M. J. Harvey

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.

The Houk–List transition states for organocatalytic mechanisms revisited

The ten year old Houk–List model for rationalising the origin of stereoselectivity in the organocatalysed intermolecular aldol addition is revisited, using a variety of computational techniques that have been introduced or improved since the original study. Even for such a relatively small system, the role of dispersion interactions is shown to be crucial, along with the use of basis sets where the superposition errors are low. An NCI (non-covalent interactions) analysis of the transition states is able to identify the noncovalent interactions that influence the selectivity of the reaction, confirming the role of the electrostatic NCHδ+⋯Oδ− interactions. Simple visual inspection of the NCI surfaces is shown to be a useful tool for the design of alternative reactants. Alternative mechanisms, such as proton-relays involving a water molecule or the Hajos–Parrish alternative, are shown to be higher in energy and for which computed kinetic isotope effects are incompatible with experiment. The Amsterdam manifesto, which espouses the principle that scientific data should be citable, is followed here by using interactive data tables assembled via calls to the data DOI (digital-object-identifiers) for calculations held on a digital data repository and themselves assigned a DOI.

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.

High-throughput molecular dynamics: the powerful new tool for drug discovery.

Molecular dynamics simulations are capable of resolving molecular recognition processes with chemical accuracy, but their practical application is popularly considered limited to the timescale accessible to a single simulation, which is far below biological timescales. In this perspective article, we propose that the true limiting factor for molecular dynamics is rather the high hardware and electrical power costs, which constrain not only the length of runs but also the number that can be performed concurrently. As a result of innovation in accelerator processors and high-throughput protocols, the cost of molecular dynamics sampling has been dramatically reduced and we argue that molecular dynamics simulation is now placed to become a key technology for in silico drug discovery in terms of binding pathways, poses, kinetics and affinities.

SPECTRa: The Deposition and Validation of Primary Chemistry Research Data in Digital Repositories

The SPECTRa (Submission, Preservation and Exposure of Chemistry Teaching and Research Data) project has investigated the practices of chemists in archiving and disseminating primary chemical data from academic research laboratories. To redress the loss of the large amount of data never archived or disseminated, we have developed software for data publication into departmental and institutional Open Access digital repositories (DSpace). Data adhering to standard formats in selected disciplines (crystallography, NMR, computational chemistry) is transformed to XML (CML, Chemical Markup Language) which provides added validation. Context-specific chemical metadata and persistent Handle identifiers are added to enable long-term data reuse. It was found essential to provide an embargo mechanism, and policies for operating this and other processes are presented.

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

The impact of accelerator processors for high-throughput molecular modeling and simulation

The recent introduction of cost-effective accelerator processors (APs), such as the IBM Cell processor and Nvidias graphics processing units (GPUs), represents an important technological innovation which promises to unleash the full potential of atomistic molecular modeling and simulation for the biotechnology industry. Present APs can deliver over an order of magnitude more floating-point operations per second (flops) than standard processors, broadly equivalent to a decade of Moores law growth, and significantly reduce the cost of current atom-based molecular simulations. In conjunction with distributed and grid-computing solutions, accelerated molecular simulations may finally be used to extend current in silico protocols by the use of accurate thermodynamic calculations instead of approximate methods and simulate hundreds of protein-ligand complexes with full molecular specificity, a crucial requirement of in silico drug discovery workflows.

A survey of computational molecular science using graphics processing units

Computational molecular science is a very computationally intense discipline, and the use of parallel programming and high‐performance computers well established as being necessary to support research activities. Recently, graphical processing units (GPUs) have garnered substantial interest as alternative sources of high‐performance computing capability. These devices, although capable of very high rates of floating‐point arithmetic, are also intrinsically highly parallel processors and their effective exploitation typically requires extensive software refactoring and development. Here, we review the current landscape of GPU hardware and programming models, and provide a snapshot survey of the current state of computational molecular science codes ported to GPUs to help domain scientists and software developers understand the potential benefits and drawbacks of this new computing architecture.