Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

The Standard Template Adaptive Parallel Library ( stapl ) is a parallel programming framework that extends C++ and stl with support for parallelism. stapl provides a collection of parallel data structures ( pContainers ) and algorithms ( pAlgorithms ) and a generic methodology for extending them to provide customized functionality. stapl pContainers are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. They also provide appropriate interfaces that can be used by generic pAlgorithms . In this work, we present the design and implementation of the stapl associative pContainers : pMap , pSet , pMultiMap , pMultiSet , pHashMap , and pHashSet . These containers provide optimal insert, search, and delete operations for a distributed collection of elements based on keys. Their methods include counterparts of the methods provided by the stl associative containers, and also some asynchronous (non-blocking) variants that can provide improved performance in parallel. We evaluate the performance of the stapl associative pContainers on an IBM Power5 cluster, an IBM Power3 cluster, and on a linux-based Opteron cluster, and show that the new pContainer asynchronous methods, generic pAlgorithms (e.g., pfind ) and a sort application based on associative pContainers , all provide good scalability on more than 103processors.

Associative Parallel Containers in STAPL

In this paper, we present a framework for studying folding problems from a motion planning perspective In particular, all folding objects are modeled as tree-like multi-link articulated `robots'', where fold positions correspond to joints and areas that cannot fold correspond to links This formulation allows us to apply recent techniques developed in the robotics motion planning community for articulated objects with many degrees of freedom (many links) to folding problems An important benefit of this approach is that it not only allows us to study foldability questions, such as, can one object be folded (or unfolded) into another object, but also enables us to study the dynamic folding process itself The framework proposed here has application in traditional motion planning areas such as automation, teaching through demonstration, animation, and most importantly, presents a different approach to the most profound problem in computational biology: protein struction prediction Indeed, our preliminary experimental results with traditional paper crafts (eg, box folding) and a relatively small protein are quite promising

A Motion Planning Approach to Folding: From Paper Craft to Protein Structure Prediction

This paper introduces a hybrid scheme for simulating rigid bodies in contact. We use an adaptive strategy for handling two different contact situations, 'bouncing' and 'steady'. To handle contact for rigid bodies, we use two impulse-based methods to explicitly or implicitly compute impulses due to collision impact. These two methods are used so that different impulse methods are applied adaptively depending on the contact situations. Our experiments show that our simple adaptive simulation scheme enables efficient and physically-correct dynamic simulation involving rigid-body contacts with Coulomb friction. This adaptive scheme was incorporated into our dynamic simulator, called I-GMS, which supports various types of simulations. We demonstrate the simulation results of our scheme using a ball falling on a flat surface in three dimensions.

/pdf/hybrid-dynamic-simulation-of-rigid-body-contact-with-coulomb-4qbdggr1rz.pdf

Hybrid dynamic simulation of rigid-body contact with Coulomb friction

This paper introduces I-GMS, a dynamic simulator that accommodates various systems of rigid bodies, ranging from a single free flying rigid object to complex linkages such as those needed for robotic systems or human body simulation. I-GMS's object-oriented design provides a generic framework for representing various types of rigid body systems, and exploits virtual functions to apply common kinematic and dynamic functionalities to them. Moreover, I-GMS supports interactive simulation so that it easily incorporates user-input for the on-line editing and modification of trajectories. User-interaction is achieved with the PHANToM haptic device which runs as an integrated part of I-GMS. We demonstrate I-GMS's capability to simulate general multi-rigid-body systems through examples that include a free-falling sphere, a robot manipulator, and a human body model with 36 degrees of freedom (dof). We present a simple example of interactive simulation for a 3-dof robot manipulator.

An interactive generalized motion simulator (GMS) in an object-oriented framework

In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. We demonstrate the utility of this approach for a variety of scenarios including pursuit-evasion on terrains, in multi-level buildings, and in crowds.

Nancy M. Amato

Papers

Associative Parallel Containers in STAPL

A Motion Planning Approach to Folding: From Paper Craft to Protein Structure Prediction

Hybrid dynamic simulation of rigid-body contact with Coulomb friction

An interactive generalized motion simulator (GMS) in an object-oriented framework

Toward simulating realistic pursuit-evasion using a roadmap-based approach