04301 Abstracts Collection -- Cache-Oblivious and Cache-Aware Algorithms

The Dagstuhl Seminar 04301 ``Cache-Oblivious and Cache-Aware Algorithms\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl, from 18.07.2004 to 23.07.2004. 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

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible

Engineering Java 7's Dual Pivot Quicksort Using MaLiJAn

Wild S, Nebel M, Reitzig R, Laube U. Engineering Java 7's Dual Pivot Quicksort Using MaLiJan. In: Proceedings of the 15th Meeting on Algorithm Engineering and Experiments, ALENEX 2013, New Orleans, Louisiana, USA, January 7, 2013. Philadelphia, PA: Society for Industrial and Applied Mathematics; 2013: 55--69

Good Predictions Are Worth a Few Comparisons

Most modern processors are heavily parallelized and use predictors to guess the outcome of conditional branches, in order to avoid costly stalls in their pipelines. We propose predictor-friendly versions of two classical algorithms: exponentiation by squaring and binary search in a sorted array. These variants result in less mispredictions on average, at the cost of an increased number of operations. These theoretical results are supported by experimentations that show that our algorithms perform significantly better than the standard ones, for primitive data types

From Reduction-Based to Reduction-Free Normalization

We present a systematic construction of a reduction-free normalization function. Starting from a reduction-based normalization function, i.e., the transitive closure of a one-step reduction function, we successively subject it to refocusing (i.e., deforestation of the intermediate reduced terms), simplification (i.e., fusing auxiliary functions), refunctionalization (i.e., Church encoding), and direct-style transformation (i.e., the converse of the CPS transformation). We consider two simple examples and treat them in detail: for the first one, arithmetic expressions, we construct an evaluation function; for the second one, terms in the free monoid, we construct an accumulator-based flatten function. The resulting two functions are traditional reduction-free normalization functions. The construction builds on previous work on refocusing and on a functional correspondence between evaluators and abstract machines. It is also reversible

A framework proposal for algorithm animation systems

The learning and analysis of algorithms and algorithm concepts are challenging to students due to the abstract and conceptual nature of algorithms. Algorithm animation is a form of technological support tool which encourages algorithm comprehension by visualising algorithms in execution. Algorithm animation can potentially be utilised to support students while learning algorithms. Despite widespread acknowledgement for the usefulness of algorithm animation in algorithm courses at tertiary institutions, no recognised framework exists upon which algorithm animation systems can be effectively modelled. This dissertation consequently focuses on the design of an extensible algorithm animation framework to support the generation of interactive algorithm animations. A literature and extant system review forms the basis for the framework design process. The result of the review is a list of requirements for a pedagogically effective algorithm animation system. The proposed framework supports the pedagogic requirements by utilising an independent layer structure to support the generation and display of algorithm animations. The effectiveness of the framework is evaluated through the implementation of a prototype algorithm animation system using sorting algorithms as a case study. This dissertation is successful in proposing a framework to support the development of algorithm animations. The prototype developed will enable the integration of algorithm animations into the Nelson Mandela Metropolitan University’s teaching model, thereby permitting the university to conduct future research relating to the usefulness of algorithm animation in algorithm courses