Preparing sparse solvers for exascale computing.

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'

Location based services in wireless ad hoc networks

In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii

Optimising power flow in a volatile electrical grid using a message passing algorithm

Current methods of optimal power flow were not designed to handle increasing level of volatility in the electrical networks, this thesis suggests that a message passing-based approach could be useful for managing power distribution in electricity networks. This thesis shows the adaptability of message passing algorithms and demonstrates and validates its capabilities in addressing scenarios with inherent fluctuations, in minimising load shedding and generation costs, and in limiting voltages. Results are promising but more work is needed for this to be practical to real networks

Network Partitioning in Distributed Agent-Based Models

Agent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical. The real-time requirement necessitates the use of in-memory computing, as it is difficult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use parallel and distributed computing platforms, such as MPI-enabled CPU clusters. Consequently, the agent population must be partitioned across different CPUs in a cluster. Further, the typically high volume of interactions among agents can quickly become a significant bottleneck for real-time or large-scale simulations. The problem is exacerbated if the underlying ABM network is dynamic and the inter-process communication evolves over the course of the simulation. Therefore, it is critical to develop topology-aware partitioning mechanisms to support such large simulations. In this dissertation, we demonstrate that distributed agent-based model simulations benefit from the use of graph partitioning algorithms that involve a local, neighborhood-based perspective. Such methods do not rely on global accesses to the network and thus are more scalable. In addition, we propose two partitioning schemes that consider the bottom-up individual-centric nature of agent-based modeling. The First technique utilizes label-propagation community detection to partition the dynamic agent network of an ABM. We propose a latency-hiding, seamless integration of community detection in the dynamics of a distributed ABM. To achieve this integration, we exploit the similarity in the process flow patterns of a label-propagation community-detection algorithm and self-organizing ABMs. In the second partitioning scheme, we apply a combination of the Guided Local Search (GLS) and Fast Local Search (FLS) metaheuristics in the context of graph partitioning. The main driving principle of GLS is the dynamic modi?cation of the objective function to escape local optima. The algorithm augments the objective of a local search, thereby transforming the landscape structure and escaping a local optimum. FLS is a local search heuristic algorithm that is aimed at reducing the search space of the main search algorithm. It breaks down the space into sub-neighborhoods such that inactive sub-neighborhoods are removed from the search process. The combination of GLS and FLS allowed us to design a graph partitioning algorithm that is both scalable and sensitive to the inherent modularity of real-world networks

Throughput-driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

This is an Open Access article. © 2015 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.Graph partitioning is an important problem in computer science and is of NP-hard complexity. In practice it is usually solved using heuristics. In this article we introduce the use of graph partitioning to partition the workload of stream programs to optimise the throughput on heterogeneous distributed platforms. Existing graph partitioning heuristics are not adequate for this problem domain. In this article we present two new heuristics to capture the problem space of graph partitioning for stream programs to optimise throughput. The first algorithm is an adaptation of the well-known Kernighan-Lin algorithm, called KL-Adapted (KLA), which is relatively slow. As a second algorithm we have developed the Congestion Avoidance (CA) partitioning algorithm, which performs reconfiguration moves optimised to our problem type. We compare both KLA and CA with the generic meta-heuristic Simulated Annealing (SA). All three methods achieve similar throughput results for most cases, but with significant differences in calculation time. For small graphs KLA is faster than SA, but KLA is slower for larger graphs. CA on the other hand is always orders of magnitudes faster than both KLA and SA, even for large graphs. This makes CA potentially useful for re-partitioning of systems during runtime.Peer reviewedFinal Published versio

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

Towards Adaptive and Grid-Transparent Adjoint-Based Design Optimization Frameworks

With the growing environmental consciousness, the global perspective in energy production is shifting towards renewable resources. As recently reported by the Office of Energy Efficiency & Renewable Energy at the U.S. Department of Energy, wind-generated electricity is the least expensive form of renewable power and is becoming one of the cheapest forms of electricity from any source. The aeromechanical design of wind turbines is a complex and multidisciplinary task which necessitates a high-fidelity flow solver as well as efficient design optimization tools. With the advances in computer technologies, Computational Fluid Dynamics (CFD) has established its role as a high-fidelity tool for aerodynamic design.In this dissertation, a grid-transparent unstructured two- and three-dimensional compressible Reynolds-Averaged Navier-Stokes (RANS) solver, named UNPAC, is developed. This solver is enhanced with an algebraic transition model that has proven to offer accurate flow separation and reattachment predictions for the transitional flows. For the unsteady time-periodic flows, a harmonic balance (HB) method is incorporated that couples the sub-time level solutions over a single period via a pseudo-spectral operator. Convergence to the steady-state solution is accelerated using a novel reduced-order-model (ROM) approach that can offer significant reductions in the number of iterations as well as CPU times for the explicit solver. The unstructured grid is adapted in both steady and HB cases using an r-adaptive mesh redistribution (AMR) technique that can efficiently cluster nodes around regions of large flow gradients.Additionally, a novel toolbox for sensitivity analysis based on the discrete adjoint method is developed in this work. The Fast automatic Differentiation using Operator-overloading Technique (FDOT) toolbox uses an iterative process to evaluate the sensitivities of the cost function with respect to the entire design space and requires only minimal modifications to the available solver. The FDOT toolbox is coupled with the UNPAC solver to offer fast and accurate gradient information. Ultimately, a wrapper program for the design optimization framework, UNPAC-DOF, has been developed. The nominal and adjoint flow solutions are directly incorporated into a gradient-based design optimization algorithm with the goal of improving designs in terms of minimized drag or maximized efficiency

Doctor of Philosophy

dissertationSolutions to Partial Di erential Equations (PDEs) are often computed by discretizing the domain into a collection of computational elements referred to as a mesh. This solution is an approximation with an error that decreases as the mesh spacing decreases. However, decreasing the mesh spacing also increases the computational requirements. Adaptive mesh re nement (AMR) attempts to reduce the error while limiting the increase in computational requirements by re ning the mesh locally in regions of the domain that have large error while maintaining a coarse mesh in other portions of the domain. This approach often provides a solution that is as accurate as that obtained from a much larger xed mesh simulation, thus saving on both computational time and memory. However, historically, these AMR operations often limit the overall scalability of the application. Adapting the mesh at runtime necessitates scalable regridding and load balancing algorithms. This dissertation analyzes the performance bottlenecks for a widely used regridding algorithm and presents two new algorithms which exhibit ideal scalability. In addition, a scalable space- lling curve generation algorithm for dynamic load balancing is also presented. The performance of these algorithms is analyzed by determining their theoretical complexity, deriving performance models, and comparing the observed performance to those performance models. The models are then used to predict performance on larger numbers of processors. This analysis demonstrates the necessity of these algorithms at larger numbers of processors. This dissertation also investigates methods to more accurately predict workloads based on measurements taken at runtime. While the methods used are not new, the application of these methods to the load balancing process is. These methods are shown to be highly accurate and able to predict the workload within 3% error. By improving the accuracy of these estimations, the load imbalance of the simulation can be reduced, thereby increasing the overall performance