On bulk-synchronous distributed-memory parallel processing of relational-database transactions

This paper describes two parallel algorithms for the eÆcient processing of relational database transactions and presents a performance analysis of them. These algorithms are built upon the bulk-synchronous parallel model of computation. The well-de ned structure of this model enabled us to evaluate their performance by using an implementation independent and yet em- pirical approach which includes the e ects of synchronization, communication and computation. The analysis reveals that the algorithm which borrows ideas from optimistic parallel discrete event simulation achieves better performance than the classical approach for synchronizing con- current transactions on a distributed memory system.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

On bulk-synchronous distributed-memory parallel processing of relational-database transactions

This paper describes two parallel algorithms for the eÆcient processing of relational database transactions and presents a performance analysis of them. These algorithms are built upon the bulk-synchronous parallel model of computation. The well-de ned structure of this model enabled us to evaluate their performance by using an implementation independent and yet em- pirical approach which includes the e ects of synchronization, communication and computation. The analysis reveals that the algorithm which borrows ideas from optimistic parallel discrete event simulation achieves better performance than the classical approach for synchronizing con- current transactions on a distributed memory system.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

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

Event-Driven Molecular Dynamics in Parallel

Although event-driven algorithms have been shown to be far more efficient than time-driven methods such as conventional molecular dynamics, they have not become as popular. The main obstacle seems to be the difficulty of parallelizing event-driven molecular dynamics. Several basic ideas have been discussed in recent years, but to our knowledge no complete implementation has been published yet. In this paper we present a parallel event-driven algorithm including dynamic load-balancing, which can be easily implemented on any computer architecture. To simplify matters our explanations refer to a basic multi-particle system of hard spheres, but can be extended easily to a wide variety of possible models.Comment: 10 pages, 9 figure

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward