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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper presents an efficient method for finding matches to a given regular expression in given text using FPGAs. To match a regular expression of length n, a serial machine requires 0(2^n) memory and takes 0(1) time per text character. The proposed approach reqiures only 0(n^2) space and still process a text character in 0(1) time (one clock cycle).The improvement is due to the Nondetermineistic Finite Automaton (NFA) used to perform the matching. As far as the authors are aware, this is the first prctical use of a nondeterministic state machine on programmable logic. Furthermore, the paper presents a simple, fast algorithm that quickly constructs the NFA for the given regular expression. Fast NFA construction is crucial because the NFA structure depends on the regular expression, which is known only at runtime. Implementations of the algorithm for conventional FPGAs and the self-reconfigurable Gate Array (SRGA) are described. To evaluate performance, the NFA logic was mapped onto the Virtex XCV100 FPGA and the SRGA. Also, the performance of GNU grep for matching regular expressions was evaluated on an 800 MHz Pentium III machine. The proposed approach was faster than best case grep performance in most cases. It was orders of magnitude faster than worst case grep performance. Logic for the largest NFA considered fit in less than a 1000 CLBs while DFA storage for grep in the worst case consumed a few hundred megabytes.

Fast Regular Expression Matching Using FPGAs

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).

GraphSAINT: Graph Sampling Based Inductive Learning Method

We study the problem of scheduling packet transmissions for data gathering in wireless sensor networks. The focus is to explore the energy-latency tradeoffs in wireless communication using techniques such as modulation scaling. The data aggregation tree - a multiple-source single-sink communication paradigm - is employed for abstracting the packet flow. We consider a real-time scenario where the data gathering must be performed within a specified latency constraint. We present algorithms to minimize the overall energy dissipation of the sensor nodes in the aggregation tree subject to the latency constraint. For the off-line problem, we propose (a) a numerical algorithm for the optimal solution, and (h) a pseudo-polynomial time approximation algorithm based on dynamic programming. We also discuss techniques for handling interference among the sensor nodes. Simulations have been conducted for both long-range communication and short-range communication. The simulation results show that compared with the classic shutdown technique, between 20% to 90% energy savings can be achieved by our techniques, under different settings of several key system parameters. We also develop an on-line distributed protocol that relies only on the local information available at each sensor node within the aggregation tree. Simulation results show that between 15% to 90% energy conservation can be achieved by the on-line protocol. The adaptability of the protocol with respect to variations in the packet size and latency constraint is also demonstrated through several run-time scenarios.

Energy-latency tradeoffs for data gathering in wireless sensor networks

The issues and problems posed by heterogeneous computing are discussed. They include design of algorithms for applications, partitioning and mapping of application tasks, interconnection requirements, and the design of programming environments. The use of heterogeneous computing in image understanding is reviewed. An example vision task is presented, and the different types of parallelism used in the example are identified. >

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Heterogeneous computing: challenges and opportunities

This article focuses on a scalable software platform for the Smart Grid cyber-physical system using cloud technologies Dynamic Demand Response (D2R) is a challenge-application to perform intelligent demand-side management and relieve peak load in Smart Power Grids The platform offers an adaptive information integration pipeline for ingesting dynamic data; a secure repository for researchers to share knowledge; scalable machine-learning models trained over massive datasets for agile demand forecasting; and a portal for visualizing consumption patterns, and validated at the University of Southern California's campus microgrid The article examines the role of clouds and their tradeoffs for use in the Smart Grid Cyber-Physical Sagileystem

Viktor K. Prasanna

Papers

Fast Regular Expression Matching Using FPGAs

GraphSAINT: Graph Sampling Based Inductive Learning Method

Energy-latency tradeoffs for data gathering in wireless sensor networks

Heterogeneous computing: challenges and opportunities

Cloud-Based Software Platform for Big Data Analytics in Smart Grids