Accuracy Study of a Free Particle Using Quantum Trajectory Method on Message Passing Architecture

Bhom\u27s hydrodynamic formulation (or quantum fluid dynamics) is an attractive approach since, it connects classical and quantum mechanical theories of matter through Hamilton-Jacobi (HJ) theory, and quantum potential. Lopreore and Wyatt derived and implemented one-dimensional quantum trajectory method (QTM), a new wave-packet approach, for solving hydrodynamic equations of motion on serial computing environment. Brook et al. parallelized the QTM on shared memory computing environment using a partially implicit method, and conducted accuracy study of a free particle. These studies exhibited a strange behavior of the relative error for the probability density referred to as the transient effect. In the present work, numerical experiments of Brook et al. were repeated with a view to identify the physical origin of the transient effect and its resolution. The present work used the QTM implemented on a distributed memory computing environment using MPI. The simulation is guided by an explicit scheme

AUTOMATIC BINDING OF VIRTUAL INTERNET PROTOCOL ADDRESS TO THE APPROPRIATE INTERFACE

Techniques are described herein for providing an innovative configuration model that requires a user to configure Virtual Internet Protocol (VIP) addresses in just one place, thus avoiding the possibilities of configuration error and also making the network infrastructure easy to deploy. For example, to configure four VIP addresses in a three-node cluster, state-of-the-art configuration models would require the user to touch/configure twelve values spanning across three appliances, which is cumbersome and error prone. By contrast, the configuration model described herein has just one touch point in the whole cluster

Grid-enabled treatment planning for proton therapy using monte carlo simulations

Grid computing is an emerging technology that enables computational tasks to be accomplished in a collaborative approach by using a distributed network of computers. The grid approach is especially important for computationally intensive problems that are not tractable with a single computer or even with a small cluster of computers, e.g., radiation transport calculations for cancer therapy. The objective of this work was to extend a Monte Carlo (MC) transport code used for proton radiotherapy to utilize grid computing techniques and demonstrate its promise in reducing runtime from days to minutes. As proof of concept we created the Medical Grid between Texas Tech University and Rice University. Preliminary computational experiments were carried out in the GEANT4 simulation environment for transport of 25 × 106 200 MeVprotons in a prostate cancer treatment plan. The simulation speedup was approximately linear; deviations were attributed to the spectrum of parallel runtimes and communication overhead due to Medical Grid computing. The results indicate that ∼3 × 105 to 5 ×105 proton events with processor core would result in 65 to 83% efficiency. Extrapolation of our results indicates that about 103 processor cores of the class used here would reduce the MC simulation runtime from 18.3 days to ∼1 h

POROUS PLASTIC MATRIX TABLETS OF LEVETIRACETAM FOR ZERO-ORDER CONTROLLED RELEASE: DEVELOPMENT AND FORMULATION OPTIMIZATION

Objective: The prior objective of the current research work was to develop once-daily levetiracetam extended/controlled-release tablets having zero-order release kinetics with the plastic matrix as the release retarding element. For a high water-soluble drug, the formulation of a dosage form so as to have an extended drug release has always been a difficult task. Methods: In the current work, levetiracetam which is a highly soluble drug was taken as the model drug for which extended-release matrix tablets were developed using varied plastic polymers like Polyvinyl acetate (PVAc), Polyvinyl chloride (PVC), Eudragit RSPO and Eudragit RLPO. PVP was considered as a pore-forming agent and PEG 6000 was taken as a water regulating agent. The porous plastic matrix tablets were prepared by embedding the drug in solvent-activated polymer dispersion followed by drying, sieving, mixing with other excipients and finally compressed. Including physical characterization studies and drug release studies, the tablets were subjected to SEM studies before and after the dissolution studies to analyze the effect of the pore former. Results: Pre-compression mixtures exhibited good packageability of 81-92% and hence the compressed tablets were strong enough with good tensile strength in the range of 0.78–0.90 N/mm2. Drug release study results showed that the drug release was controlled for a period of 12–24h. PVAc had shown better controlled-release among all the plastic polymers taken. PEG 6000 in combination with PVP produced the desired zero-order drug release. Conclusion: The levetiracetam porous plastic matrix tablets were developed with zero-order drug release that was effectively controlled for 24hr

A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses

Summary Statement of the problem Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. Methods Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. Results and conclusions Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation’s obesity epidemic

Parallel Adaptive Quantum Trajectory Method for Wavepacket Simulations

Time-dependent wavepackets are widely used to model various phenomena in physics. One approach in simulating the wavepacket dynamics is the quantum trajectory method (QTM). Based on the hydrodynamic formulation of quantum mechanics, the QTM represents the wavepacket by an unstructured set of pseudoparticles whose trajectories are coupled by the quantum potential. The governing equations for the pseudoparticle trajectories are solved using a computationally intensive moving weighted least squares (MWLS) algorithm, and the trajectories can be computed in parallel. This paper contributes a strategy for improving the performance of wavepacket simulations using the QTM. Specifically, adaptivity is incorporated into the MWLS algorithm, and loop scheduling techniques are employed to dynamically load balance the parallel computation of the trajectories. The adaptive MWLS algorithm reduces the amount of computations without sacrificing accuracy, while adaptive loop scheduling addresses the load imbalance introduced by the algorithm and the runtime system. Results of experiments on a Linux cluster are presented to confirm that the adaptive MWLS reduces the trajectory computation time by up to 24%, and adaptive loop scheduling achieves parallel efficiencies of up to 85% when simulating a free particle

