introduce a simple and general model of proportional growth for both the number of firm independent constituent units and their size, which conveys a good representation of the empirical evidences. This general and plausible generative process can account for the observed scaling in a wide variety of economic and industrial systems. Our findings contribute to shed light on the mechanisms that sustain economic growth in terms of the relationships between the size of economic entities and the number and size distribution of their elementary components.

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.

Fluctuating hydrodynamic modeling of fluids at the nanoscale

A good representation of mesoscopic fluids is required to combine with molecular simulations at larger length and time scales [De Fabritiis, Phys. Rev. Lett. 97, 134501 (2006)]. However, accurate computational models of the hydrodynamics of nanoscale molecular assemblies are lacking, at least in part because of the stochastic character of the underlying fluctuating hydrodynamic equations. Here we derive a finite volume discretization of the compressible isothermal fluctuating hydrodynamic equations over a regular grid in the Eulerian reference system. We apply it to fluids such as argon at arbitrary densities and water under ambient conditions. To that end, molecular dynamics simulations are used to derive the required fluid properties. The equilibrium state of the model is shown to be thermodynamically consistent and correctly reproduces linear hydrodynamics including relaxation of sound and shear modes. We also consider nonequilibrium states involving diffusion and convection in cavities with no-slip boundary conditions.

On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations

High-throughput molecular dynamics (MD) simulations are a computational method consisting of using multiple short trajectories, instead of few long ones, to cover slow biological time scales. Compared to long trajectories this method offers the possibility to start the simulations in successive batches, building a knowledgeable model of the available data to inform subsequent new simulations iteratively. Here, we demonstrate an automatic, iterative, on-the-fly method for learning and sampling molecular simulations in the context of ligand binding for the case of trypsin-benzamidine binding. The method uses Markov state models to learn a simplified model of the simulations and decide where best to sample from, achieving a converged binding affinity in approximately one microsecond, 1 order of magnitude faster than classical sampling. This method demonstrates for the first time the potential of adaptive sampling schemes in the case of ligand binding.

Mesoscopic Models of Liquid/Solid Phase Transitions

A generalization of mesoscopic Lattice-Boltzmann models aimed at describing flows with solid/liquid phase transitions is presented. It exhibits lower computational costs with respect to the numerical schemes resulting from differential models. Moreover it is suitable to describe chaotic motions in the mushy zone.

Swan: A tool for porting CUDA programs to OpenCL

Abstract The use of modern, high-performance graphical processing units (GPUs) for acceleration of scientific computation has been widely reported. The majority of this work has used the CUDA programming model supported exclusively by GPUs manufactured by NVIDIA. An industry standardisation effort has recently produced the OpenCL specification for GPU programming. This offers the benefits of hardware-independence and reduced dependence on proprietary tool-chains. Here we describe a source-to-source translation tool, “Swan” for facilitating the conversion of an existing CUDA code to use the OpenCL model, as a means to aid programmers experienced with CUDA in evaluating OpenCL and alternative hardware. While the performance of equivalent OpenCL and CUDA code on fixed hardware should be comparable, we find that a real-world CUDA application ported to OpenCL exhibits an overall 50% increase in runtime, a reduction in performance attributable to the immaturity of contemporary compilers. The ported application is shown to have platform independence, running on both NVIDIA and AMD GPUs without modification. We conclude that OpenCL is a viable platform for developing portable GPU applications but that the more mature CUDA tools continue to provide best performance. Program summary Program title: Swan Catalogue identifier: AEIH_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEIH_v1_0.html Program obtainable from: CPC Program Library, Queens University, Belfast, N. Ireland Licensing provisions: GNU Public License version 2 No. of lines in distributed program, including test data, etc.: 17 736 No. of bytes in distributed program, including test data, etc.: 131 177 Distribution format: tar.gz Programming language: C Computer: PC Operating system: Linux RAM: 256 Mbytes Classification: 6.5 External routines: NVIDIA CUDA, OpenCL Nature of problem: Graphical Processing Units (GPUs) from NVIDIA are preferentially programed with the proprietary CUDA programming toolkit. An alternative programming model promoted as an industry standard, OpenCL, provides similar capabilities to CUDA and is also supported on non-NVIDIA hardware (including multicore ×86 CPUs, AMD GPUs and IBM Cell processors). The adaptation of a program from CUDA to OpenCL is relatively straightforward but laborious. The Swan tool facilitates this conversion. Solution method: Swan performs a translation of CUDA kernel source code into an OpenCL equivalent. It also generates the C source code for entry point functions, simplifying kernel invocation from the host program. A concise host-side API abstracts the CUDA and OpenCL APIs. A program adapted to use Swan has no dependency on the CUDA compiler for the host-side program. The converted program may be built for either CUDA or OpenCL, with the selection made at compile time. Restrictions: No support for CUDA C++ features Running time: Nominal