Performance analysis of a hardware accelerator of dependence management for taskbased dataflow programming models

Along with the popularity of multicore and manycore, task-based dataflow programming models obtain great attention for being able to extract high parallelism from applications without exposing the complexity to programmers. One of these pioneers is the OpenMP Superscalar (OmpSs). By implementing dynamic task dependence analysis, dataflow scheduling and out-of-order execution in runtime, OmpSs achieves high performance using coarse and medium granularity tasks. In theory, for the same application, the more parallel tasks can be exposed, the higher possible speedup can be achieved. Yet this factor is limited by task granularity, up to a point where the runtime overhead outweighs the performance increase and slows down the application. To overcome this handicap, Picos was proposed to support task-based dataflow programming models like OmpSs as a fast hardware accelerator for fine-grained task and dependence management, and a simulator was developed to perform design space exploration. This paper presents the very first functional hardware prototype inspired by Picos. An embedded system based on a Zynq 7000 All-Programmable SoC is developed to study its capabilities and possible bottlenecks. Initial scalability and hardware consumption studies of different Picos designs are performed to find the one with the highest performance and lowest hardware cost. A further thorough performance study is employed on both the prototype with the most balanced configuration and the OmpSs software-only alternative. Results show that our OmpSs runtime hardware support significantly outperforms the software-only implementation currently available in the runtime system for finegrained tasks.This work is supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272) and by the European Research Council RoMoL Grant Agreement number 321253. We also thank the Xilinx University Program for its hardware and software donations.Peer ReviewedPostprint (published version

Defining software configuration management for product development

Yleensä ohjelmistokonfiguraation hallinnan käyttömalli ohjelmistokehitysprojektille on valittu tekemällä ainutkertainen soveltuvuustutkimus jokaiselle ohjelmistokehitysprojektille erikseen. Tällainen päätöksentekoprosessi on tarkka, mutta se vaatii paljon aikaa sekä resursseja. Onkin tarve pystyä helpottamaan ohjelmistokonfiguraation hallinnan käyttömallin valintaa, jotta päätöksentekoprosessista saadaan nopeampi ja kannattavampi. Tämän diplomityön tavoite on löytää tekijät, jotka vaikuttavat ohjelmistokonfiguraation hallinta prosessiin ohjelmistokehitysympäristössä, sekä luoda kysymyslista jota pystytään hyödyntämään ohjelmistokehitysympäristön ominaisuuksien tutkimisessa. Lisäksi tutkitaan miten eri Nokia Siemens Networksillä käytettävät versionhallintajärjestelmät ja ohjelmistokonfiguraation hallinnan käyttömallit tukevat löydettyjä vaatimuksia. Yhdistämällä nämä kaksi tutkimustulosta pystytään saamaan hyvä pohja kun suunnitellaan ohjelmistokonfiguraation hallinnan prosesseja uusille tai jo olemassa oleville ohjelmistokehitysprojekteille. Tutkittaessa eri ohjelmistokonfiguraation hallinnan prosesseja keskityttiin jokaisen prosessin vahvuuksiin, heikkouksiin, sekä samankaltaisuuksiin. Tämä sen takia, koska ohjelmistokonfiguraation hallinnan työkalut, sekä prosessit sisältävät ominaisuuksia, jotka soveltuvat paremmin eri ohjelmistokehitysympäristöihin kuin toiset.Normally, the usage model for Software Configuration Management (SCM) used in software development projects has been chosen by making a individual feasibility studies for each project. This way of making decisions is accurate, but it requires plenty of time and resources. There is a need to ease the selection of a SCM usage model to increase speed and profitability of decision making process. The object of this thesis is to discover the factors affecting the software configuration management process in the SW development environment and create question lists that can be exploited when investigating the characteristics of SW development. In addition, we have studied how different version control systems and software configuration management usage models used inside Nokia Siemens Networks handle these different requirements for SCM. When combining these two outcomes we have a good basis for designing new software configuration management processes for new or existing software development projects. When comparing different SCM processes, we have studied the strengths, disadvantages and similarities of each solution. This is, due to the fact that different software configuration management tools and processes have characteristics that can be applied better to one software development context than in another

From Agile to DevOps, Holistic Approach for Faster and Efficient Software Product Release Management

