Mechanical Alloying is a manufacturing process that produces alloys by cold welding of powders. Usually, a vial containing both the powder and steel balls is agitated. Due to impact between the balls and balls and the vial, the powder is mechanically deformed, crushed, and mixed at nano-scales. In this thesis, a numerical model is developed to simulate the dynamics of the vial and the grinding balls of the SPEX 8000 ball milling device, a standardized equipment in both industrial and academic investigations of ball milling. The numerical model is based on the Event Driven Method, typically used to model granular flows. The method implemented is more efficient than the discrete element method used previously to study ball milling dynamics. The numerical tool obtained is useful for scale-up and optimization of mechanical alloying of various materials. An optimization study is presented for the SPEX 8000.

Mechanical Alloying is a manufacturing process that produces alloys by cold welding of powders. Usually, a vial containing both the powder and steel balls is agitated. Due to impact between the balls and balls and the vial, the powder is mechanically deformed, crushed, and mixed at nano-scales. In this thesis, a numerical model is developed to simulate the dynamics of the vial and the grinding balls of the SPEX 8000 ball milling device, a standardized equipment in both industrial and academic investigations of ball milling. The numerical model is based on the Event Driven Method, typically used to model granular flows. The method implemented is more efficient than the discrete element method used previously to study ball milling dynamics. The numerical tool obtained is useful for scale-up and optimization of mechanical alloying of various materials. An optimization study is presented for the SPEX 8000.

Mechanical Alloying is a manufacturing process that produces alloys by cold welding of powders. Usually, a vial containing both the powder and steel balls is agitated. Due to impact between the balls and balls and the vial, the powder is mechanically deformed, crushed, and mixed at nano-scales. In this thesis, a numerical model is developed to simulate the dynamics of the vial and the grinding balls of the SPEX 8000 ball milling device, a standardized equipment in both industrial and academic investigations of ball milling. The numerical model is based on the Event Driven Method, typically used to model granular flows. The method implemented is more efficient than the discrete element method used previously to study ball milling dynamics. The numerical tool obtained is useful for scale-up and optimization of mechanical alloying of various materials. An optimization study is presented for the SPEX 8000.

Mechanical Alloying is a manufacturing process that produces alloys by cold welding of powders. Usually, a vial containing both the powder and steel balls is agitated. Due to impact between the balls and balls and the vial, the powder is mechanically deformed, crushed, and mixed at nano-scales. In this thesis, a numerical model is developed to simulate the dynamics of the vial and the grinding balls of the SPEX 8000 ball milling device, a standardized equipment in both industrial and academic investigations of ball milling. The numerical model is based on the Event Driven Method, typically used to model granular flows. The method implemented is more efficient than the discrete element method used previously to study ball milling dynamics. The numerical tool obtained is useful for scale-up and optimization of mechanical alloying of various materials. An optimization study is presented for the SPEX 8000.

Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Dedicated hardware may thus be more suitable for executing them. Given that there is no clear consensus on the model of computation in the brain, model flexibility is at least as important a characteristic of neural hardware as is performance acceleration. The SpiNNaker chip is an example of the emerging 'neuromimetic' architecture, a universal platform that specialises the hardware for neural networks but allows flexibility in model choice. It integrates four key attributes: native parallelism, event-driven processing, incoherent memory and incremental reconfiguration, in a system combining an array of general-purpose processors with a configurable asynchronous interconnect. Making such a device usable in practice requires an environment for instantiating neural models on the chip that allows the user to focus on model characteristics rather than on hardware details. The central part of this system is a library of predesigned, 'drop-in' event-driven neural components that specify their specific implementation on SpiNNaker. Three exemplar models: two spiking networks and a multilayer perceptron network, illustrate techniques that provide a basis for the library and demonstrate a reference methodology that can be extended to support third-party library components not only on SpiNNaker but on any configurable neuromimetic platform. Experiments demonstrate the capability of the library model to implement efficient on-chip neural networks, but also reveal important hardware limitations, particularly with respect to communications, that require careful design. The ultimate goal is the creation of a library-based development system that allows neural modellers to work in the high-level environment of their choice, using an automated tool chain to create the appropriate SpiNNaker instantiation. Such a system would enable the use of the hardware to explore abstractions of biological neurodynamics that underpin a functional model of neural computation.

