26,190 research outputs found
Message Passing for Collective Graphical Models
National Research Foundation (NRF) Singapore under Corp Lab @ University schem
Region graph partition function expansion and approximate free energy landscapes: Theory and some numerical results
Graphical models for finite-dimensional spin glasses and real-world
combinatorial optimization and satisfaction problems usually have an abundant
number of short loops. The cluster variation method and its extension, the
region graph method, are theoretical approaches for treating the complicated
short-loop-induced local correlations. For graphical models represented by
non-redundant or redundant region graphs, approximate free energy landscapes
are constructed in this paper through the mathematical framework of region
graph partition function expansion. Several free energy functionals are
obtained, each of which use a set of probability distribution functions or
functionals as order parameters. These probability distribution
function/functionals are required to satisfy the region graph
belief-propagation equation or the region graph survey-propagation equation to
ensure vanishing correction contributions of region subgraphs with dangling
edges. As a simple application of the general theory, we perform region graph
belief-propagation simulations on the square-lattice ferromagnetic Ising model
and the Edwards-Anderson model. Considerable improvements over the conventional
Bethe-Peierls approximation are achieved. Collective domains of different sizes
in the disordered and frustrated square lattice are identified by the
message-passing procedure. Such collective domains and the frustrations among
them are responsible for the low-temperature glass-like dynamical behaviors of
the system.Comment: 30 pages, 11 figures. More discussion on redundant region graphs. To
be published by Journal of Statistical Physic
Towards Automatic and Adaptive Optimizations of MPI Collective Operations
Message passing is one of the most commonly used paradigms of parallel programming. Message Passing Interface, MPI, is a standard used in scientific and high-performance computing. Collective operations are a subset of MPI standard that deals with processes synchronization, data exchange and computation among a group of processes. The collective operations are commonly used and can be application performance bottleneck. The performance of collective operations depends on many factors, some of which are the input parameters (e.g., communicator and message size); system characteristics (e.g., interconnect type); the application computation and communication pattern; and internal algorithm parameters (e.g., internal segment size). We refer to an algorithm and its internal parameters as a method.
The goal of this dissertation is a performance improvement of MPI collective operations and applications that use them. In our framework, during a collective call, a system-specific decision function is invoked to select the most appropriate method for the particular collective instance. This dissertation focuses on automatic techniques for system-specific decision function generation. Our approach takes the following steps: first, we collect method performance information on the system of interest; second, we analyze this information using parallel communication models, graphical encoding methods, and decision trees; third, based on the previous step, we automatically generate the system-specific decision function to be used at run-time. In situation when a detailed performance measurement is not feasible, method performance models can be used to supplement the measured method performance information.
We build and evaluate parallel communication models of 35 different collective algorithms. These models are built on top of the three commonly used point-to-point communication models, Hockney, LogGP, and PLogP.We use the method performance information on a system to build quadtrees and C4.5 decision trees of variable sizes and accuracies. The collective method selection functions are then generated automatically from these trees. Our experiments show that quadtrees of three or four levels are often enough to approximate experimentally optimal decision with a small mean performance penalty (less than 10%). The C4.5 decision trees are even more accurate (with mean performance penalty of less than 5%). The size and accuracy of C4.5 decision trees can be further improved with use of appropriate composite attributes (such as “total message size”, or “even communicator size”.) Finally, we apply these techniques to tune the collective operations on the Grig cluster at the University of Tennessee and to improve an application performance on the Cray XT4 system at Oak Ridge National Laboratory. The tuned collective is able to achieve more than 40% mean performance improvement over the native broadcast implementation. Using the platform-specific reduce on Cray XT4 lead to 10% improvement in the overall application performance. Our results show that the methods we explored are both applicable and effective for the system-specific optimizations of collective operations and are a right step toward automatically tunable, adaptive, MPI collectives
Bayesian decision support for complex systems with many distributed experts
Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems
Gaussian Approximation of Collective Graphical Models
The Collective Graphical Model (CGM) models a population of independent and
identically distributed individuals when only collective statistics (i.e.,
counts of individuals) are observed. Exact inference in CGMs is intractable,
and previous work has explored Markov Chain Monte Carlo (MCMC) and MAP
approximations for learning and inference. This paper studies Gaussian
approximations to the CGM. As the population grows large, we show that the CGM
distribution converges to a multivariate Gaussian distribution (GCGM) that
maintains the conditional independence properties of the original CGM. If the
observations are exact marginals of the CGM or marginals that are corrupted by
Gaussian noise, inference in the GCGM approximation can be computed efficiently
in closed form. If the observations follow a different noise model (e.g.,
Poisson), then expectation propagation provides efficient and accurate
approximate inference. The accuracy and speed of GCGM inference is compared to
the MCMC and MAP methods on a simulated bird migration problem. The GCGM
matches or exceeds the accuracy of the MAP method while being significantly
faster.Comment: Accepted by ICML 2014. 10 page version with appendi
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