Release management is one of the most important software processes and is a set of processes that includes the compilation, configuration, and management of software versions in different environments. In recent years, changes in processes, technologies, and tools and changes in practices and understanding have paved the way for more effective, efficient, sustainable, reusable models and methods in this field. The purpose of this study is to examine the DevOps idea to produce a flow, highlight their benefits, and investigate with a model how these philosophies, which are two of the most important processes and methods in software development today, can reveal an effective release management process. What has been learned from the research is how the agile and DevOps practices, which have become widespread in recent years, can be positioned in a general flow in the release management process, although there are different practices, flows, disciplines, and technology. Sharing a case study on these issues in future studies and an experience sharing research where the flow is applied as a case study will reveal positive feedback on the real-life application and results of the flow and the model. Further, a literature review studies in which deficiencies in the literature are identified will be useful in determining the gaps in the process.Comment: You can find published version: https://dergipark.org.tr/tr/pub/@fatihbildiric

Integrated Management of Variability in Space and Time in Software Families

Software Product Lines (SPLs) and Software Ecosystems (SECOs) are approaches to capturing families of closely related software systems in terms of common and variable functionality (variability in space). SPLs and especially SECOs are subject to software evolution to adapt to new or changed requirements resulting in different versions of the software family and its variable assets (variability in time). Both dimensions may be interconnected (e.g., through version incompatibilities) and, thus, have to be handled simultaneously as not all customers upgrade their respective products immediately or completely. However, there currently is no integrated approach allowing variant derivation of features in different version combinations. In this thesis, remedy is provided in the form of an integrated approach making contributions in three areas: (1) As variability model, Hyper-Feature Models (HFMs) and a version-aware constraint language are introduced to conceptually capture variability in time as features and feature versions. (2) As variability realization mechanism, delta modeling is extended for variability in time, and a language creation infrastructure is provided to devise suitable delta languages. (3) For the variant derivation procedure, an automatic version selection mechanism is presented as well as a procedure to derive large parts of the application order for delta modules from the structure of the HFM. The presented integrated approach enables derivation of concrete software systems from an SPL or a SECO where both features and feature versions may be configured.:I. Context and Preliminaries 1. The Configurable TurtleBot Driver as Running Example 1.1. TurtleBot: A Domestic Service Robot 1.2. Configurable Driver Functionality 1.3. Software Realization Artifacts 1.4. Development History of the Driver Software 2. Families of Variable Software Systems 2.1. Variability 2.1.1. Variability in Space and Time 2.1.2. Internal and External Variability 2.2. Manifestations of Configuration Knowledge 2.2.1. Variability Models 2.2.2. Variability Realization Mechanisms 2.2.3. Variability in Realization Assets 2.3. Types of Software Families 2.3.1. Software Product Lines 2.3.2. Software Ecosystems 2.3.3. Comparison of Software Product Lines and Software Ecosystems 3. Fundamental Approaches and Technologies of the Thesis 3.1. Model-Driven Software Development 3.1.1. Metamodeling Levels 3.1.2. Utilizing Models in Generative Approaches 3.1.3. Representation of Languages using Metamodels 3.1.4. Changing the Model-Representation of Artifacts 3.1.5. Suitability of Model-Driven Software Development 3.2. Fundamental Variability Management Techniques of the Thesis 3.2.1. Feature Models as Variability Models 3.2.2. Delta Modeling as Variability Realization Mechanism 3.2.3. Variant Derivation Process of Delta Modeling with Feature Models 3.3. Constraint Satisfaction Problems 3.4. Scope 3.4.1. Problem Statement 3.4.2. Requirements 3.4.3. Assumptions and Boundaries II. Integrated Management of Variability in Space and Time 4. Capturing Variability in Space and Time with Hyper-Feature Models 4.1. Feature Models Cannot Capture Variability in Time 4.2. Formal Definition of Feature Models 4.3. Definition of Hyper-Feature Models 4.4. Creation of Hyper-Feature Model Versions 4.5. Version-Aware Constraints to Represent Version