Message-driven dynamics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 251-260).by Richard Anton Lethin.Ph.D

On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks

We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data diffusion in ad-hoc networks, Ad Hoc Netw. (2011

A simple asynchronous replica-exchange implementation

We discuss the possibility of implementing asynchronous replica-exchange (or parallel tempering) molecular dynamics. In our scheme, the exchange attempts are driven by asynchronous messages sent by one of the computing nodes, so that different replicas are allowed to perform a different number of time-steps between subsequent attempts. The implementation is simple and based on the message-passing interface (MPI). We illustrate the advantages of our scheme with respect to the standard synchronous algorithm and we benchmark it for a model Lennard-Jones liquid on an IBM-LS21 blade center cluster.Comment: Preprint of Proceeding for CSFI 200

Distributed voltage control in electrical power systems

Voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the combined transmission and generation system. Discrete event controllers such as load tap changing transformers (LTCs), electronically controlled HVDC lines and switched capacitor banks can locally maintain the voltage but following a major disturbance that causes a strong decrease in the voltages, there are some interaction between LTCs action and up to now there has been relatively little attention paid to coordination between important components in voltage stability using message exchange between them and applying distributed control and taking discrete events into account. So, this study aims at voltage stability enhancement by using coordinated control of the discrete event controllers by using message exchange between the different local control agents. Various approaches for coordinating local controllers (e.g. distributed model predictive controllers) will be investigated. The influence of the discrete event driven local voltage controllers on remote locations of the network has to be investigated in a hybrid systems model framework

Role of Activity in Human Dynamics

The human society is a very complex system; still, there are several non-trivial, general features. One type of them is the presence of power-law distributed quantities in temporal statistics. In this Letter, we focus on the origin of power-laws in rating of movies. We present a systematic empirical exploration of the time between two consecutive ratings of movies (the interevent time). At an aggregate level, we find a monotonous relation between the activity of individuals and the power-law exponent of the interevent-time distribution. At an individual level, we observe a heavy-tailed distribution for each user, as well as a negative correlation between the activity and the width of the distribution. We support these findings by a similar data set from mobile phone text-message communication. Our results demonstrate a significant role of the activity of individuals on the society-level patterns of human behavior. We believe this is a common character in the interest-driven human dynamics, corresponding to (but different from) the universality classes of task-driven dynamics.Comment: 5 pages, 6 figures. Accepted by EP

Principles for problem aggregation and assignment in medium scale multiprocessors

One of the most important issues in parallel processing is the mapping of workload to processors. This paper considers a large class of problems having a high degree of potential fine grained parallelism, and execution requirements that are either not predictable, or are too costly to predict. The main issues in mapping such a problem onto medium scale multiprocessors are those of aggregation and assignment. We study a method of parameterized aggregation that makes few assumptions about the workload. The mapping of aggregate units of work onto processors is uniform, and exploits locality of workload intensity to balance the unknown workload. In general, a finer aggregate granularity leads to a better balance at the price of increased communication/synchronization costs; the aggregation parameters can be adjusted to find a reasonable granularity. The effectiveness of this scheme is demonstrated on three model problems: an adaptive one-dimensional fluid dynamics problem with message passing, a sparse triangular linear system solver on both a shared memory and a message-passing machine, and a two-dimensional time-driven battlefield simulation employing message passing. Using the model problems, the tradeoffs are studied between balanced workload and the communication/synchronization costs. Finally, an analytical model is used to explain why the method balances workload and minimizes the variance in system behavior

HPC with Python: An MPI-parallel implementation of the Lattice Boltzmann Method

The Lattice Boltzmann Method is well suited for high performance computational fluid dynamics. We show by means of a common two-dimensional test case, the lid-driven cavity problem, that excellent parallel scaling can be achieved in an implementation based on pure Python, using the numpy library and the Message Passing Interface. We highlight opportunities and pitfalls for the implementation of parallel high-performance codes in the high-level language Python

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Graph neural networks (GNNs) have demonstrated significant promise in modelling relational data and have been widely applied in various fields of interest. The key mechanism behind GNNs is the so-called message passing where information is being iteratively aggregated to central nodes from their neighbourhood. Such a scheme has been found to be intrinsically linked to a physical process known as heat diffusion, where the propagation of GNNs naturally corresponds to the evolution of heat density. Analogizing the process of message passing to the heat dynamics allows to fundamentally understand the power and pitfalls of GNNs and consequently informs better model design. Recently, there emerges a plethora of works that proposes GNNs inspired from the continuous dynamics formulation, in an attempt to mitigate the known limitations of GNNs, such as oversmoothing and oversquashing. In this survey, we provide the first systematic and comprehensive review of studies that leverage the continuous perspective of GNNs. To this end, we introduce foundational ingredients for adapting continuous dynamics to GNNs, along with a general framework for the design of graph neural dynamics. We then review and categorize existing works based on their driven mechanisms and underlying dynamics. We also summarize how the limitations of classic GNNs can be addressed under the continuous framework. We conclude by identifying multiple open research directions