Modern techniques for constraint solving the CASPER experience

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaConstraint programming is a well known paradigm for addressing combinatorial problems which has enjoyed considerable success for solving many relevant industrial and academic problems. At the heart of constraint programming lies the constraint solver, a computer program which attempts to find a solution to the problem, i.e. an assignment of all the variables in the problemsuch that all the constraints are satisfied. This dissertation describes a set of techniques to be used in the implementation of a constraint solver. These techniques aim at making a constraint solver more extensible and efficient,two properties which are hard to integrate in general, and in particular within a constraint solver. Specifically, this dissertation addresses two major problems: generic incremental propagation and propagation of arbitrary decomposable constraints. For both problemswe present a set of techniques which are novel, correct, and directly concerned with extensibility and efficiency. All the material in this dissertation emerged from our work in designing and implementing a generic constraint solver. The CASPER (Constraint Solving Platformfor Engineering and Research)solver does not only act as a proof-of-concept for the presented techniques, but also served as the common test platform for the many discussed theoretical models. Besides the work related to the design and implementation of a constraint solver, this dissertation also presents the first successful application of the resulting platform for addressing an open research problem, namely finding good heuristics for efficiently directing search towards a solution

Summary of research in progress at ICASE

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period October 1, 1992 through March 31, 1993

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology

Random lattice particle modeling of fracture processes in cementitious materials

The capability of representing fracture processes in non-homogeneous media is of great interest among the scientific community for at least two reasons: the first one stems from the fact that the use of composite materials is ubiquitous within structural applications, since the advantages of the constituents can be exploited to improve material performance; the second consists of the need to assess the non-linear post-peak behavior of such structures to properly determine margins of safety with respect to strong excitations (e.g. earthquakes, blast or impact loadings). Different kinds of theories and methodologies have been developed in the last century in order to model such phenomena, starting from linear elastic equivalent methods, then moving to plastic theories and fracture mechanics. Among the different modeling techniques available, in recent years lattice models have established themselves as a powerful tool for simulating failure modes and crack paths in heterogeneous materials. The basic idea dates back to the pioneeristic work of Hrennikoff: a continuum medium can be modeled through the interaction of unidimensional elements (e.g. springs or beams) spatially arranged in different ways. The set of nodes that interconnect the elements can be regularly or irregularly placed inside the domain, leading to regular or random lattices. It has been shown that lattices with regular geometry can strongly bias the direction of cracking, leading to incorrect results. A variety of lattice models have been developed. Such models have seen a wide field of applications, ranging from aerodynamics (using Lattice-Boltzman models) to heat transfer, crystallography and many others. Every material used in civil and infrastructure engineering is constituted of different phases. This is due to the fact that the different features of different elements are usually coupled in order to obtain greater advantages with respect to the original constituents. Even structural steel, which is usually thought of as a homogeneous continuum-type medium, includes carbon particles that can be seen as inhomogeneities at the microscopic level. The mechanical behavior of concrete, which is the main object of the present work, is strongly affected not only by the presence of inclusions (i.e. the aggregates pieces) but also by their arrangement. For this reason, the explicit, statistical representation of their presence is of great interest in the simulations of concrete behavior. Lattice models can directly account for the presence of different phases, and so are advantageous from this perspective. The definition of such models, their implementation in a computer program, together with validation on laboratory tests will be presented. The present work will briefly review the state of the art and the basic principles of these models, starting from the geometrical and computing tools needed to build the simulations. The implementation of this technique in the Matlab environment will be presented, highlighting the theoretical background. The numerical results will be validated based on two complementary experimental campaigns,which focused on the meso- and macro-scales of concrete. Whereas the aim of this work is the representation of the quasi-brittle fracture processes in cementitious materials such as concrete, the discussed approach is general, and therefore valid for the representation of damage and crack growth in a variety of different materials

Identifying Graphs from Noisy Observational Data

There is a growing amount of data describing networks -- examples include social networks, communication networks, and biological networks. As the amount of available data increases, so does our interest in analyzing the properties and characteristics of these networks. However, in most cases the data is noisy, incomplete, and the result of passively acquired observational data; naively analyzing these networks without taking these errors into account can result in inaccurate and misleading conclusions. In my dissertation, I study the tasks of entity resolution, link prediction, and collective classification to address these deficiencies. I describe these tasks in detail and discuss my own work on each of these tasks. For entity resolution, I develop a method for resolving the identities of name mentions in email communications. For link prediction, I develop a method for inferring subordinate-manager relationships between individuals in an email communication network. For collective classification, I propose an adaptive active surveying method to address node labeling in a query-driven setting on network data. In many real-world settings, however, these deficiencies are not found in isolation and all need to be addressed to infer the desired complete and accurate network. Furthermore, because of the dependencies typically found in these tasks, the tasks are inherently inter-related and must be performed jointly. I define the general problem of graph identification which simultaneously performs these tasks; removing the noise and missing values in the observed input network and inferring the complete and accurate output network. I present a novel approach to graph identification using a collection of Coupled Collective Classifiers, C3, which, in addition to capturing the variety of features typically used for each task, can capture the intra- and inter-dependencies required to correctly infer nodes, edges, and labels in the output network. I discuss variants of C3 using different learning and inference paradigms and show the superior performance of C3, in terms of both prediction quality and runtime performance, over various previous approaches. I then conclude by presenting the Graph Alignment, Identification, and Analysis (GAIA) open-source software library which not only provides an implementation of C3 but also algorithms for various tasks in network data such as entity resolution, link prediction, collective classification, clustering, active learning, data generation, and analysis