Dependencies and Incompatibilities 4.6. Hyper-Feature Models are a True Extension to Feature Models 4.7. Case Study 4.8. Demarcation from Related Work 4.9. Chapter Summary 5. Creating Delta Languages Suitable for Variability in Space and Time 5.1. Current Delta Languages are not Suitable for Variability in Time 5.2. Software Fault Trees as Example of a Source Language 5.3. Evolution Delta Modules as Manifestation of Variability in Time 5.4. Automating Delta Language Generation 5.4.1. Standard Delta Operations Realize Usual Functionality 5.4.2. Custom Delta Operations Realize Specialized Functionality 5.5. Delta Language Creation Infrastructure 5.5.1. The Common Base Delta Language Provides Shared Functionality for all Delta Languages 5.5.2. Delta Dialects Define Delta Operations for Custom Delta Languages 5.5.3. Custom Delta Languages Enable Variability in Source Languages 5.6. Case Study 5.7. Demarcation from Related Work 5.8. Chapter Summary 6. Deriving Variants with Variability in Space and Time 6.1. Variant Derivation Cannot Handle Variability in Time 6.2. Associating Features and Feature Versions with Delta Modules 6.3. Automatically Select Versions to Ease Configuration 6.4. Application Order and Implicitly Required Delta Modules 6.4.1. Determining Relevant Delta Modules 6.4.2. Forming a Dependency Graph of Delta Modules 6.4.3. Performing a Topological Sorting of Delta Modules 6.5. Generating Variants with Versions of Variable Assets 6.6. Case Study 6.7. Demarcation from Related Work 6.8. Chapter Summary III. Realization and Application 7. Realization as Tool Suite DeltaEcore 7.1. Creating Delta Languages 7.1.1. Shared Base Metamodel 7.1.2. Common Base Delta Language 7.1.3. Delta Dialects 7.2. Specifying a Software Family with Variability in Space and Time 7.2.1. Hyper-Feature Models 7.2.2. Version-Aware Constraints 7.2.3. Delta Modules 7.2.4. Application-Order Constraints 7.2.5. Mapping Models 7.3. Deriving Variants 7.3.1. Creating a Configuration 7.3.2. Collecting Delta Modules 7.3.3. Ordering Delta Modules 7.3.4. Applying Delta Modules 8. Evaluation 8.1. Configurable TurtleBot Driver Software 8.1.1. Variability in Space 8.1.2. Variability in Time 8.1.3. Integrated Management of Variability in Space and Time 8.2. Metamodel Family for Role-Based Modeling and Programming Languages 8.2.1. Variability in Space 8.2.2. Variability in Time 8.2.3. Integrated Management of Variability in Space and Time 8.3. A Software Product Line of Feature Modeling Notations and Constraint Languages 8.3.1. Variability in Space 8.3.2. Variability in Time 8.3.3. Integrated Management of Variability in Space and Time 8.4. Results and Discussion 8.4.1. Results and Discussion of RQ1: Variability Model 8.4.2. Results and Discussion of RQ2: Variability Realization Mechanism 8.4.3. Results and Discussion of RQ3: Variant Derivation Procedure 9. Conclusion 9.1. Discussion 9.1.1. Supported Evolutionary Changes 9.1.2. Conceptual Representation of Variability in Time 9.1.3. Perception of Versions as Incremental 9.1.4. Version Numbering Schemes 9.1.5. Created Delta Languages 9.1.6. Scalability of Approach 9.2. Possible Future Application Areas 9.2.1. Extend to Full Software Ecosystem Feature Model 9.2.2. Model Software Ecosystems 9.2.3. Extract Hyper-Feature Model Versions and Record Delta Modules 9.2.4. Introduce Metaevolution Delta Modules 9.2.5. Support Incremental Reconfiguration 9.2.6. Apply for Evolution Analysis and Planning 9.2.7. Enable Evolution of Variable Safety-Critical Systems 9.3. Contribution 9.3.1. Individual Contributions 9.3.2. Handling Updater Stereotypes IV. Appendix A. Delta Operation Generation Algorithm B. Delta Dialects B.1. Delta Dialect for Java B.2. Delta Dialect for Eclipse Projects B.3. Delta Dialect for DocBook Markup B.4. Delta Dialect for Software Fault Trees B.5. Delta Dialect for Component Fault Diagrams B.6. Delta Dialect for Checklists B.7. Delta Dialect for the Goal Structuring Notation B.8. Delta Dialect for EMF Ecore B.9. Delta Dialect for EMFText Concrete Syntax File

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Environments to support collaborative software engineering

With increasing globalisation of software production, widespread use of software components, and the need to maintain software systems over long periods of time, there has been a recognition that better support for collaborative working is needed by software engineers. In this paper, two approaches to developing improved system support for collaborative software engineering are described: GENESIS and OPHELIA. As both projects are moving towards industrial trials and eventual publicreleases of their systems, this exercise of comparing and contrasting our approaches has provided the basis for future collaboration between our projects particularly in carrying out comparative studies of our approaches in practical use