Time Slice (time + slice)

Distribution by Scientific Domains


Selected Abstracts


Reparallelization techniques for migrating OpenMP codes in computational grids

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 3 2009
Michael Klemm
Typical computational grid users target only a single cluster and have to estimate the runtime of their jobs. Job schedulers prefer short-running jobs to maintain a high system utilization. If the user underestimates the runtime, premature termination causes computation loss; overestimation is penalized by long queue times. As a solution, we present an automatic reparallelization and migration of OpenMP applications. A reparallelization is dynamically computed for an OpenMP work distribution when the number of CPUs changes. The application can be migrated between clusters when an allocated time slice is exceeded. Migration is based on a coordinated, heterogeneous checkpointing algorithm. Both reparallelization and migration enable the user to freely use computing time at more than a single point of the grid. Our demo applications successfully adapt to the changed CPU setting and smoothly migrate between, for example, clusters in Erlangen, Germany, and Amsterdam, the Netherlands, that use different kinds and numbers of processors. Benchmarks show that reparallelization and migration impose average overheads of about 4 and 2%, respectively. Copyright © 2008 John Wiley & Sons, Ltd. [source]


An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2004
Boris Brandherm
We extend Darwiche's differential approach to inference in Bayesian networks (BNs) to handle specific problems that arise in the context of dynamic Bayesian networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial. We then show how procedures for the computation of corresponding polynomials for DBNs can be derived. These procedures permit not only an exact roll-up of old time slices but also a constant-space evaluation of DBNs. The method is applicable to both forward and backward propagation, and it does not presuppose that each time slice of the DBN has the same structure. It is compatible with approximative methods for roll-up and evaluation of DBNs. Finally, we discuss further ways of improving efficiency, referring as an example to a mobile system in which the computation is distributed over a normal workstation and a resource-limited mobile device. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 727,748, 2004. [source]


Estimating time dependent O-D trip tables during peak periods

JOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2000
Srinivas S. Pulugurtha
Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [source]


Analysis of brain activity immediately before conscious teeth clenching using magnetoencephalographic method

JOURNAL OF ORAL REHABILITATION, Issue 7 2007
T. IIDA
summary, The reasons for unconscious teeth clenching have not been clarified. The long-term goal of our project was the elucidation of processing in the brain immediately before unconscious teeth clenching, in order to clarify its significance in humans. The objective of the present study was to establish a magnetoencephalographic (MEG) method of measuring brain activity immediately before clenching, and to clarify the time-course of brain activity immediately before conscious clenching. We measured the MEG signal in six subjects before, during and after clenching in a protocol that restricted head movement <5 mm. We derived tomographic estimates of brain activity for each time slice of data, as well as time courses for regional brain activations. Analysis of the tomographic images and time courses yielded statistical maps of activity in the motor, pre-motor and somatosensory cortices immediately before clenching in all subjects. Activations were found bilaterally, but with a strong unilateral bias in most subjects. Our results demonstrate that the MEG procedures, we have introduced are capable of measuring brain activity immediately before clenching, and indicate that analysis should begin from at least 200 ms before electromyogram onset. [source]


A multimodel assessment of future climatological droughts in the United Kingdom,

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 14 2009
Jean-Philippe Vidal
Abstract This paper presents a detailed assessment of future rainfall drought patterns over the United Kingdom. Previously developed bias-corrected high-resolution gridded precipitation time series are aggregated to the scale relevant for water resources management, in order to provide 21st-century time series for 183 hydrologic areas, as computed by six General Circulation Models (GCMs) under two emissions scenarios. The control run data are used as a ,learning time series' to compute the Standardized Precipitation Index (SPI) at four different time scales. SPI values for three 30-year future time slices are computed with respect to these learning time series in order to assess the changes in drought frequency. Multimodel results under the A2 scenario show a dramatic increase in the frequency of short-term extreme drought class for most of the country. A decrease of long-term droughts is expected in Scotland, due to the projected increase in winter precipitation. The analysis for two catchment case studies also showed that changes under the B2 scenario are generally consistent with those of the A2 scenario, with a reduced magnitude in changes. The overall increase with time in the spread of individual GCM results demonstrates the utility of multimodel statistics when assessing the uncertainty in future drought indices to be used in long-term water resources planning. Copyright © 2009 Royal Meteorological Society [source]


An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2004
Boris Brandherm
We extend Darwiche's differential approach to inference in Bayesian networks (BNs) to handle specific problems that arise in the context of dynamic Bayesian networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial. We then show how procedures for the computation of corresponding polynomials for DBNs can be derived. These procedures permit not only an exact roll-up of old time slices but also a constant-space evaluation of DBNs. The method is applicable to both forward and backward propagation, and it does not presuppose that each time slice of the DBN has the same structure. It is compatible with approximative methods for roll-up and evaluation of DBNs. Finally, we discuss further ways of improving efficiency, referring as an example to a mobile system in which the computation is distributed over a normal workstation and a resource-limited mobile device. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 727,748, 2004. [source]


Estimating time dependent O-D trip tables during peak periods

JOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2000
Srinivas S. Pulugurtha
Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [source]