08071 Abstracts Collection -- Scheduling

From 10.02. to 15.02., the Dagstuhl Seminar 08071 ``Scheduling\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Parallel Weighted Random Sampling

Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps both for shared-memory and distributed-memory machines. We give efficient, fast, and practicable algorithms for sampling single items, k items with/without replacement, permutations, subsets, and reservoirs. We also give improved sequential algorithms for alias table construction and for sampling with replacement. Experiments on shared-memory parallel machines with up to 158 threads show near linear speedups both for construction and queries

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'

Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization

Das Ziel des automatischen maschinellen Lernens (AutoML) ist es, alle Aspekte der Modellwahl in prädiktiver Modellierung zu automatisieren. Diese Arbeit beschäftigt sich mit Gradienten Boosting im Kontext von AutoML mit einem Fokus auf Gradient Tree Boosting und komponentenweisem Boosting. Beide Techniken haben eine gemeinsame Methodik, aber ihre Zielsetzung ist unterschiedlich. Während Gradient Tree Boosting im maschinellen Lernen als leistungsfähiger Vorhersagealgorithmus weit verbreitet ist, wurde komponentenweises Boosting im Rahmen der Modellierung hochdimensionaler Daten entwickelt. Erweiterungen des komponentenweisen Boostings auf multidimensionale Vorhersagefunktionen werden in dieser Arbeit ebenfalls untersucht. Die Herausforderung der Hyperparameteroptimierung wird mit Fokus auf Bayesianische Optimierung und effiziente Stopping-Strategien diskutiert. Ein groß angelegter Benchmark über Hyperparameter verschiedener Lernalgorithmen, zeigt den kritischen Einfluss von Hyperparameter Konfigurationen auf die Qualität der Modelle. Diese Daten können als Grundlage für neue AutoML- und Meta-Lernansätze verwendet werden. Darüber hinaus werden fortgeschrittene Strategien zur Variablenselektion zusammengefasst und eine neue Methode auf Basis von permutierten Variablen vorgestellt. Schließlich wird ein AutoML-Ansatz vorgeschlagen, der auf den Ergebnissen und Best Practices für die Variablenselektion und Hyperparameteroptimierung basiert. Ziel ist es AutoML zu vereinfachen und zu stabilisieren sowie eine hohe Vorhersagegenauigkeit zu gewährleisten. Dieser Ansatz wird mit AutoML-Methoden, die wesentlich komplexere Suchräume und Ensembling Techniken besitzen, verglichen. Vier Softwarepakete für die statistische Programmiersprache R sind Teil dieser Arbeit, die neu entwickelt oder erweitert wurden: mlrMBO: Ein generisches Paket für die Bayesianische Optimierung; autoxgboost: Ein AutoML System, das sich vollständig auf Gradient Tree Boosting fokusiert; compboost: Ein modulares, in C++ geschriebenes Framework für komponentenweises Boosting; gamboostLSS: Ein Framework für komponentenweises Boosting additiver Modelle für Location, Scale und Shape.The goal of automatic machine learning (AutoML) is to automate all aspects of model selection in (supervised) predictive modeling. This thesis deals with gradient boosting techniques in the context of AutoML with a focus on gradient tree boosting and component-wise gradient boosting. Both techniques have a common methodology, but their goal is quite different. While gradient tree boosting is widely used in machine learning as a powerful prediction algorithm, component-wise gradient boosting strength is in feature selection and modeling of high-dimensional data. Extensions of component-wise gradient boosting to multidimensional prediction functions are considered as well. Focusing on Bayesian optimization and efficient early stopping strategies the challenge of hyperparameter optimization for these algorithms is discussed. Difficulty in the optimization of these algorithms is shown by a large scale random search on hyperparameters for machine learning algorithms, that can build the foundation of new AutoML and metalearning approaches. Furthermore, advanced feature selection strategies are summarized and a new method based on shadow features is introduced. Finally, an AutoML approach based on the results and best practices for feature selection and hyperparameter optimization is proposed, with the goal of simplifying and stabilizing AutoML while maintaining high prediction accuracy. This is compared to AutoML approaches using much more complex search spaces and ensembling techniques. Four software packages for the statistical programming language R have been newly developed or extended as a part of this thesis: mlrMBO: A general framework for Bayesian optimization; autoxgboost: An automatic machine learning framework that heavily utilizes gradient tree boosting; compboost: A modular framework for component-wise boosting written in C++; gamboostLSS: A framework for component-wise boosting for generalized additive models for location scale and shape

