Analysis and selection of the simulation environment

This document provides the initial report of the Simulation work package (Work Package 4,WP4) of the CATNETS project. It contains an analisys of the requirements for a simulation tool to be used in CATNETS and an evaluation of a number of grid and general purpose simulators with respect to the selected requirements. A reasoned choice of a suitable simulator is performed based on the evaluation conducted. -- Diese Arbeit analysiert die Anforderungen an eine Simulationsumgebung für die Analyse der Katallaxie. Anhand von Kennzahlen wird die Auswahl der Simulationsumgebung bestimmt.Grid Computing

Dynamic and fault tolerant three-dimensional cellular genetic algorithms

In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has been very active, especially in the last decade. These algorithms started to evolve when scientists from various regions of the world applied the principles of evolution to algorithmic search and problem solving. EAs have been utilised successfully in diverse complex application areas. Their success in tackling hard problems has been the engine of the field of Evolutionary Computation (EC). Nowadays, EAs are considered to be the best solution to use when facing a hard search or optimisation problem. Various improvements are continually being made with the design of new operators, hybrid models, among others. A very important example of such improvements is the use of parallel models of GAs (PGAs). PGAs have received widespread attention from various researchers as they have proved to be more effective than panmictic GAs, especially in terms of efficacy and speedup. This thesis focuses on, and investigates, cellular Genetic Algorithms (cGAs)-a competitive variant of parallel GAs. In a cGA, the tentative solutions evolve in overlapped neighbourhoods, allowing smooth diffusion of the solutions. The benefits derived from using cGAs come not only from flexibility gains and their fitness to the objective target in combination with a robust behaviour but also from their high performance and amenability to implementation using advanced custom silicon chip technologies. Nowadays, cGAs are considered as adaptable concepts for solving problems, especially complex optimisation problems. Due to their structural characteristics, cGAs are able to promote an adequate exploration/exploitation trade-off and thus maintain genetic diversity. Moreover, cGAs are characterised as being massively parallel and easy to implement. The structural characteristics inherited in a cGA provide an active area for investigation. Because of the vital role grid structure plays in determining the effectiveness of the algorithm, cellular dimensionality is the main issue to be investigated here. The implementation of cGAs is commonly carried out on a one- or two-dimensional structure. Studies that investigate higher cellular dimensions are lacking. Accordingly, this research focuses on cGAs that are implemented on a three-dimensional structure. Having a structure with three dimensions, specifically a cubic structure, facilitates faster spreading of solutions due to the shorter radius and denser neighbourhood that result from the vertical expansion of cells. In this thesis, a comparative study of cellular dimensionality is conducted. Simulation results demonstrate higher performance achieved by 3D-cGAs over their 2D-cGAs counterparts. The direct implementation of 3D-cGAs on the new advanced 3D-IC technology will provide added benefits such as higher performance combined with a reduction in interconnection delays, routing length, and power consumption. The maintenance of system reliability and availability is a major concern that must be addressed. A system is likely to fail due to either hard or soft errors. Therefore, detecting a fault before it deteriorates system performance is a crucial issue. Single Event Upsets (SEUs), or soft errors, do not cause permanent damage to system functionality, and can be handled using fault-tolerant techniques. Existing fault-tolerant techniques include hardware or software fault tolerance, or a combination of both. In this thesis, fault-tolerant techniques that mitigate SEUs at the algorithmic level are explored and the inherent abilities of cGAs to deal with these errors are investigated. A fault-tolerant technique and several mitigation techniques are also proposed, and faulty critical data are evaluated critical fault scenarios (stuck at ‘1’ and stuck at ‘0’ faults) are taken into consideration. Chief among several test and real world problems is the problem of determining the attitude of a vehicle using a Global Positioning System (GPS), which is an example of hard real-time application. Results illustrate the ability of cGAs to maintain their functionality and give an adequate performance even with the existence of up to 40% errors in fitness score cells. The final aspect investigated in this thesis is the dynamic characteristic of cGAs. cGAs, and EAs in general, are known to be stochastic search techniques. Hence, adaptive systems are required to continue to perform effectively in a changing environment, particularly when tackling real-world problems. The adaptation in cellular engines is mainly achieved through dynamic balancing between exploration and exploitation. This area has received considerable attention from researchers who focus on improving the algorithmic performance without incurring additional computational effort. The structural properties and the genetic operations provide ways to control selection pressure and, as a result, the exploration/exploitation trade-off. In this thesis, the genetic operations of cGAs, particularly the selection aspect and their influence on the search process, are investigated in order to dynamically control the exploration/exploitation trade-off. Two adaptive-dynamic techniques that use genetic diversity and convergence speeds to guide the search are proposed. Results obtained by evaluating the proposed approaches on a test bench of diverse-characteristic real-world and test problems showed improvement in dynamic cGAs performance over their static counterparts and other dynamic cGAs. For example, the proposed Diversity-Guided 3D-cGA outperformed all the other dynamic cGAs evaluated by obtaining a higher search success rate that reached to 55%