In persistent monitoring tasks, cooperating mobile agents are used to monitor a dynamically changing environment that cannot be fully covered by a stationary team of agents. The exploration process leads to the discovery of various "points of interest" (targets) to be perpetually monitored. Through an optimal control approach, the first part of this dissertation shows that in a one-dimensional mission space the solution can be reduced to a simpler parametric problem. The behavior of agents under optimal control is described by a hybrid system which can be analyzed using Infinitesimal Perturbation Analysis (IPA) to obtain an on-line solution. IPA allows the modeling of virtually arbitrary stochastic effects in target uncertainty and its event-driven nature renders the solution scalable in the number of events rather than the state space. The second part of this work extends the results of the one-dimensional persistent monitoring problem to a two-dimensional space with constrained agent mobility. Under a general graph setting, the properties of the one-dimensional optimal control solution are largely inherited. The solution involves the design of agent trajectories defined by both the sequence of nodes to be visited and the amount of time spent at each node. A class of distributed threshold-based parametric controllers is proposed to reduce the computational complexity. These parameters are optimized through an event-driven IPA gradient-based algorithm and yield optimal controllers within this family of threshold-based policies. The performance of the threshold-based parametric controller is close to that of the optimal controller derived through dynamic programming and its computational complexity is smaller by orders of magnitude. Although effective, the aforementioned optimal controls are established on the assumption that agents are all connected via a centralized controller which is energy-consuming and unreliable in adversarial environments. The third part of this work extends the previous controls by developing decentralized controllers which distribute functionality to the agents so that each one acts upon local information and sparse communication with neighbors. The complexity of decentralization for persistent monitoring problems is significant given agent mobility and the overall time-varying graph topology. Conditions are identified and a decentralized framework is proposed under which the centralized solution can be exactly recovered in a decentralized event-driven manner based on local information -- except for one event requiring communication from a non-neighbor agent.

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Microservices is one of the most commonly used buzzword of the systems architecture industry and is being adopted by several of the world’s largest technology companies such as Netflix, Uber and Amazon. The architecture which embraces splitting up your system in smaller independent units is an extension of the service-oriented architecture and an opponent of the monolithic architecture. Being a top buzzword and promises of extreme scalability has spiked the interest for microservices, but unlike the relatively simple monolithic architecture the complexity of microservices creates a new set of obstacles. This work sheds a light on these issues and implements solutions for some of the most frequent problems using a case study. The study shows that while microservices can help reduce the inner complexity of a system, it greatly increases the outer complexity and creates the need for a variety of tools aimed at distributed systems. It also concludes that communication and data storage are two of the most frequently occurring issues when developing microservices with the most difficult one being how you reason with and structure your data, especially for efficient queries across microservices. / Microservices eller så kallade mikrotjänster är ett ofta förekommande buzzword inom systemarkitektur och nyttjas av flera teknikjättar som exempelvis Netflix, Uber och Amazon. Arkitekturen som bygger på att dela upp sina system i mindre oberoende delar är en utbyggnad av den tjänstorienterade arkitekturen och numera motståndare till den klassiska monolitiska arkitekturen. En plats högt upp på trendlistan och lovord om extrem skalbarhet har gjort att intresset för mikrotjänster är enormt, men till skillnad från den relativt simpla monolitiska arkitekturen skapar komplexiteten hos mikrostjänster en rad nya hinder. Det här arbetet belyser dessa hinder och implementerar även lösningar för de vanligaste förekommande problemen med hjälp av en fallstudie. Resultatet visar att även fast en mikrotjänstarkitektur kan minska systemets interna komplexitet så leder det till en markant ökning av systemets yttre komplexitet och det skapas ytterligare behov av en mängd olika verktyg och tjänster designade för distribuerade system. Studien visar också att de två mest förekommande problemen vid utveckling av en mikrotjänstarkitektur är kommunikation och datalagring där hantering och struktur av data är den mest komplicerade och kräver mycket kunskap, speciellt för att skapa effektiva datasökningar som sträcker sig över flera mikrotjänster.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A particular engineering aspect of distributed sensor networks that has not received adequate attention is the system level hardware architecture of the individual nodes of the network. A novel hardware architecture based on an idea of task specific modular computing is proposed to provide for both the high flexibility and low power consumption required for distributed sensing solutions. The power consumption of the architecture is mathematically analyzed against a traditional approach, and guidelines are developed for application scenarios that would benefit from using this new design.

