Analysis, Tracing, Characterization and Performance Modeling of Select ASCI Applications for BlueGene/L Using Parallel Discrete Event Simulation

Caltech's Jet Propulsion Laboratory (JPL) and Center for Advanced Computer Architecture (CACR) are conducting application and simulation analyses of Blue Gene/L[1] in order to establish a range of effectiveness of the architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution

Tracing Communications and Computational Workload in LJS (Lennard-Jones with Spatial Decomposition)

LJS (Lennard-Jones with Spatial decomposition) is a molecular dynamics application developed by Steve Plimpton at Sandia National Laboratories [1]. It performs thermodynamic simulations of a system containing fixed large number (millions) of atoms or molecules confined within a regular, three-dimensional domain. Since the simulations model interactions on atomic scale, the computations carried out in a single timestep (iteration) correspond to femtoseconds of the real time. Hence, a meaningful simulation of the evolution of the system's state typically requires a large number (thousands and more) of timesteps. The particles in LJS are represented as material points subjected to forces resulting from interactions with other particles. While the general case involves N-body solvers, LJS implements only pair-wise material point interactions using derivative of Lennard-Jones potential energy for each particle pair to evaluate the acting forces. The velocities and positions of particles are updated by integrating Newton's equations (classical molecular dynamics). The interaction range depends on the modeled problem type; LJS focuses on short-range forces, implementing a cutoff distance rc outside which the interactions are ignored. The computational complexity of O(N2), characteristic for systems with long-range interactions, is therefore substantially alleviated. LJS deploys spatial decomposition of the domain volume to distribute the computations across the available processors on a parallel computer. The decomposition process uniformly divides parallelepiped containing all particles into volumes equal in size and as close in shape to a cube as possible, assigning each of such formed cells to a CPU. The correctness of computations requires the positions of some particles (depending on the value of rc) residing in the neighboring cells to be known to the local process. This information is exchanged in every timestep via explicit communication with the neighbor nodes in all three dimensions (for details see [2]). LJS also takes the advantage of the third Newton's law to calculate the force only once per particle pair; if the involved particles belong to cells located on different processors, the results are forwarded to the other node in a "reverse communication" phase. Besides communications occurring in every iteration, additional messages are sent once every preset number of timesteps. Their purpose is to adjust cell assignments of particles due to their movement. To minimize the overhead of the construction of particle neighbor lists, LJS replaces rc with extended cutoff radius rs (rs > rc), which accounts for possible particle movement before any list updates need to be carried out. Due to a relatively small impact of that phase on the overall behavior of the application, we ignored it in our analysis

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Distributed video coding in wireless multimedia sensor network for multimedia broadcasting

Recently the development of Distributed Video Coding (DVC) has provided the promising theory support to realize the infrastructure of Wireless Multimedia Sensor Network (WMSN), which composed of autonomous hardware for capturing and transmission of quality audio-visual content. The implementation of DVC in WMSN can better solve the problem of energy constraint of the sensor nodes due to the benefit of lower computational encoder in DVC. In this paper, a practical DVC scheme, pixel-domain Wyner-Ziv(PDWZ) video coding, with slice structure and adaptive rate selection(ARS) is proposed to solve the certain problems when applying DVC into WMSN. Firstly, the proposed slice structure in PDWZ has extended the feasibility of PDWZ to work with any interleaver size used in Slepian-wolf turbo codec for heterogeneous applications. Meanwhile, based on the slice structure, an adaptive code rate selection has been proposed aiming at reduce the system delay occurred in feedback request. The simulation results clearly showed the enhancement in R-D performance and perceptual quality. It also can be observed that system delay caused by frequent feedback is greatly reduced, which gives a promising support for WMSN with low latency and facilitates the QoS management

MGOS: A library for molecular geometry and its operating system

The geometry of atomic arrangement underpins the structural understanding of molecules in many fields. However, no general framework of mathematical/computational theory for the geometry of atomic arrangement exists. Here we present "Molecular Geometry (MG)'' as a theoretical framework accompanied by "MG Operating System (MGOS)'' which consists of callable functions implementing the MG theory. MG allows researchers to model complicated molecular structure problems in terms of elementary yet standard notions of volume, area, etc. and MGOS frees them from the hard and tedious task of developing/implementing geometric algorithms so that they can focus more on their primary research issues. MG facilitates simpler modeling of molecular structure problems; MGOS functions can be conveniently embedded in application programs for the efficient and accurate solution of geometric queries involving atomic arrangements. The use of MGOS in problems involving spherical entities is akin to the use of math libraries in general purpose programming languages in science and engineering. (C) 2019 The Author(s). Published by Elsevier B.V