SAT and CP: Parallelisation and Applications

This thesis is considered with the parallelisation of solvers which search for either an arbitrary, or an optimum, solution to a problem stated in some formal way. We discuss the parallelisation of two solvers, and their application in three chapters.In the first chapter, we consider SAT, the decision problem of propositional logic, and algorithms for showing the satisfiability or unsatisfiability of propositional formulas. We sketch some proof-theoretic foundations which are related to the strength of different algorithmic approaches. Furthermore, we discuss details of the implementations of SAT solvers, and show how to improve upon existing sequential solvers. Lastly, we discuss the parallelisation of these solvers with a focus on clause exchange, the communication of intermediate results within a parallel solver. The second chapter is concerned with Contraint Programing (CP) with learning. Contrary to classical Constraint Programming techniques, this incorporates learning mechanisms as they are used in the field of SAT solving. We present results from parallelising CHUFFED, a learning CP solver. As this is both a kind of CP and SAT solver, it is not clear which parallelisation approaches work best here. In the final chapter, we will discuss Sorting networks, which are data oblivious sorting algorithms, i. e., the comparisons they perform do not depend on the input data. Their independence of the input data lends them to parallel implementation. We consider the question how many parallel sorting steps are needed to sort some inputs, and present both lower and upper bounds for several cases

Coupled Kinetic-Fluid Simulations of Ganymede's Magnetosphere and Hybrid Parallelization of the Magnetohydrodynamics Model

The largest moon in the solar system, Ganymede, is the only moon known to possess a strong intrinsic magnetic field. The interaction between the Jovian plasma and Ganymede's magnetic field creates a mini-magnetosphere with periodically varying upstream conditions, which creates a perfect laboratory in nature for studying magnetic reconnection and magnetospheric physics. Using the latest version of Space Weather Modeling Framework (SWMF), we study the upstream plasma interactions and dynamics in this subsonic, sub-Alfvénic system. We have developed a coupled fluid-kinetic Hall Magnetohydrodynamics with embedded Particle-in-Cell (MHD-EPIC) model for Ganymede's magnetosphere, with a self-consistently coupled resistive body representing the electrical properties of the moon's interior, improved inner boundary conditions, and high resolution charge and energy conserved PIC scheme. I reimplemented the boundary condition setup in SWMF for more versatile control and functionalities, and developed a new user module for Ganymede's simulation. Results from the models are validated with Galileo magnetometer data of all close encounters and compared with Plasma Subsystem (PLS) data. The energy fluxes associated with the upstream reconnection in the model is estimated to be about 10^-7 W/cm^2, which accounts for about 40% to the total peak auroral emissions observed by the Hubble Space Telescope. We find that under steady upstream conditions, magnetopause reconnection in our fluid-kinetic simulations occurs in a non-steady manner. Flux ropes with length of Ganymede's radius form on the magnetopause at a rate about 3/minute and create spatiotemporal variations in plasma and field properties. Upon reaching proper grid resolutions, the MHD-EPIC model can resolve both electron and ion kinetics at the magnetopause and show localized crescent shape distribution in both ion and electron phase space, non-gyrotropic and non-isotropic behavior inside the diffusion regions. The estimated global reconnection rate from the models is about 80 kV with 60% efficiency. There is weak evidence of $sim 1$ minute periodicity in the temporal variations of the reconnection rate due to the dynamic reconnection process. The requirement of high fidelity results promotes the development of hybrid parallelized numerical model strategy and faster data processing techniques. The state-of-the-art finite volume/difference MHD code Block Adaptive Tree Solarwind Roe Upwind Scheme (BATS-R-US) was originally designed with pure MPI parallelization. The maximum problem size achievable was limited by the storage requirements of the block tree structure. To mitigate this limitation, we have added multithreaded OpenMP parallelization to the previous pure MPI implementation. We opt to use a coarse-grained approach by making the loops over grid blocks multithreaded and have succeeded in making BATS-R-US an efficient hybrid parallel code with modest changes in the source code while preserving the performance. Good weak scalings up to 50,0000 and 25,0000 of cores are achieved for the explicit and implicit time stepping schemes, respectively. This parallelization strategy greatly extends the possible simulation scale by an order of magnitude, and paves the way for future GPU-portable code development. To improve visualization and data processing, I have developed a whole new data processing workflow with the Julia programming language for efficient data analysis and visualization. As a summary, 1. I build a single fluid Hall MHD-EPIC model of Ganymede's magnetosphere; 2. I did detailed analysis of the upstream reconnection; 3. I developed a MPI+OpenMP parallel MHD model with BATSRUS; 4. I wrote a package for data analysis and visualization.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163032/1/hyzhou_1.pd

Analysis and application of Fourier-Motzkin variable elimination to program optimization : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand

This thesis examines four of the most influential dependence analysis techniques in use by optimizing compilers: Fourier-Motzkin Variable Elimination, the Banerjee Bounds Test, the Omega Test, and the I-Test. Although the performance and effectiveness of these tests have previously been documented empirically, no in-depth analysis of how these techniques are related from a purely analytical perspective has been done. The analysis given here clarifies important aspects of the empirical results that were noted but never fully explained. A tighter bound on the performance of one of the Omega Test algorithms than was known previously is proved and a link is shown between the integer refinement technique used in the Omega Test and the well-known Frobenius Coin Problem. The application of a Fourier-Motzkin based algorithm to the elimination of redundant bound checks in Java bytecode is described. A system which incorporated this technique improved performance on the Java Grande Forum Benchmark Suite by up to 10 percent