10,701 research outputs found
Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems
This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems
Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions
Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units.
The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures.
The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system.
Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter.
The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation
Implicit Loss of Surjectivity and Facial Reduction: Theory and Applications
Facial reduction, pioneered by Borwein and Wolkowicz, is a preprocessing method that is commonly used to obtain strict feasibility in the reformulated, reduced constraint system.
The importance of strict feasibility is often addressed in the context of the convergence results for interior point methods.
Beyond the theoretical properties that the facial reduction conveys, we show that facial reduction, not only limited to interior point methods, leads to strong numerical performances in different classes of algorithms.
In this thesis we study various consequences and the broad applicability of facial reduction.
The thesis is organized in two parts.
In the first part, we show the instabilities accompanied by the absence
of strict feasibility through the lens of facially reduced systems.
In particular, we exploit the implicit redundancies, revealed by each nontrivial facial reduction step, resulting in the implicit loss of surjectivity.
This leads to the two-step facial reduction and two novel related notions of singularity.
For the area of semidefinite programming, we use these singularities to strengthen a known bound on the solution rank, the Barvinok-Pataki bound.
For the area of linear programming, we reveal degeneracies caused by the implicit redundancies.
Furthermore, we propose a preprocessing tool that uses the simplex method.
In the second part of this thesis, we continue with the semidefinite programs that do not have strictly feasible points.
We focus on the doubly-nonnegative relaxation of the binary quadratic program and a semidefinite program with a nonlinear objective function.
We closely work with two classes of algorithms, the splitting method and the Gauss-Newton interior point method.
We elaborate on the advantages in building models from facial reduction. Moreover, we develop algorithms for real-world problems including the quadratic assignment problem, the protein side-chain positioning problem, and the key rate computation for quantum key distribution.
Facial reduction continues to play an important role for
providing robust reformulated models in both the theoretical and the practical aspects, resulting in successful numerical performances
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
A Low-Delay MAC for IoT Applications: Decentralized Optimal Scheduling of Queues without Explicit State Information Sharing
We consider a system of several collocated nodes sharing a time slotted
wireless channel, and seek a MAC (medium access control) that (i) provides low
mean delay, (ii) has distributed control (i.e., there is no central scheduler),
and (iii) does not require explicit exchange of state information or control
signals. The design of such MAC protocols must keep in mind the need for
contention access at light traffic, and scheduled access in heavy traffic,
leading to the long-standing interest in hybrid, adaptive MACs.
Working in the discrete time setting, for the distributed MAC design, we
consider a practical information structure where each node has local
information and some common information obtained from overhearing. In this
setting, "ZMAC" is an existing protocol that is hybrid and adaptive. We
approach the problem via two steps (1) We show that it is sufficient for the
policy to be "greedy" and "exhaustive". Limiting the policy to this class
reduces the problem to obtaining a queue switching policy at queue emptiness
instants. (2) Formulating the delay optimal scheduling as a POMDP (partially
observed Markov decision process), we show that the optimal switching rule is
Stochastic Largest Queue (SLQ).
Using this theory as the basis, we then develop a practical distributed
scheduler, QZMAC, which is also tunable. We implement QZMAC on standard
off-the-shelf TelosB motes and also use simulations to compare QZMAC with the
full-knowledge centralized scheduler, and with ZMAC. We use our implementation
to study the impact of false detection while overhearing the common
information, and the efficiency of QZMAC. Our simulation results show that the
mean delay with QZMAC is close that of the full-knowledge centralized
scheduler.Comment: 28 pages, 19 figure
A Methodology to Enable Concurrent Trade Space Exploration of Space Campaigns and Transportation Systems
Space exploration campaigns detail the ways and means to achieve goals for our human spaceflight programs. Significant strategic, financial, and programmatic investments over long timescales are required to execute them, and therefore must be justified to decision makers. To make an informed down-selection, many alternative campaign designs are presented at the conceptual-level, as a set and sequence of individual missions to perform that meets the goals and constraints of the campaign, either technical or programmatic. Each mission is executed by in-space transportation systems, which deliver either crew or cargo payloads to various destinations. Design of each of these transportation systems is highly dependent on campaign goals and even small changes in subsystem design parameters can prompt significant changes in the overall campaign strategy. However, the current state of the art describes campaign and vehicle design processes that are generally performed independently, which limits the ability to assess these sensitive impacts. The objective of this research is to establish a methodology for space exploration campaign design that represents transportation systems as a collection of subsystems and integrates its design process to enable concurrent trade space exploration. More specifically, the goal is to identify existing campaign and vehicle design processes to use as a foundation for improvement and eventual integration.
