Abstract Molecular dynamics simulations have a prominent role in biophysics and drug discovery due to the atomistic information they provide on the structure, energetics and dynamics of biomolecules. Specialized software packages are required to analyze simulated trajectories, either interactively or via scripts, to derive quantities of interest and provide insight for further experiments. This paper presents the Density Profile Tool, a package that enhances the Visual Molecular Dynamics environment with the ability to interactively compute and visualize 1-D projections of various density functions of molecular models. We describe how the plugin is used to perform computations both via a graphical interface and programmatically. Results are presented for realistic examples, all-atom bilayer models, showing how mass and electron densities readily provide measurements such as membrane thickness, location of structural elements, and how they compare to X-ray diffraction experiments. Program summary Program title: Density Profile Tool Catalogue identifier: AEQM_v1_0 Program summary URL: http: cpc.cs.qub.ac.uk summaries AEQM_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: yes No. of lines in distributed program, including test data, etc.: 1742 No. of bytes in distributed program, including test data, etc.: 12764 Distribution format: tar.gz Programming language: TCL TK. Computer: Any, with or without graphical display. Operating system: Linux Unix, OSX, Windows. RAM: VMD should be able to hold the trajectory in memory. Classification: 3, 23. External routines: VMD (version 1.9 or higher) ( http: www.ks.uiuc.edu Research vmd ). Nature of problem: Compute and visualize one-dimensional density profiles of molecular dynamics trajectories in the VMD environment, either interactively or programmatically. Solution method: Density profiles are computed by binning the simulation space into slabs of finite thickness. A graphical user interface allows the choice of the atomic property (number, mass, charge, electrons) and the details of the binning. Restrictions: The current version only supports orthorhombic cells. Unusual features: The Density Profile Tool is not a standalone program but a plug-in that enhances VMD’s analysis features. Running time: A contemporary PC completes the analysis of 500 frames of the example system discussed in the paper (35,000 atoms) in under 1 min.
Other analysis packages contain command-line tools to perform density profile computations similar to the one described here. GROMACS' distribution, for example, provides +g_density+, a stand-alone executable meant to be used from the command-line or in shell scripts @cite_0 . This approach has the advantage of not being tied to a specific graphical or scripting environment; however, it can be limiting in three respects: first, a GUI may be desirable for quick one-off computations; second, binaries mostly require GROMACS-specific file formats and topologies; finally, performing computations in shell scripts implies a programming model in which text files are used to store intermediate results -- a model which is more cumbersome than manipulating variables, the route afforded by conventional programming languages like TCL or Python.
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Abstract In this paper, we consider the problem of recovering a compactly supported multivariate function from a collection of pointwise samples of its Fourier transform taken nonuniformly. We do this by using the concept of weighted Fourier frames. A seminal result of Beurling shows that sampling points give rise to a classical Fourier frame provided they are relatively separated and of sufficient density. However, this result does not allow for arbitrary clustering of sampling points, as is often the case in practice. Whilst keeping the density condition sharp and dimension independent, our first result removes the separation condition and shows that density alone suffices. However, this result does not lead to estimates for the frame bounds. A known result of Grochenig provides explicit estimates, but only subject to a density condition that deteriorates linearly with dimension. In our second result we improve these bounds by reducing the dimension dependence. In particular, we provide explicit frame bounds which are dimensionless for functions having compact support contained in a sphere. Next, we demonstrate how our two main results give new insight into a reconstruction algorithm—based on the existing generalized sampling framework—that allows for stable and quasi-optimal reconstruction in any particular basis from a finite collection of samples. Finally, we construct sufficiently dense sampling schemes that are often used in practice—jittered, radial and spiral sampling schemes—and provide several examples illustrating the effectiveness of our approach when tested on these schemes. Abstract A parallel message-passing implementation of a molecular dynamics (MD) program that is useful for bio(macro)molecules in aqueous environment is described. The software has been developed for a custom-designed 32-processor ring GROMACS (GROningen MAchine for Chemical Simulation) with communication to and from left and right neighbours, but can run on any parallel system onto which a a ring of processors can be mapped and which supports PVM-like block send and receive calls. The GROMACS software consists of a preprocessor, a parallel MD and energy minimization program that can use an arbitrary number of processors (including one), an optional monitor, and several analysis tools. The programs are written in ANSI C and available by ftp (information: gromacs@chem.rug.nl). The functionality is based on the GROMOS (GROningen MOlecular Simulation) package (van Gunsteren and Berendsen, 1987; BIOMOS B.V., Nijenborgh 4, 9747 AG Groningen). Conversion programs between GROMOS and GROMACS formats are included. The MD program can handle rectangular periodic boundary conditions with temperature and pressure scaling. The interactions that can be handled without modification are variable non-bonded pair interactions with Coulomb and Lennard-Jones or Buckingham potentials, using a twin-range cut-off based on charge groups, and fixed bonded interactions of either harmonic or constraint type for bonds and bond angles and either periodic or cosine power series interactions for dihedral angles. Special forces can be added to groups of particles (for non-equilibrium dynamics or for position restraining) or between particles (for distance restraints). The parallelism is based on particle decomposition. Interprocessor communication is largely limited to position and force distribution over the ring once per time step. Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory. To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with user-defined dynamic generations. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times. NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8 for Cassandra, 85.0 for Lucene and 96.45 for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2c has no negative impact on application throughput or memory usage. In this paper, we study the problem of sub-dataset analysis over distributed file systems, e.g., the Hadoop file system. Our experiments show that the sub-datasets distribution over HDFS blocks, which is hidden by HDFS, can often cause corresponding analyses to suffer from a seriously imbalanced or inefficient parallel execution. Specifically, the content clustering of sub-datasets results in some computational nodes carrying out much more workload than others; furthermore, it leads to inefficient sampling of sub-datasets, as analysis programs will often read large amounts of irrelevant data. We conduct a comprehensive analysis on how imbalanced computing patterns and inefficient sampling occur. We then propose a storage distribution aware method to optimize sub-dataset analysis over distributed storage systems referred to as DataNet. First, we propose an efficient algorithm to obtain the meta-data of sub-dataset distributions. Second, we design an elastic storage structure called ElasticMap based on the HashMap and BloomFilter techniques to store the meta-data. Third, we employ distribution-aware algorithms for sub-dataset applications to achieve balanced and efficient parallel execution. Our proposed method can benefit different sub-dataset analyses with various computational requirements. Experiments are conducted on PRObEs Marmot 128-node cluster testbed and the results show the performance benefits of DataNet.