Automatic synthesis and optimization of chip multiprocessors

The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

Evolutionary approaches to optimisation in rough machining

This thesis concerns the use of Evolutionary Computation to optimise the sequence and selection of tools and machining parameters in rough milling applications. These processes are not automated in current Computer-Aided Manufacturing (CAM) software and this work, undertaken in collaboration with an industrial partner, aims to address this. Related research has mainly approached tool sequence optimisation using only a single tool type, and machining parameter optimisation of a single-tool sequence. In a real world industrial setting, tools with different geometrical profiles are commonly used in combination on rough machining tasks in order to produce components with complex sculptured surfaces. This work introduces a new representation scheme and search operators to support the use of the three most commonly used tool types: end mill, ball nose and toroidal. Using these operators, single-objective metaheuristic algorithms are shown to find near-optimal solutions, while surveying only a small number of tool sequences. For the first time, a multi-objective approach is taken to tool sequence optimisation. The process of ‘multi objectivisation’ is shown to offer two benefits: escaping local optima on deceptive multimodal search spaces and providing a selection of tool sequence alternatives to a machinist. The multi-objective approach is also used to produce a varied set of near-Pareto optimal solutions, offering different trade-offs between total machining time and total tooling costs, simultaneously optimising tool sequences and the cutting speeds of individual tools. A challenge for using computationally expensive CAM software, important for real world machining, is the time cost of evaluations. An asynchronous parallel evolutionary optimisation system is presented that can provide a significant speed up, even in the presence of heterogeneous evaluation times produced by variable length tool sequences. This system uses a distributed network of processors that could be easily and inexpensively implemented on existing commercial hardware, and accessible to even small workshops

Heuristics and metaheuristics in the design of sound-absorbing porous materials

Inexact optimisation techniques such as heuristics and metaheuristics that quickly find near-optimal solutions are widely used to solve hard problems. While metaheuristics are well studied on specific problem domains such as travelling salesman, timetabling, vehicle routing etc., their extension to engineering domains is largely unexplored due to the requirement of domain expertise. In this thesis, we address a specific engineering domain: the design of sound-absorbing porous materials. Porous materials are foams, fibrous materials, woven and non-woven textiles, etc., that are widely used in automotive, aerospace and household applications to isolate and absorb noise to prevent equipment damage, protect hearing or ensure comfort. These materials constitute a significant amount of dead weight in aircraft and space applications, and choosing sub-optimal designs would lead to inefficiency and increased costs. By carefully choosing the material properties and shapes of these materials, favourable resonances can be created making it possible to improve absorption while also reducing weight. The optimisation problem structure is yet to be well-explored and not many comparison studies are available in this domain. This thesis aims to address the knowledge gap by analysing the performance of existing and novel heuristic and metaheuristic methods. Initially, the problem structure is explored by considering a one-dimensional layered sound package problem. Then, the challenging two-dimensional foam shape and topology optimisation is addressed. Topology optimisation involves optimally distributing a given volume of material in a design region such that a performance measure is maximised. Although extensive studies exist for the compliance minimisation problem domain, studies and comparisons on porous material problems are relatively rare. Firstly, a single objective absorption maximisation problem with a constraint on the weight is considered. Then a multi-objective problem of simultaneously maximising absorption and minimising weight is considered. The unique nature of the topology optimisation problem allows it to be solved using combinatorial or continuous, gradient or non-gradient methods. In this work, several optimisation methods are studied, including solid isotropic material with penalisation (SIMP), hill climbing, constructive heuristics, genetic algorithms, tabu search, co-variance matrix adaptation evolution strategy (CMA-ES), differential evolution, non-dominated sorting genetic algorithm (NSGA-II) and hybrid strategies. These approaches are tested on a benchmark of seven acoustics problem instances. The results are used to extract domain-specific insights. The findings highlight that the problem domain is rich with unique varieties of solutions, and by using domain-specific insights, one can design hybrid gradient and non-gradient methods that consistently outperform state-of-the-art ones

Two-Objective Design of Benchmark Problems of a Water Distribution System via MOEAs: Towards the Best-Known Approximation of the True Pareto Front