In the past two decades, researchers have adopted terrestrial logistics and supply chain optimization processes to the space campaign design problem by accounting for the challenges that accompany space travel. Fundamentally, a space campaign is formulated as a network design problem where destinations, such as orbits or surfaces of planetary bodies, are represented as nodes with the routes between them as arcs. The objective of this design problem is to optimize the flow of commodities within network using available transport systems. Given the dynamic nature and the number of commodities involved, each campaign can be modeled as a time-expanded, generalized multi-commodity network flow and solved using a mixed integer programming algorithm. To address the challenge of modeling complex concept of operations (ConOps), this formulation was extended to include paths as a set of arcs, further enabling the inclusion of vehicle stacks and payload transfers in the campaign optimization process. Further, with the focus of transportation system within this research, the typical fixed orbital nodes in the logistics network are modified to represent ranges of orbits, categorized by their characteristic energy. This enables the vehicle design process to vary each orbit in the mission as it desires to find the best one per vehicle.
By extension, once integrated, arc costs of dV and dT are updated each iteration. Once campaign goals and external constraints are included, the formulated campaign design process generates alternatives at the conceptual level, where each one identifies the optimal set and sequence of missions to perform.
Representing transportation systems as a collection of subsystems introduces challenges in the design of each vehicle, with a high degree of coupling between each subsystem as well as the driving mission. Additionally, sizing of each subsystem can have many inputs and outputs linked across the system, resulting in a complex, multi-disciplinary analysis, and optimization problem. By leveraging the ontology within the Dynamic Rocket Equation Tool, DYREQT, this problem can be solved rapidly by defining each system as a hierarchy of elements and subelements, the latter corresponding to external subsystem-level sizing models. DYREQT also enables the construction of individual missions as a series of events, which can be directly driven and generated by the mission set found by the campaign optimization process. This process produces sized vehicles iteratively by using the mission input, subsystem level sizing models, and the ideal rocket equation.
By conducting a literature review of campaign and vehicle design processes, the different pieces of the overall methodology are identified, but not the structure. The specific iterative solver, the corresponding convergence criteria, and initialization scheme are the primary areas for experimentation of this thesis. Using NASA’s reference 3-element Human Landing System campaign, the results of these experiments show that the methodology performs best with the vehicle sizing and synthesis process initializing and a path guess that minimizes dV. Further, a converged solution is found faster using non-linear Gauss Seidel fixed point iteration over Jacobi and set of convergence criteria that covers vehicle masses and mission data.