The exploitation of parallelism on shared memory multiprocessors

PhD ThesisWith the arrival of many general purpose shared memory multiple processor (multiprocessor) computers into the commercial arena during the mid-1980's, a rift has opened between the raw processing power offered by the emerging hardware and the relative inability of its operating software to effectively deliver this power to potential users. This rift stems from the fact that, currently, no computational model with the capability to elegantly express parallel activity is mature enough to be universally accepted, and used as the basis for programming languages to exploit the parallelism that multiprocessors offer. To add to this, there is a lack of software tools to assist programmers in the processes of designing and debugging parallel programs. Although much research has been done in the field of programming languages, no undisputed candidate for the most appropriate language for programming shared memory multiprocessors has yet been found. This thesis examines why this state of affairs has arisen and proposes programming language constructs, together with a programming methodology and environment, to close the ever widening hardware to software gap. The novel programming constructs described in this thesis are intended for use in imperative languages even though they make use of the synchronisation inherent in the dataflow model by using the semantics of single assignment when operating on shared data, so giving rise to the term shared values. As there are several distinct parallel programming paradigms, matching flavours of shared value are developed to permit the concise expression of these paradigms.The Science and Engineering Research Council

Design and Implementation of a Computational Platform and a Parallelized Interaction Analysis for Large Scale Genomics Data in Multiple Sclerosis

Abstract The multiple sclerosis (MS) genetics research group led by professor Jan Hillert at Karolinska Institutet, focuses on investigating the aetiology of the disease. Samples have been collected routinely from patients visiting the clinic for decades. From these samples, large amounts of genetics data is being generated. The traditional methods of analyzing the data is becoming increasingly inefficient as data sets grow larger. New approaches are needed to perform the analyses. This thesis gives an introduction to the relevant genetics and discusses possible approaches for enabling more efficient execution of legacy analysis tools, as well as improving a gene-environment and gene-gene interaction analysis. Different computational paradigms are presented followed by the implementation of a computational platform to support the researchers' existing, and possibly future, analysis needs. The improved interaction analysis application is then implemented and executed in a virtual instance of this platform. The performance of the analysis application is then evaluated with respect to the original reference application. Referat Design och implementation av beräkningsplattform och paralelliserad interaktionsanalys för storskaliga genetiska data inom multipel skleros Professor Jan Hillert vid Karolinska Institutet leder en forskargrupp som fokuserar på etiologin bakom multipel skleros (MS). Under flera årtionden har patientprover samlats in från kliniken och från dessa prover har stora mängder genetiska data genererats. De traditionella analysmetoderna blir allt mer ineffektiva då datamängderna öker. Det finns ett stort behov av nya tillvägagångssätt och metoder för att analysera dessa data. Denna uppsats ger en introduktion i relevant genetik och diskuterar olika tillvägagångssätt för att möjliggöra effektivare exekvering av befintliga analysverktyg, så väl som förbättring av en gen-miljö och gen-gen-interaktionsanalys. Olika etablerade beräkningsparadigmer presenteras, följt av en implementation av en beräkningsplattform som ett stöd i att tillgodose forskargruppens nuvarande och möjli-ga framtida behov. Den förbättrade interaktionsanalysen är sedan implementerad och exekverad i en virtuell instans av plattformen. Interaktionsanalysens prestanda utvärderas sedan och jämförs med ursprungsimplementationen

The connection machine

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Bibliography: leaves 134-157.by William Daniel Hillis.Ph.D

GENETIC ALGORITHM CONTROLLED COMMON SUBEXPRESSION ELIMINATION FOR SPILL-FREE REGISTER ALLOCATION

As code complexity increases, maxlive increases. This is especially true in the case of the Kentucky If-Then-Else architecture proposed for Nanocontrollers. To achieve low circuit complexity, computations are decomposed to bit-level operations, thus generating large blocks of code with complex dependence structures. Additionally, the Nanocontroller architecture allows for only a small number of single bit registers and no extra memory. The assumption of an infinite number of registers made during code generation becomes a huge problem during register allocation because the small number of registers and no additional memory. The large basic blocks mean that maxlive almost always exceeds the number of registers and the traditional methods of register allocation such as instruction re-ordering and register spill/reload cannot be applied trivially. This thesis deals with finding a solution to reduce maxlive for successful register allocation using Genetic Algorithms