Copyright © 2015 American Society of Civil EngineersVarious multiobjective evolutionary algorithms (MOEAs) have been applied to solve the optimal design problems of a water distribution system (WDS). Such methods are able to find the near-optimal trade-off between cost and performance benefit in a single run. Previously published work used a number of small benchmark networks and/or a few large, real-world networks to test MOEAs on design problems of WDS. A few studies also focused on the comparison of different MOEAs given a limited computational budget. However, no consistent attempt has been made before to investigate and report the best-known approximation of the true Pareto front (PF) for a set of benchmark problems, and thus there is not a single point of reference. This paper applied 5 state-of-the-art MOEAs, with minimum time invested in parameterization (i.e., using the recommended settings), to 12 design problems collected from the literature. Three different population sizes were implemented for each MOEA with respect to the scale of each problem. The true PFs for small problems and the best-known PFs for the other problems were obtained. Five MOEAs were complementary to each other on various problems, which implies that no one method was completely superior to the others. The nondominated sorting genetic algorithm-II (NSGA-II), with minimum parameters tuning, remains a good choice as it showed generally the best achievements across all the problems. In addition, a small population size can be used for small and medium problems (in terms of the number of decision variables). However, for intermediate and large problems, different sizes and random seeds are recommended to ensure a wider PF. The publicly available best-known PFs obtained from this work are a good starting point for researchers to test new algorithms and methodologies for WDS analysis

Heuristics and metaheuristics in the design of sound-absorbing porous materials

Inexact optimisation techniques such as heuristics and metaheuristics that quickly find near-optimal solutions are widely used to solve hard problems. While metaheuristics are well studied on specific problem domains such as travelling salesman, timetabling, vehicle routing etc., their extension to engineering domains is largely unexplored due to the requirement of domain expertise. In this thesis, we address a specific engineering domain: the design of sound-absorbing porous materials. Porous materials are foams, fibrous materials, woven and non-woven textiles, etc., that are widely used in automotive, aerospace and household applications to isolate and absorb noise to prevent equipment damage, protect hearing or ensure comfort. These materials constitute a significant amount of dead weight in aircraft and space applications, and choosing sub-optimal designs would lead to inefficiency and increased costs. By carefully choosing the material properties and shapes of these materials, favourable resonances can be created making it possible to improve absorption while also reducing weight. The optimisation problem structure is yet to be well-explored and not many comparison studies are available in this domain. This thesis aims to address the knowledge gap by analysing the performance of existing and novel heuristic and metaheuristic methods. Initially, the problem structure is explored by considering a one-dimensional layered sound package problem. Then, the challenging two-dimensional foam shape and topology optimisation is addressed. Topology optimisation involves optimally distributing a given volume of material in a design region such that a performance measure is maximised. Although extensive studies exist for the compliance minimisation problem domain, studies and comparisons on porous material problems are relatively rare. Firstly, a single objective absorption maximisation problem with a constraint on the weight is considered. Then a multi-objective problem of simultaneously maximising absorption and minimising weight is considered. The unique nature of the topology optimisation problem allows it to be solved using combinatorial or continuous, gradient or non-gradient methods. In this work, several optimisation methods are studied, including solid isotropic material with penalisation (SIMP), hill climbing, constructive heuristics, genetic algorithms, tabu search, co-variance matrix adaptation evolution strategy (CMA-ES), differential evolution, non-dominated sorting genetic algorithm (NSGA-II) and hybrid strategies. These approaches are tested on a benchmark of seven acoustics problem instances. The results are used to extract domain-specific insights. The findings highlight that the problem domain is rich with unique varieties of solutions, and by using domain-specific insights, one can design hybrid gradient and non-gradient methods that consistently outperform state-of-the-art ones

Rough-Cut Capacity Planning in Multimodal Freight Transportation Networks

A main challenge in transporting cargo for United States Transportation Command (USTRANSCOM) is in mode selection or integration. Demand for cargo is time sensitive and must be fulfilled by an established due date. Since these due dates are often inflexible, commercial carriers are used at an enormous expense, in order to fill the gap in organic transportation asset capacity. This dissertation develops a new methodology for transportation capacity assignment to routes based on the Resource Constrained Shortest Path Problem (RCSP). Routes can be single or multimodal depending on the characteristics of the network, delivery timeline, modal capacities, and costs. The difficulty of the RCSP requires use of metaheuristics to produce solutions. An Ant Colony System to solve the RCSP is developed in this dissertation. Finally, a method for generating near Pareto optimal solutions with respect to the objectives of cost and time is developed