Computational Frameworks for Multi-Robot Cooperative 3D Printing and Planning

This dissertation proposes a novel cooperative 3D printing (C3DP) approach for multi-robot additive manufacturing (AM) and presents scheduling and planning strategies that enable multi-robot cooperation in the manufacturing environment. C3DP is the first step towards achieving the overarching goal of swarm manufacturing (SM). SM is a paradigm for distributed manufacturing that envisions networks of micro-factories, each of which employs thousands of mobile robots that can manufacture different products on demand. SM breaks down the complicated supply chain used to deliver a product from a large production facility from one part of the world to another. Instead, it establishes a network of geographically distributed micro-factories that can manufacture the product at a smaller scale without increasing the cost. In C3DP, many printhead-carrying mobile robots work together to print a single part cooperatively. While it holds the promise to mitigate issues associated with gantry-based 3D printers, such as lack of scalability in print size and print speed, its realization is challenging because existing studies in the relevant literature do not address the fundamental issues in C3DP that stem from the amalgamation of the mobile nature of the robots, and continuous nature of the manufacturing tasks. To address this challenge, this dissertation asks two fundamental research questions: RQ1) How can the traditional 3D printing process be transformed to enable multi-robot cooperative AM? RQ2) How can cooperative manufacturing planning be realized in the presence of inherent uncertainties in AM and constraints that are dynamic in both space and time? To answer RQ1, we discretize the process of 3D printing into multiple stages. These stages include chunking (dividing a part into smaller chunks), scheduling (assigning chunks to robots and generating print sequences), and path and motion planning. To test the viability of the approach, we conducted a study on the tensile strength of chunk-based parts to examine their mechanical integrity. The study demonstrates that the chunk-based part can be as strong as the conventionally 3D-printed part. Next, we present different computational frameworks to address scheduling issues in C3DP. These include the development of 1) the world-first working strategy for C3DP, 2) a framework for automatic print schedule generation, evaluation, and validation, and 3) a resource-constrained scheduling approach for C3DP that uses a meta-heuristic approach such as a modified Genetic Algorithm (MGA) and a new algorithm that uses a constraint-satisficing approach to obtain collision-free print schedules for C3DP. To answer RQ2, a multi-robot decentralized approach based on a simple set of rules is used to plan for C3DP. The approach is resilient to uncertainties such as variation in printing times and can even outperform the centralized approach that uses MGA with a conflict-based search for large-scale problems. By answering these two fundamental questions, the central objective of the research project to establish computational frameworks to enable multi-robot cooperative manufacturing was achieved. The search for answers to the RQs led to the development of novel concepts that can be used not only in C3DP, but many other manufacturing tasks, in general, requiring cooperation among multiple robots

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Beyond 3D Printing: The New Dimensions of Additive Fabrication

Additive fabrication, often referred to as 3D printing, is the construction of objects by adding material. This stands in contrast to subtractive methods, which involve removing material by means of milling or cutting. Although additive fabrication and 3D printing are thought of as synonymous, additive fabrication encompasses a far broader range of construction, and new dimensions are on the horizon, inspiring innovation across scales and applications. For instance, can you print a full-scale building? How can we structurally engineer color and alter on the nanoscale? If trees grow additively, can biology be designed for fabrication

Logic, parallelism and semantic networks : the binary predicate execution model

This thesis develops the Binary Predicate Execution Model; a distributed, massively-parallel system for semantic networks and knowledge bases that is built on a subset of first-order predicate logic. The use of logic gives the model an easily-understood programming paradigm and a well-defined semantics of execution. When expressed in binary predicates, a simple graphical interpretation can be used. All program facts are represented in an assertion graph. Each vertex is associated with a term appearing in a fact and the edges are labeled with the predicate names. Similar graphs are also associated with each rule body and the query. Finding all possible solutions corresponds to finding all possible matches between the query graph and the assertion graph. Invoking a rule corresponds to substituting the graph of its body constrained by the dependencies between its arguments. This can be implemented in a parallel, message-passing fashion where the assertion graph vertices are active processing elements which asynchronously exchange messages identifying different parts of the query that remain to be matched and containing any binding information from previous matching required to accomplish this. The model is data-driven since every message can be immediately processed without the need for any centralized control or centralized memory. By restricting how functional terms can occur, distributed data structures and remote data look-ups for unification are eliminated. Thus, the model's performance on increasingly larger problems scales-up given increasingly larger machines in most cases. Architectural support for the model is investigated and simulation results of a relatively simple software implementation are reported. This suggests performance on the order of 10^5 logical inferences per second for 256 processing elements in an n-cube configuration. Further research directions, including that of increasing efficiency, are discussed