Les machines multicœurs actuelles utilisent une architecture à Accès Mémoire Non-Uniforme (Non-Uniform Memory Access - NUMA). Dans ces machines, les cœurs sont regroupés en nœuds. Chaque nœud possède son propre contrôleur mémoire et est relié aux autres nœuds via des liens d'interconnexion. Utiliser ces architectures à leur pleine capacité est difficile : il faut notamment veiller à éviter les accès distants (i.e., les accès d'un nœud vers un autre nœud) et la congestion sur les bus mémoire et les liens d'interconnexion. L'optimisation de performance sur une machine NUMA peut se faire de deux manières : en implantant des optimisations ad-hoc au sein des applications ou de manière automatique en utilisant des heuristiques. Cependant, les outils existants fournissent trop peu d'informations pour pouvoir implanter efficacement des optimisations et les heuristiques existantes ne permettent pas d'éviter les problèmes de congestion. Cette thèse résout ces deux problèmes. Dans un premier temps nous présentons MemProf, le premier outil d'analyse permettant d'implanter efficacement des optimisations NUMA au sein d'applications. Pour ce faire, MemProf construit des flots d'interactions entre threads et objets. Nous évaluons MemProf sur 3 machines NUMA et montrons que les optimisations trouvées grâce à MemProf permettent d'obtenir des gains de performance significatifs (jusqu'à 2.6x) et sont très simples à implanter (moins de 10 lignes de code). Dans un second temps, nous présentons Carrefour, un algorithme de gestion de la mémoire pour machines NUMA. Contrairement aux heuristiques existantes, Carrefour se concentre sur la réduction de la congestion sur les machines NUMA. Carrefour permet d'obtenir des gains de performance significatifs (jusqu'à 3.3x) et est toujours plus performant que les heuristiques existantes. / Modern multicore systems are based on a Non-Uniform Memory Access (NUMA) design. In a NUMA system, cores are grouped in a set of nodes. Each node has a memory controller and is interconnected with other nodes using high speed interconnect links. Efficiently exploiting such architectures is notoriously complex for programmers. Two key objectives on NUMA multicore machines are to limit as much as possible the number of remote memory accesses (i.e., accesses from a node to another node) and to avoid contention on memory controllers and interconnect links. These objectives can be achieved by implementing application-level optimizations or by implementing application-agnostic heuristics. However, in many cases, existing profilers do not provide enough information to help programmers implement application-level optimizations and existing application-agnostic heuristics fail to address contention issues. The contributions of this thesis are twofold. First we present MemProf, a profiler that allows programmers to choose and implement efficient application-level optimizations for NUMA systems. MemProf builds temporal flows of interactions between threads and objects, which help programmers understand why and which memory objects are accessed remotely. We evaluate MemProf on Linux on three different machines. We show how MemProf helps us choose and implement efficient optimizations, unlike existing profilers. These optimizations provide significant performance gains (up to 2.6x), while requiring very lightweight modifications (10 lines of code or less). Then we present Carrefour, an application-agnostic memory management algorithm. Contrarily to existing heuristics, Carrefour focuses on traffic contention on memory controllers and interconnect links. Carrefour provides significant performance gains (up to 3.3x) and always performs better than existing heuristics.