Channel parameter tuning in a hybrid Wi-Fi-Dynamic Spectrum Access Wireless Mesh Network

This work addresses Channel Assignment in a multi-radio multi-channel (MRMC) Wireless Mesh Network (WMN) using both Wi-Fi and Dynamic Spectrum Access (DSA) spectrum bands and standards. This scenario poses new challenges because nodes are spread out geographically so may have differing allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) avoid or minimise interference within the network and from external interference sources, and 2) maintain connectivity within the network. These two requirements must be met while staying within the link constraints and the radio interface constraints, such as only assigning as many channels to a node as it has radios. This work's original contribution to the field is a unified framework for channel optimisation and assignment in a WMN that uses both DSA and traditional Wi-Fi channels for interconnectivity. This contribution is realised by providing and analysing the performance of near-optimal Channel Assignment (CA) solutions using metaheuristic algorithms for the MRMC WMNs using DSA bands. We have created a simulation framework for evaluating the algorithms. The performance of Simulated Annealing, Genetic Algorithm, Differential Evolution, and Particle Swarm Optimisation algorithms have been analysed and compared for the CA optimisation problem. We introduce a novel algorithm, used alongside the metaheuristic optimisation algorithms, to generate feasible candidate CA solutions. Unlike previous studies, this sensing and CA work takes into account the requirement to use a Geolocation Spectrum Database (GLSD) to get the allowed channels, in addition to using spectrum sensing to identify and estimate the cumulative severity of both internal and external interference sources. External interference may be caused by other secondary users (SUs) in the vicinity or by primary transmitters of the DSA band whose emissions leak into adjacent channels, next-toadjacent, or even into further channels. We use signal-to-interference-plus-noise ratio (SINR) as the optimisation objective. This incorporates any possible source or type of interference and makes our method agnostic to the protocol or technology of the interfering devices while ensuring that the received signal level is high enough for connectivity to be maintained on as many links as possible. To support our assertion that SINR is a reasonable criterion on which to base the optimisation, we have carried out extensive outdoor measurements in both line-of-sight and wooded conditions in the television white space (TVWS) DSA band and the 5 GHz Wi-Fi band. These measurements show that SINR is useful as a performance measure, especially when the interference experienced on a link is high. Our statistical analysis shows that SINR effectively differentiates the performance of different channels and that SINR is well correlated with throughput and is thus a good predictor of end-user experience, despite varying conditions. We also identify and analyse the idle times created by Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) contention-based Medium Access Control (MAC) operations and propose the use of these idle times for spectrum sensing to measure the SINR on possible channels. This means we can perform spectrum sensing with zero spectrum sensing delay experienced by the end user. Unlike previous work, this spectrum sensing is transparent and can be performed without causing any disruption to the normal data transmission of the network. We conduct Markov chain analysis to find the expected length of time of a sensing window. We also derive an efficient minimum variance unbiased estimator of the interference plus noise and show how the SINR can be found using this estimate. Our estimation is more granular, accurate, and appropriate to the problem of Secondary User (SU)-SU coexistence than the binary hypothesis testing methods that are most common in the literature. Furthermore, we construct confidence intervals based on the probability density function derived for the observations. This leads to finding and showing the relationships between the number of sampling windows and sampling time, the interference power, and the achievable confidence interval width. While our results coincide with (and thus are confirmed by) some key previous recommendations, ours are more precise, granular, and accurate and allow for application to a wider range of operating conditions. Finally, we present alterations to the IEEE 802.11k protocol to enable the reporting of spectrum sensing results to the fusion or gateway node and algorithms for distributing the Channel Assignment once computed. We analyse the convergence rate of the proposed procedures and find that high network availability can be maintained despite the temporary loss of connectivity caused by the channel switching procedure. This dissertation consolidates the different activities required to improve the channel parameter settings of a multi-radio multi-channel DSA-WMN. The work facilitates the extension of Internet connectivity to the unconnected or unreliably connected in rural or peri-urban areas in a more cost-effective way, enabling more meaningful and affordable access technologies. It also empowers smaller players to construct better community networks for sharing local content. This technology can have knock-on effects of improved socio-economic conditions for the communities that use it