Performance Debugging and Tuning using an Instruction-Set Simulator

Instruction-set simulators allow programmers a detailed level of insight into, and control over, the execution of a program, including parallel programs and operating systems. In principle, instruction set simulation can model any target computer and gather any statistic. Furthermore, such simulators are usually portable, independent of compiler tools, and deterministic-allowing bugs to be recreated or measurements repeated. Though often viewed as being too slow for use as a general programming tool, in the last several years their performance has improved considerably. We describe SIMICS, an instruction set simulator of SPARC-based multiprocessors developed at SICS, in its rôle as a general programming tool. We discuss some of the benefits of using a tool such as SIMICS to support various tasks in software engineering, including debugging, testing, analysis, and performance tuning. We present in some detail two test cases, where we've used SimICS to support analysis and performance tuning of two applications, Penny and EQNTOTT. This work resulted in improved parallelism in, and understanding of, Penny, as well as a performance improvement for EQNTOTT of over a magnitude. We also present some early work on analyzing SPARC/Linux, demonstrating the ability of tools like SimICS to analyze operating systems

Bayesian leave-one-out cross-validation for large data

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of using approximate inference techniques and probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation for large datasets. We provide both theoretical and empirical results showing good properties for large data.Comment: Accepted to ICML 2019. This version is the submitted pape

Behaviour of Concrete Structural Members Subjected to Air Blast Loading

Numerical analysis is a suitable tool in the design of complex reinforced concrete structures under extreme impulsive loadings such as impacts or explosions at close range. Such events may be the result of terrorist attacks. Reinforced concrete is commonly used for buildings and infrastructures. For this reason, the ability to accurately run numerical simulations of concrete elements subjected to blast loading is needed. In this context, reliable constitutive models for concrete are of capital importance. In this research numerical simulations using two different constitutive models for concrete (Continuous Surface Cap Model and Brittle Damage Model) have been carried out using LS-DYNA. Two experimental benchmark tests have been taken as reference. The results of the numerical simulations with the aforementioned constitutive models show different abilities to accurately represent the structural response of the reinforced concrete elements studied

Wind and Wave Extremes over the World Oceans from Very Large Ensembles

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the dataset. The period (9 yrs) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for non-parametric 100-yr return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.Comment: 28 pages, 16 figure

Numerical analysis of the lubricant gap in external gear pumps considering micro level surface features

The design procedure has made the manufactures through experience aware that the tolerances in gear machines plays a significant influence on the system. In liter- ature there is only a few studies that investigates the phenomena of the geometrical micro details. This due to the complexity of the problem since it involves multiple domains and scales. This work will prove how it is possible to investigate the problem involving micro level details and the goal has been to study the effects with the aim of quantifying and proving the impact of a chamfer on the gears lateral side. The work has been performed by coupling the tooth space pressures solved by HYGESim (Hydralic Gear machines Simulator) with a numerical solver developed in an OpenFOAM environment. The simulations are carried out by solving Reynolds equations which has been proven to give accurate solutions to problems involving fluid films. Simulations involving both with and without the balancing squeeze term has been performed. Two different chamfers has been compared to a case without a chamfer at a fixed and constant gap height. The different cases are compared to each other by first separation the terms in Reynolds equation and then comparing the leakages to the drain and the power losses. It was found out that chamfer generates translational squeeze effects, which makes this study first of its kind. It was also found out that the generated hydrodynamic effects tends to increase with an increased chamfer. When it comes to the leakages, the chamfer has a positive effect, i.e decreased leakages. When comparing the power losses for one of the chamfers, the smaller one gives decreased losses while the bigger one generates increased losses. Simulations are also performed with the normal squeeze term taken into account, this to balance the generated forces by tilting the bushing. The results indicate that the hydrodynamic pressures generated to balance the bushing mainly originate from the tilt and not from the chamfer, even though the chamfer tends to decrease the maximum generated pressure and decreasing the tilt constant, t

