On thermal sensor calibration and software techniques for many-core thermal management

The high power density of a many-core processor results in increased temperature which negatively impacts system reliability and performance. Dynamic thermal management applies thermal-aware techniques at run time to avoid overheating using temperature information collected from on-chip thermal sensors. Temperature sensing and thermal control schemes are two critical technologies for successfully maintaining thermal safety. In this dissertation, on-line thermal sensor calibration schemes are developed to provide accurate temperature information. Software-based dynamic thermal management techniques are proposed using calibrated thermal sensors. Due to process variation and silicon aging, on-chip thermal sensors require periodic calibration before use in DTM. However, the calibration cost for thermal sensors can be prohibitively high as the number of on-chip sensors increases. Linear models which are suitable for on-line calculation are employed to estimate temperatures at multiple sensor locations using performance counters. The estimated temperature and the actual sensor thermal profile show a very high similarity with correlation coefficient ~0.9 for SPLASH2 and SPEC2000 benchmarks. A calibration approach is proposed to combine potentially inaccurate temperature values obtained from two sources: thermal sensor readings and temperature estimations. A data fusion strategy based on Bayesian inference, which combines information from these two sources, is demonstrated. The result shows the strategy can effectively recalibrate sensor readings in response to inaccuracies caused by process variation and environmental noise. The average absolute error of the corrected sensor temperature readings is A dynamic task allocation strategy is proposed to address localized overheating in many-core systems. Our approach employs reinforcement learning, a dynamic machine learning algorithm that performs task allocation based on current temperatures and a prediction regarding which assignment will minimize the peak temperature. Our results show that the proposed technique is fast (scheduling performed in \u3c1 \u3ems) and can efficiently reduce peak temperature by up to 8 degree C in a 49-core processor (6% on average) versus a leading competing task allocation approach for a series of SPLASH-2 benchmarks. Reinforcement learning has also been applied to 3D integrated circuits to allocate tasks with thermal awareness

Computational Methods for Discrete Conic Optimization Problems

This thesis addresses computational aspects of discrete conic optimization. Westudy two well-known classes of optimization problems closely related to mixedinteger linear optimization problems. The case of mixed integer second-ordercone optimization problems (MISOCP) is a generalization in which therequirement that solutions be in the non-negative orthant is replaced by arequirement that they be in a second-order cone. Inverse MILP, on the otherhand, is the problem of determining the objective function that makes a givensolution to a given MILP optimal.Although these classes seem unrelated on the surface, the proposedsolution methodology for both classes involves outer approximation of a conicfeasible region by linear inequalities. In both cases, an iterative algorithmin which a separation problem is solved to generate the approximation isemployed. From a complexity standpoint, both MISOCP and inverse MILP areNP--hard. As in the case of MILPs, the usual decision version ofMISOCP is NP-complete, whereas in contrast to MILP, we provide the firstproof that a certain decision version of inverse MILP is rathercoNP-complete.With respect to MISOCP, we first introduce a basic outer approximationalgorithm to solve SOCPs based on a cutting-plane approach. As expected, theperformance of our implementation of such an algorithm is shown to lag behindthe well-known interior point method. Despite this, such a cutting-planeapproach does have promise as a method of producing bounds when embedded withina state-of-the-art branch-and-cut implementation due to its superior ability towarm-start the bound computation after imposing branching constraints. Ourouter-approximation-based branch-and-cut algorithm relaxes both integrality andconic constraints to obtain a linear relaxation. This linear relaxation isstrengthened by the addition of valid inequalities obtained by separatinginfeasible points. Valid inequalities may be obtained by separation from theconvex hull of integer solution lying within the relaxed feasible region or byseparation from the feasible region described by the (relaxed) conicconstraints. Solutions are stored when both integer and conic feasibility isachieved. We review the literature on cutting-plane procedures for MISOCP andmixed integer convex optimization problems.With respect to inverse MILP, we formulate this problem as a conicproblem and derive a cutting-plane algorithm for it. The separation problem inthis algorithm is a modified version of the original MILP. We show that thereis a close relationship between this algorithm and a similar iterativealgorithm for separating infeasible points from the convex hull of solutions tothe original MILP that forms part of the basis for the well-known result ofGrotschel-Lovasz-Schrijver that demonstrates the complexity-wiseequivalence of separation and optimization.In order to test our ideas, we implement a number of software librariesthat together constitute DisCO, a full-featured solver for MISOCP. Thefirst of the supporting libraries is OsiConic, an abstract base classin C++ for interfacing to SOCP solvers. We provide interfaces using thislibrary for widely used commercial and open source SOCP/nonlinear problemsolvers. We also introduce CglConic, a library that implements cuttingprocedures for MISOCP feasible set. We perform extensive computationalexperiments with DisCO comparing a wide range of variants of our proposedalgorithm, as well as other approaches. As DisCO is built on top of a libraryfor distributed parallel tree search algorithms, we also perform experimentsshowing that our algorithm is effective and scalable when parallelized

