A comparison of CPU and GPU implementations for solving the Convection Diffusion equation using the local Modified SOR method

In this paper we study a parallel form of the SOR method for the numerical solution of the Convection Diffusion equation suitable for GPUs using CUDA. To exploit the parallelism offered by GPUs we consider the fine grain parallelism model. This is achieved by considering the local relaxation version of SOR. More specifically, we use SOR with red-black ordering using two sets of parameters _ω1ij${\omega }_{1\mathit{ij}}$ and _ω2ij${\omega }_{2\mathit{ij}}$ for the 5 point stencil. The parameter _ω1ij${\omega }_{1\mathit{ij}}$ is associated with each red (i + j   even) grid point (i,j)$(i\text{,}j)$, whereas the parameter _ω2ij${\omega }_{2\mathit{ij}}$ is associated with each black (i+j$(i+j$ odd) grid point (i,j)$(i\text{,}j)$. The use of a parameter for each grid point avoids the global communication required in the adaptive determination of the best value of ω$\omega$ and also increases the convergence rate of the SOR method (Varga, 1962) [38] and (Young, 1971) [41]. We present our strategy and the results of our effort to exploit the computational capabilities of GPUs under the CUDA environment. Additionally, two parallel CPU programs utilizing manual SSE2 (Streaming SIMD Extensions 2) and AVX (Advanced Vector Extensions) vectorization were developed as performance references. The optimizations applied on the GPU version were also considered for the CPU version. Significant performance improvement was achieved with all three developed GPU kernels differentiated by the degree of recomputations thus affecting the flops per element access ratio.     

The incomplete depletion approximation in semiconductor heterojunctions

Under equilibrium the region near the heterojunction interface is not completely depleted of carriers. As the forward bias is increased, carriers are transported out of the region and the charge density is increased. The well-known depletion approximation is a special case of the more general incomplete depletion approximation.     

The copula echo state network

Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications; as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency.     

Adaptive user modelling in car racing games using behavioural and physiological data

Personalised content adaptation has great potential to increase user engagement in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adaptive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to identify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user’s physiological data and game-related actions, and cluster them into low level primitives. These primitives are transformed and evaluated into higher level abstractions such as experience, exploration and attention. These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feedback from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions personalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation.     

Local learning regularization networks for localized regression

Local learning algorithms use a neighborhood of training data close to a given testing query point in order to learn the local parameters and create on-the-fly a local model specifically designed for this query point. The local approach delivers breakthrough performance in many application domains. This paper considers local learning versions of regularization networks (RN) and investigates several options for improving their online prediction performance, both in accuracy and speed. First, we exploit the interplay between locally optimized and globally optimized hyper-parameters (regularization parameter and kernel width) each new predictor needs to optimize online. There is a substantial reduction of the operation cost in the case we use two globally optimized hyper-parameters that are common to all local models. We also demonstrate that this global optimization of the two hyper-parameters produces more accurate models than the other cases that locally optimize online either the regularization parameter, or the kernel width, or both. Then by comparing Eigenvalue decomposition (EVD) with Cholesky decomposition specifically for the local learning training and testing phases, we also reveal that the Cholesky-based implementations are faster that their EVD counterparts for all the training cases. While EVD is suitable for validating cost-effectively several regularization parameters, Cholesky should be preferred when validating several neighborhood sizes (the number of k-nearest neighbors) as well as when the local network operates online. Then, we exploit parallelism in a multi-core system for these local computations demonstrating that the execution times are further reduced. Finally, although the use of pre-computed stored local models instead of the online learning local models is even faster, this option deteriorates the performance. Apparently, there is a substantial gain in waiting for a testing point to arrive before building a local model, and hence the online local learning RNs are more accurate than their pre-computed stored local models. To support all these findings, we also present extensive experimental results and comparisons on several benchmark datasets.

     

Decentralized Swarm Coordination: A Combined Coverage/Connectivity Approach

Decentralized motion coordination for coverage optimization purposes in mobile sensor networks is the scope of this paper. Coordination is performed based on spatial Voronoi tessellation, while taking into consideration the limited sensing capabilities of the agents. Each node performs an independent optimization in order to increase network??s area coverage via its motion, while it attains information from its current and future Delaunay neighbors. A decentralized algorithm is proposed in order to achieve optimal network??s coverage, based on local information. Connectivity issues are analyzed in detail, while a lower bound on the communication radius of the nodes is derived, in order to attain sufficient information for performing the corresponding optimization. An agent moves inside its region of responsibility in a way that the total area surveyed by the network is a monotonically increasing function of time. The online control action makes the network adaptive to possible changes in the environment.     

Revisiting active perception

Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.     

Local Power of Fixed‐T Panel Unit Root Tests With Serially Correlated Errors and Incidental Trends

The asymptotic local power properties of various fixed T panel unit root tests with serially correlated errors and incidental trends are studied. Asymptotic (over N) local power functions are analytically derived, and through them, the effects of general forms of serial correlation are examined. We find that a test based on an instrumental variables (IV) estimator dominates the tests based on the within‐groups (WG) estimator. These functions also show that in the presence of incidental trends, an instrumental variables test based on the first differences of the model has non‐trivial local power in an N^?1/2 neighbourhood of unity. Furthermore, for a test based on the within‐groups estimator, although it is found that it has trivial power in the presence of incidental trends, this ceases to be the case if there is serial correlation as well.