In the big data era, the efficient processing of large volumes of data has became a standard requirement for both organizations and enterprises. Since single workstations cannot sustain such tremendous workloads, MapReduce was introduced with the aim of providing a robust, easy, and fault-tolerant parallelization framework for the execution of applications on large clusters. One of the most representative examples of such applications is the machine learning algorithms which dominate the broad research area of data mining. Simultaneously, the recent advances in hardware technology led to the introduction of high-performing alternative devices for secondary storage, known as Solid State Drives (SSDs). In this paper we examine the perfor-mance of several parallel data mining algorithms on MapReduce clusters equipped with such modern hardware. More specifically, we investigate standard dataset preprocessing methods including vectorization and dimensionality reduction, and two supervised classifiers, Naive Bayes and Linear Regression. We compare the execution times of these algorithms on an experimental cluster equipped with both standard magnetic disks and SSDs, by employing two different datasets and by applying several different cluster configurations. Our experiments demonstrate that the usage of SSDs can accelerate the execution of machine learning methods by a margin which depends on the cluster setup and the nature of the applied algorithms.

Efficient analysis of on-chip power delivery networks is one of the most challenging problems facing the electronic design automation industry today. The fast dc and transient simulation of power grids is necessary to determine the proper operation of the integrated circuits at the design phase, but is made very difficult by the sheer size of modern power grids, reaching quite a few million nodes in nanometer-scale integrated circuits. This paper presents two efficient and highly parallel preconditioning mechanisms for the analysis of large-scale power grids of near-2-D structure (with small via resistances) or 3-D structure (with large via resistances) by iterative solution methods. The proposed preconditioners approximate the matrices of practical power grids well enough to ensure fast convergence of the iterative method, while their application within the core of the method is based on a fast transform solver which makes use of a series of independent fast Fourier transforms. Apart from the near-optimal operation complexity, the main characteristics of a fast transform solver are the large degree of multilevel parallelism and low memory requirements, which enable harnessing the computational resources of massively parallel architectures like graphics processing units (GPUs). Experimental evaluation of the proposed methodology on a set of large-scale industrial benchmarks demonstrates nearly two orders of magnitude speedup and reduction in memory footprint over parallel implementations of state-of-the-art direct and iterative methods, when GPUs are utilized.

In smart cities residential homes are fully equipped with information networking and computing technologies and are connected to the power grid via intelligent meters. Connectivity of meters allows formation of groups of residents, which are physically close, and as a result individual consumptions can be aggregated into a shared consumption. In this paper an approach of unfolding shared consumption and making inferences about resident personal usage is presented. The proposed approach tackles the problem of unfolding as a multiobjective problem in which a set of residential profiles is fitted to the measured consumption. A solution to the multiobjective problem is sought by using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) that utilizes the Pareto optimality theory to identify an optimal solution. The approach is applied to a set electricity consumption signals for making inferences about the personal energy usage of residential participants in the shared consumption pattern.

Efficient analysis of massive on-chip power delivery networks is among the most challenging problems facing the EDA industry today. Due to Joule heating effect and the temperature dependence of resistivity, temperature is one of the most important factors that affect IR drop and must be taken into account in power grid analysis. However, the sheer size of modern power delivery networks (comprising several thousands or millions of nodes) usually forces designers to neglect thermal effects during IR drop analysis in order to simplify and accelerate simulation. As a result, the absence of accurate estimates of Joule heating effect on IR drop analysis introduces significant uncertainty in the evaluation of circuit functionality. This work presents a new approach for fast electrical-thermal co-simulation of large-scale power grids found in contemporary nanometer-scale ICs. A state-of-the-art iterative method is combined with an efficient and extremely parallel preconditioning mechanism, which enables harnessing the computational resources of massively parallel architectures, such as graphics processing units (GPUs). Experimental results demonstrate that the proposed method achieves a speedup of 66.1X for a 3.1M-node design over a state-of-the-art direct method and a speedup of 22.2X for a 20.9M-node design over a state-of-the-art iterative method when GPUs are utilized.

Efficient analysis of massive on-chip power delivery networks is among the most challenging problems facing the EDA industry today. In this paper, we present a new preconditioned iterative method for fast DC and transient simulation of large-scale power grids found in contemporary nanometer-scale ICs. The emphasis is placed on the preconditioner which reduces the number of iterations by a factor of 5X for a 2.6M-node industrial design and by 72.6X for a 6.2M-node synthetic benchmark, compared with incomplete factorization preconditioners. Moreover, owing to the preconditioner's special structure that allows utilizing a Fast Transform solver, the preconditioning system can be solved in a near-optimal number of operations, while it is extremely amenable to parallel computation on massively parallel architectures like graphics processing units (GPUs). Experimental results demonstrate that our method achieves a speed-up of 214.3X and 138.7X for a 2.6M-node industrial design, and a speed-up of 1610.5X and 438X for a 3.1M-node synthetic design, over state-of-the-art direct and iterative solvers respectively when GPUs are utilized. At the same time, its matrix-less formulation allows for reducing the memory footprint by up to 33% compared to the memory requirements of the best available iterative solver.

In the recent years, there is a growing interest in exploiting the advances of mobile and pervasive computing to museum environments. A mobile museum guide, named iMuse Mobile Tour is presented in this paper. The guide utilizes UHF RFID technology to provide context aware information services. It comprises predefined and self-defined tours as well as interactive games to stimulate learning. A group support service is introduced, which enables group visitors to exploit guidance services on their private mobile phones. The service provides access to information retrieved by museum's RFID enabled devices.

The simulation and modeling of complex physical systems often involves many components because (i) the physical system itself has components of differing natures, (ii) parallel computing strategies require many (somewhat independent) components, and (iii) existing simulation software applies only to simpler geometrical shapes and physical situations. We discuss how agent based networks are applied to such multi-component applications. The network agents are used to (a) control the execution of existing solvers on sub-components, (b) mediate between sub-components, and (c) coordinate the execution of the ensemble. This paper focuses on partial differential equation (PDE) models as an instance of the approach and describes the implementation of networks using the PELLPACK problem solving environment for PDEs and the Bond system for agent based computing. Copyright © 2000 John Wiley & Sons, Ltd.