Accelerated Steady-State Torque Computation for Induction Machines using Parallel-In-Time Algorithms

This paper focuses on efficient steady-state computations of induction machines. In particular, the periodic Parareal algorithm with initial-value coarse problem (PP-IC) is considered for acceleration of classical time-stepping simulations via non-intrusive parallelization in time domain, i.e., existing implementations can be reused. Superiority of this parallel-in-time method is in its direct applicability to time-periodic problems, compared to, e.g, the standard Parareal method, which only solves an initial-value problem, starting from a prescribed initial value. PP-IC is exploited here to obtain the steady state of several operating points of an induction motor, developed by Robert Bosch GmbH. Numerical experiments show that acceleration up to several dozens of times can be obtained, depending on availability of parallel processing units. Comparison of PP-IC with existing time-periodic explicit error correction method highlights better robustness and efficiency of the considered time-parallel approach

Applications and accuracy of the parallel diagonal dominant algorithm

The Parallel Diagonal Dominant (PDD) algorithm is a highly efficient, ideally scalable tridiagonal solver. In this paper, a detailed study of the PDD algorithm is given. First the PDD algorithm is introduced. Then the algorithm is extended to solve periodic tridiagonal systems. A variant, the reduced PDD algorithm, is also proposed. Accuracy analysis is provided for a class of tridiagonal systems, the symmetric, and anti-symmetric Toeplitz tridiagonal systems. Implementation results show that the analysis gives a good bound on the relative error, and the algorithm is a good candidate for the emerging massively parallel machines

A Semicoarsening Multigrid Algorithm for SIMD Machines

A semicoarsening multigrid algorithm suitable for use on single instruction multiple data (SIMD) architectures has been implemented on the CM-2. The method performs well for strongly anisotropic problems and for problems with coefficients jumping by orders of magnitude across internal interfaces. The parallel efficiency of this method is analyzed, and its actual performance is compared with its performance on some other machines, both parallel and nonparallel

An efficient steady-state analysis of the eddy current problem using a parallel-in-time algorithm

This paper introduces a parallel-in-time algorithm for efficient steady-state solution of the eddy current problem. Its main idea is based on the application of the well-known multi-harmonic (or harmonic balance) approach as the coarse solver within the periodic parallel-in-time framework. A frequency domain representation allows for the separate calculation of each harmonic component in parallel and therefore accelerates the solution of the time-periodic system. The presented approach is verified for a nonlinear coaxial cable model

Structural Drift: The Population Dynamics of Sequential Learning

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream teacher and then pass samples from the model to their downstream student. It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.Comment: 15 pages, 9 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/sdrift.ht

Building on the DEPLOY legacy: code generation and simulation

The RODIN, and DEPLOY projects have laid solid foundations for further theoretical, and practical (methodological and tooling) advances with Event-B; we investigated code generation for embedded, multi-tasking systems. This work describes activities from a follow-on project, ADVANCE; where our interest is co-simulation of cyber-physical systems. We are working to better understand the issues arising in a development when modelling with Event-B, and animating with ProB, in tandem with a multi-simulation strategy. With multi-simulation we aim to simulate various features of the environment separately, in order to exercise the deployable code. This paper has two contributions, the first is the extension of the code generation work of DEPLOY, where we add the ability to generate code from Event-B state-machine diagrams. The second describes how we may use code, generated from state-machines, to simulate the environment, and simulate concurrently executing state-machines, in a single task. We show how we can instrument the code to guide the simulation, by controlling the relative rate that non-deterministic transitions are traversed in the simulation

Tasking Event-B: An Extension to Event-B for Generating Concurrent Code

The Event-B method is a formal approach for modelling systems in safety-, and business-critical, domains. Initially, system specification takes place at a high level of abstraction; detail is added in refinement steps as the development proceeds toward implementation. Our aim has been to develop a novel approach for generating code, for concurrent programs, from Event-B. We formulated the approach so that it integrates well with the existing Event-B methodology and tools. In this paper we introduce a tasking extension for Event-B, with Tasking and Shared Machines. We make use of refinement, decomposition, and the extension, to structure projects for code generation for multitasking implementations. During the modelling phase decomposition is performed; decomposition reduces modelling complexity and makes proof more tractable. The decomposed models are then extended with sufficient information to enable generation of code. A task body describes a task’s behaviour, mainly using imperative, programming-like constructs. Task priority and life-cycle (periodic, triggered, etc.) are also specified, but timing aspects are not modelled formally. We provide tool support in order to validate the practical aspects of the approach

Building on the DEPLOY Legacy: Code Generation and Simulation

The RODIN, and DEPLOY projects laid solid foundations for further theoretical, and practical (methodological and tooling) advances with Event-B. Our current interest is the co-simulation of cyber-physical systems using Event-B. Using this approach we aim to simulate various features of the environment separately, in order to exercise deployable code. This paper has two contributions, the first is the extension of the code generation work of DEPLOY, where we add the ability to generate code from Event-B state-machine diagrams. The second describes how we may use code, generated from state-machines, to simulate the environment, and simulate concurrently executing state-machines, in a single task. We show how we can instrument the code to guide the simulation, by controlling the relative rate that non-deterministic transitions are traversed in the simulation.Comment: In Proceedings of DS-Event-B 2012: Workshop on the experience of and advances in developing dependable systems in Event-B, in conjunction with ICFEM 2012 - Kyoto, Japan, November 13, 201