Ambulance routing problems with rich constraints and multiple objectives

Humanitäre non-profit Organisationen im Bereich des Patiententransports sehen sich dazu verpflichtet alle möglichen Einsparungs- und Optimierungspotentiale auszuloten um ihre Ausgaben zu reduzieren. Im Gegensatz zu Notfalleinsatzfahrten, bei denen ein Zusammenlegen mehrerer Transportaufträge normalerweise nicht möglich ist, besteht bei regulären Patiententransporten durchaus Einsparungspotential. Diese Tatsache gibt Anlass zur wissenschaftlichen Analyse jener Problemstellung, welche die täglich notwendige Planung regulärer Patiententransportaufträge umfasst. Solche Aufgabenstellungen werden als Dial-A-Ride-Probleme modelliert. Eine angemessene Service-Qualität kann entweder durch entsprechende Nebenbedingungen gewährleistet oder durch eine zusätzliche Zielfunktion minimiert werden. Beide Herangehensweisen werden hier untersucht. Zuerst wird eine vereinfachte Problemstellung aus der Literatur behandelt und ein kompetitives heuristisches Lösungsverfahren entwickelt. Diese vereinfachte Problemstellung wird in zwei Richtungen erweitert. Einerseits wird, zusätzlich zur Minimierung der Gesamtkosten, eine zweite benutzerorientierte Zielfunktion eingeführt. Andererseits werden eine heterogene Fahrzeugflotte und unterschiedliche Patiententypen in die Standardproblemstellung integriert. Letztendlich wird das reale Patiententransportproblem, basierend auf Informationen des Roten Kreuzes, definiert und gelöst. Neben heterogenen Fahrzeugen und unterschiedlichen Patienten, werden nun auch die Zuordnung von Fahrern und sonstigem Personal zu den verschiedenen Fahrzeugen, Mittagspausen und weitere Aufenthalte am Depot berücksichtigt. Alle eingesetzten exakten Methoden, obwohl sie auf neuesten Erkenntnissen aus der Literatur aufbauen, können Instanzen von realistischer Größe nicht lösen. Dieser Umstand macht die Entwicklung von passenden heuristischen Verfahren nach wie vor unumgänglich. In der vorliegenden Arbeit wird ein relativ generisches System basierend auf der Variable Neighborhood Search Idee entwickelt, das auf alle behandelten Einzielproblemversionen angewandt werden kann; auch für die bi-kriterielle Problemstellung, in Kombination mit Path Relinking, werden gute Ergebnisse erzielt.Humanitarian non-profit ambulance dispatching organizations are committed to look at cost reduction potentials in order to decrease their expenses. While in the context of emergency transportation cost reduction cannot be achieved by means of combined passenger routes, this can be done when dealing with regular patients. This research work is motivated by the problem situation faced by ambulance dispatchers in the field of patient transportation. Problems of this kind are modeled as dial-a-ride problems. In the field of patient transportation, the provision of a certain quality of service is necessary; the term “user inconvenience” is used in this context. User inconvenience can either be considered in terms of additional constraints or in terms of additional objectives. Both approaches are investigated in this book. The aim is to model and solve the real world problem based on available information from the Austrian Red Cross. In a first step, a competitive heuristic solution method for a simplified problem version is developed. This problem version is extended in two ways. On the one hand, besides routing costs, a user-oriented objective, minimizing user inconvenience, in terms of mean user ride time, is introduced. On the other hand, heterogeneous patient types and a heterogeneous vehicle fleet are integrated into the standard dial-a-ride model. In a final step, in addition to heterogeneous patients and vehicles, the assignment of drivers and other staff members to vehicles, the scheduling of lunch breaks, and additional stops at the depot are considered. All exact methods employed, although based on state of the art concepts, are not capable of solving instances of realistic size. This fact makes the development of according heuristic solution methods necessary. In this book a rather generic variable neighborhood search framework is proposed. It is able to accommodate all single objective problem versions and also proves to work well when applied to the bi-objective problem in combination with path relinking