To show improvement over the state of the art, and how it enables concurrent trade studies, this methodology is used at scale in a demonstration using NASA’s Design Reference Architecture 5.0. The LH2 Nuclear Thermal Propulsion (NTP) option is traded with NH3and H2O at the vehicle-level as a way to show the impacts of alternative propellants on the vehicle sizing and campaign strategy. Martian surface stay duration is traded at the campaign-level through two options: long-stay and short-stay. The methodology was able to produce four alternative campaigns over the course of two weeks, which provided data about the launch and aggregation strategy, mission profiles, high-level figures of merit, and subsystem-level vehicle sizes for each alternative. Expectedly, with their lower specific impulses, alternative NTP propellants showed significant growth in the overall mass required to execute each campaign, subsequently represented the number of drop tanks and launches. Further, the short-stay campaign option showed a similar overall mass required compared to its long-stay counterpart, but higher overall costs even given the fewer elements required. Both trade studies supported the overall hypothesis and that integrating the campaign and vehicle design processes addresses the coupling between then and directly shows the impacts of their sensitivities on each other. As a result, the research objective was fulfilled by producing a methodology that was able to address the key gaps identified in the current state of the art.Ph.D
Innovation in Energy Security and Long-Term Energy Efficiency â…¡
The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
A Linear Programming Model for Renewable Energy Aware Discrete Production Planning and Control
Industrial production in the EU, like other sectors of the economy, is obliged to stop producing greenhouse gas emissions by 2050. With its Green Deal, the European Union has already set the corresponding framework in 2019. To achieve Net Zero in the remaining time, while not endangering one's own competitiveness on a globalized market, a transformation of industrial value creation has to be started already today. In terms of energy supply, this means a comprehensive electrification of processes and a switch to fully renewable power generation. However, due to a growing share of renewable energy sources, increasing volatility can be observed in the European electricity market already. For companies, there are mainly two ways to deal with the accompanying increase in average electricity prices. The first is to reduce consumption by increasing efficiency, which naturally has its physical limits. Secondly, an increasing volatile electricity price makes it possible to take advantage of periods of relatively low prices. To do this, companies must identify their energy-intensive processes and design them in such a way as to enable these activities to be shifted in time. This article explains the necessary differentiation between labor-intensive and energy intensive processes. A general mathematical model for the holistic optimization of discrete industrial production is presented. With the help of this MILP model, it is simulated that a flexibilization of energy intensive processes with volatile energy prices can help to reduce costs and thus secure competitiveness while getting it in line with European climate goals. On the basis of real electricity market data, different production scenarios are compared, and it is investigated under which conditions the flexibilization of specific processes is worthwhile
Flashpoint: A Low-latency Serverless Platform for Deep Learning Inference Serving
Recent breakthroughs in Deep Learning (DL) have led to high demand for executing inferences in interactive services such as ChatGPT and GitHub Copilot. However, these interactive services require low-latency inferences, which can only be met with GPUs and result in exorbitant operating costs. For instance, ChatGPT reportedly requires millions of U.S. dollars in cloud GPUs to serve its 1+ million users. A potential solution to meet low-latency requirements with acceptable costs is to use serverless platforms. These platforms automatically scale resources to meet user demands. However, current serverless systems have long cold starts which worsen with larger DL models and lead to poor performance during bursts of requests. Meanwhile, the demand for larger and larger DL models make it more challenging to deliver an acceptable user experience cost-effectively. While current systems over-provision GPUs to address this issue, they incur high costs in idle resources which greatly reduces the benefit of using a serverless platform.
In this thesis, we introduce Flashpoint, a GPU-based serverless platform that serves DL inferences with low latencies. Flashpoint achieves this by reducing cold start durations, especially for large DL models, making serverless computing feasible for latency-sensitive DL workloads. To reduce cold start durations, Flashpoint reduces download times by sourcing the DL model data from within the compute cluster rather than slow cloud storage. Additionally, Flashpoint minimizes in-cluster network congestion from redundant packet transfers of the same DL model to multiple machines with multicasting. Finally, Flashpoint also reduces cold start durations by automatically partitioning models and deploying them in parallel on multiple machines. The reduced cold start durations achieved by Flashpoint enable the platform to scale resource allocations elastically and complete requests with low latencies without over-provisioning expensive GPU resources.
We perform large-scale data center simulations that were parameterized with measurements our prototype implementations. We evaluate the system using six state-of-the-art DL models ranging from 499 MB to 11 GB in size. We also measure the performance of the system in representative real-world traces from Twitter and Microsoft Azure. Our results in the full-scale simulations show that Flashpoint achieves an arithmetic mean of 93.51% shorter average cold start durations, leading to 75.42% and 66.90% respective reductions in average and 99th percentile end-to-end request latencies across the DL models with the same amount of resources. These results show that Flashpoint boosts the performance of serving DL inferences on a serverless platform without increasing costs
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