Parallel Adaptive Quantum Trajectory Method for Wavepacket Simulations

Time-dependent wavepackets are widely used to model various phenomena in physics. One approach in simulating the wavepacket dynamics is the quantum trajectory method (QTM). Based on the hydrodynamic formulation of quantum mechanics, the QTM represents the wavepacket by an unstructured set of pseudoparticles whose trajectories are coupled by the quantum potential. The governing equations for the pseudoparticle trajectories are solved using a computationally-intensive moving weighted least squares (MWLS) algorithm, and the trajectories can be computed in parallel. This paper contributes a strategy for improving the performance of wavepacket simulations using the QTM. Specifically, adaptivity is incorporated into the MWLS algorithm, and loop scheduling techniques are employed to dynamically load balance the parallel computation of the trajectories. The adaptive MWLS algorithm reduces the amount of computations without sacrificing accuracy, while adaptive loop scheduling addresses the load imbalance introduced by the algorithm and the runtime system. Results of experiments on a Linux cluster are presented to confirm that the adaptive MWLS reduces the trajectory computation time by up to 24%, and adaptive loop scheduling achieves parallel efficiencies of up to 85% when simulating a free particle

Parallel Adaptive Quantum Trajectory Method for Wavepacket Simulations

Time-dependent wavepackets are widely used to model various phenomena in physics. One approach in simulating the wavepacket dynamics is the quantum trajectory method (QTM). Based on the hydrodynamic formulation of quantum mechanics, the QTM represents the wavepacket by an unstructured set of pseudoparticles whose trajectories are coupled by the quantum potential. The governing equations for the pseudoparticle trajectories are solved using a computationally-intensive moving weighted least squares (MWLS) algorithm, and the trajectories can be computed in parallel. This paper contributes a strategy for improving the performance of wavepacket simulations using the QTM. Specifically, adaptivity is incorporated into the MWLS algorithm, and loop scheduling techniques are employed to dynamically load balance the parallel computation of the trajectories. The adaptive MWLS algorithm reduces the amount of computations without sacrificing accuracy, while adaptive loop scheduling addresses the load imbalance introduced by the algorithm and the runtime system. Results of experiments on a Linux cluster are presented to confirm that the adaptive MWLS reduces the trajectory computation time by up to 24%, and adaptive loop scheduling achieves parallel efficiencies of up to 85% when simulating a free particle

Message-Passing Parallel Adaptive Quantum Trajectory Method

Time-dependent wavepackets are widely used to model various phenomena in physics. One approach in simulating the wavepacket dynamics is the quantum trajectory method (QTM). Based on the hydrodynamic formulation of quantum mechanics, the QTM represents the wavepacket by an unstructured set of pseudoparticles whose trajectories are coupled by the quantum potential. The governing equations for the pseudoparticle trajectories are solved using a computationally-intensive moving weighted least squares (MWLS) algorithm, and the trajectories can be computed in parallel. This work contributes a strategy for improving the performance of wavepacket simulations using the QTM on message-passing systems. Specifically, adaptivity is incorporated into the MWLS algorithm, and loop scheduling is employed to dynamically load balance the parallel computation of the trajectories. The adaptive MWLS algorithm reduces the amount of computations without sacrificing accuracy, while adaptive loop scheduling addresses the load imbalance introduced by the algorithm and the runtime system. Results of experiments on a Linux cluster are presented to confirm that the adaptive MWLS reduces the trajectory computation time by up to 24%, and adaptive loop scheduling achieves parallel effieciencies of up to 90% when simulating a free particle

Message-Passing Parallel Adaptive Quantum Trajectory Method

Time-dependent wavepackets are widely used to model various phenomena in physics. One approach in simulating the wavepacket dynamics is the quantum trajectory method (QTM). Based on the hydrodynamic formulation of quantum mechanics, the QTM represents the wavepacket by an unstructured set of pseudoparticles whose trajectories are coupled by the quantum potential. The governing equations for the pseudoparticle trajectories are solved using a computationally-intensive moving weighted least squares (MWLS) algorithm, and the trajectories can be computed in parallel. This work contributes a strategy for improving the performance of wavepacket simulations using the QTM on message-passing systems. Specifically, adaptivity is incorporated into the MWLS algorithm, and loop scheduling is employed to dynamically load balance the parallel computation of the trajectories. The adaptive MWLS algorithm reduces the amount of computations without sacrificing accuracy, while adaptive loop scheduling addresses the load imbalance introduced by the algorithm and the runtime system. Results of experiments on a Linux cluster are presented to confirm that the adaptive MWLS reduces the trajectory computation time by up to 24%, and adaptive loop scheduling achieves parallel effieciencies of up to 90% when simulating a free particle