Ambidexterity and Paradexterity: A typology of IT Governance contradictions

The theoretical construct of organizational ambidexterity addresses how organizations balance managerial contradictions such as exploitation and exploration or efficiency and flexibility. The underlying argument is that management should involve not a trade-off between two states, but simultaneous handling of contradictions. This paper expands the theory of organizational ambidexterity through introducing a typology of contradictions in the form of dichotomies and dualities within a particular management focus, i.e. IT Governance. The paper utilizes a previous study of IT Governance practice at two large, public universities to propose a typology and the concept of paradexterity. Through this, the paper seeks to add new knowledge to the fields of both organizational ambidexterity and IT Governance

Collocation Methods for Optimization in a Modelica Environment

The solution of generic dynamic optimization problems described by Modelica, and its extension Optimica, code using direct collocation methods is discussed. We start by providing a description of dynamic optimization problems in general and how to solve them by means of direct collocation. Next, an existing implementation of a collocation algorithm in JModelica.org, using CasADi and IPOPT, is presented. The extensions made to this implementation are reported. The new implementation is compared to an old C-based collocation algorithm in JModelica.org in two benchmarks. The presented benchmarks are based on a continuously stirred tank reactor and a combined cycle power plant. The new algorithm and its surrounding framework is more flexible and shown to be several times more efficient than its predecessor

Data-driven planning and prioritisation in maintenance: A case-study in the automotive industry

ABSTRACT Developments in computerised systems and digitalisation are pushing industry into the next industrial revolution, Industrie 4.0. The developments have pushed the maintenance organisation at Volvo Cars Torslanda to evolve their maintenance practices within data-driven decisions in planning and prioritisation of maintenance, through collaborating in research projects. The result of the developments was a decision support tool that uses data to automatically in real-time identify bottlenecks in the production systems, with the aim to prioritise the bottlenecks to increase the productivity of the factory. The system was implemented in the body shop at Volvo Cars Torslanda, but the developments stopped there. The thesis picks up where the development ended in order to identify organisational factors that are constraining the use of data-driven decisions in planning and prioritisation of maintenance, both considering the earlier developments but also on a broader scale. Further, to identify if data quality is sufficient for data-driven decisions. Through a questionnaire and interview-study it was identified that low data quality and insufficient support systems affect the trust to and use of data-driven decisions, which are constraining factors for transcending into a more data-driven organisation. However, it was identified that there is a drive for wanting to become more data-driven and a need for better prioritisations. The assessment of data quality performed through interviews with experts and an analysis of datasets, concluded in that the general data quality regarding context independent problems were sufficient but that there is improvement for context dependable issues that are only identifiable by the staff working in the daily operations. The overall result is that there is a need to improve the data quality in the support systems and to educate users how to fully exploit the systems, in order for Volvo Cars Torslanda to transform into being a data-driven maintenance organisation. If the industry and Volvo Cars Torslanda overcome the identified constraints there is a potential to be succeed in the transformation becoming more data-driven within maintenance, thereby taking the step into the future of maintenance

Orchestrating Digital Innovation: The Case of the Swedish Center for Digital Innovation

In recent years, researchers have paid increasing attention to how firms facilitate and enact digital innovation in networks with diverse actors (i.e., heterogeneous networks). However, while considerable evidence shows that firms can build key capabilities via engaging with external partners, we found few studies on how they orchestrate digital innovation in situations where an academic unit plays a facilitating role in the heterogeneous network. We address this question by focusing on experiences from a national academic initiative, the Swedish Center for Digital Innovation (SCDI). Formed in 2013, the SCDI has adopted an engaged scholarship approach and a combination of activities designed to increase digital innovation capabilities among partner organizations. We argue that acquiring new knowledge through external and internal sources stimulates firms and public sector organizations engaged in digital innovation to integrate such new knowledge with their existing knowledge base. Specifically, we demonstrate how SCDI’s core activities have created increased capabilities for the involved stakeholders, and we offer lessons learned and recommendations for academic units that wish to orchestrate digital innovation