To improve the accuracy in capturing the motion of deformable objects, a reconfigurable multi-camera active-vision system which can dynamically reposition its cameras online is proposed, and a design for such a system, along with a methodology to select the near-optimal positions and orientations of the set of cameras, is presented. The active-vision system accounts for the deformation of the object-of-interest by tracking triangulated vertices in order to predict the shape of the object at subsequent demand instants. It then selects a system configuration that minimizes the expected error in the recovered position of each of these vertices. Extensive simulations and experiments have verified that using the proposed reconfigurable system to both translate and rotate cameras to near-optimal poses is tangibly superior to using cameras which are either static, or can only rotate, in minimizing the error in recovered vertex positions.

Protein function is the ultimate expression of the genetic code of every organism, and determining which proteins interact helps reveal their functions. MatrixMatchMaker (MMM) is a computational method of predicting protein-protein interactions that works by detecting co-evolution between pairs of proteins. Although MMM has several advanced features compared to other co-evolution-based methods, these come at the cost of high computation, and so the goal of this research is to improve the performance of MMM. First we redefine the computational problem posed by the method, and then develop a new algorithm to solve it, achieving a total speedup of 570x over the existing MMM algorithm for a biologically meaningful data set. We also develop hardware which has not yet succeeded in further improving the performance of MMM, but could serve as a platform that could lead to further gains.

Application performance on modern processors has become increasingly dictated by the use of on-chip structures, such as caches and look-aside buffers. The hierarchical (multi-leveled) design of processor structures, the ubiquity of multicore processor architectures, as well as the increasing relative cost of accessing memory have all contributed to this condition. Our thesis is that operating systems should provide services and mechanisms for applications to more efficiently utilize on-chip processor structures. As such, this dissertation demonstrates how the operating system can improve processor efficiency of applications through specific techniques. Two operating system services are investigated: (1) improving secondary and last-level cache utilization through a run-time cache filtering technique, and (2) improving the processor efficiency of system intensive applications through a new exception-less system call mechanism. With the first mechanism, we introduce the concept of a software pollute buffer and show that it can be used effectively at run-time, with assistance from commodity hardware performance counters, to reduce pollution of secondary on-chip caches. In the second mechanism, we are able to decouple application and operating system execution, showing the benefits of the reduced interference in various processor components such as the first level instruction and data caches, second level caches and branch predictor. We show that exception-less system calls are particularly effective on modern multicore processors. We explore two ways for applications to use exception-less system calls. The first way, which is completely transparent to the application, uses multi-threading to hide asynchronous communication between the operating system kernel and the application. In the second way, we propose that applications can directly use the exception-less system call interface by designing programs that follow an event-driven architecture.

Application performance on modern processors has become increasingly dictated by the use of on-chip structures, such as caches and look-aside buffers. The hierarchical (multi-leveled) design of processor structures, the ubiquity of multicore processor architectures, as well as the increasing relative cost of accessing memory have all contributed to this condition. Our thesis is that operating systems should provide services and mechanisms for applications to more efficiently utilize on-chip processor structures. As such, this dissertation demonstrates how the operating system can improve processor efficiency of applications through specific techniques. Two operating system services are investigated: (1) improving secondary and last-level cache utilization through a run-time cache filtering technique, and (2) improving the processor efficiency of system intensive applications through a new exception-less system call mechanism. With the first mechanism, we introduce the concept of a software pollute buffer and show that it can be used effectively at run-time, with assistance from commodity hardware performance counters, to reduce pollution of secondary on-chip caches. In the second mechanism, we are able to decouple application and operating system execution, showing the benefits of the reduced interference in various processor components such as the first level instruction and data caches, second level caches and branch predictor. We show that exception-less system calls are particularly effective on modern multicore processors. We explore two ways for applications to use exception-less system calls. The first way, which is completely transparent to the application, uses multi-threading to hide asynchronous communication between the operating system kernel and the application. In the second way, we propose that applications can directly use the exception-less system call interface by designing programs that follow an event-driven architecture.

A peer-to-peer (P2P) system is a networked system characterized by the lack of centralized control, in which all or most communication is symmetric. Also, a P2P system is supposed to handle frequent arrivals and departures of nodes, and is expected to scale to very large network sizes. These requirements make the design of P2P systems particularly challenging. We investigate two central issues pertaining to the design of P2P systems: load balancing and routing. In the first part of this thesis, we study the problem of load balancing in the context of Distributed Hash Tables (DHTs). Briefly, a DHT is a giant hash table that is maintained in a P2P fashion: Keys are mapped to a hash space I --- typically the interval [0,1), which is partitioned into blocks among the nodes, and each node stores the keys that are mapped to its block. Based on the position of their blocks in I, the nodes also set up connections among themselves, forming a routing network, which facilitates efficient key location. Typically, in a DHT it is desirable that the nodes' blocks are roughly of equal size, since this usually implies a balanced distribution of the load of storing keys among nodes, and it also simplifies the design of the routing network. We propose and analyze a simple distributed scheme for partitioning I, inspired by the multiple random choices paradigm. This scheme guarantees that, with high probability, the ratio between the largest and smallest blocks remains bounded by a small constant. It is also message efficient, and the arrival or departure of a node perturbs the current partition of I minimally. A unique feature of this scheme is that it tolerates adversarial arrivals and departures of nodes. In the second part of the thesis, we investigate the complexity of a natural decentralized routing protocol, in a broad family of randomized networks. The network family and routing protocol in question are inspired by a framework proposed by Kleinberg to model small-world phenomena in social networks, and they capture many designs that have been proposed for P2P systems. For this model we establish a general lower bound on the expected message complexity of routing, in terms of the average node degree. This lower bound almost matches the corresponding known upper bound.

This thesis is composed of two parts, that relate to both parallel and heterogeneous processing. The first describes DistCL, a distributed OpenCL framework that allows a cluster of GPUs to be programmed like a single device. It uses programmer-supplied meta-functions that associate work-items to memory. DistCL achieves speedups of up to 29x using 32 peers. By comparing DistCL to SnuCL, we determine that the compute-to-transfer ratio of a benchmark is the best predictor of its performance scaling when distributed. The second is a statistical power model for the AMD Fusion heterogeneous processor. We present a systematic methodology to create a representative set of compute micro-benchmarks using data collected from real hardware. The power model is created with data from both micro-benchmarks and application benchmarks. The model showed an average predictive error of 6.9% on heterogeneous workloads. The Multi2Sim heterogeneous simulator was modified to support configurable power modelling.

The Web has become an open decentralized global information / knowledge repository, a platform for distributed computing and global electronic markets, where people are confronted with information of unknown sources, and need to interact with “strangers”. This makes trust and the validity of information in cyberspace arise as crucial issues. This thesis proposes knowledge provenance (KP) as a formal approach to determining the origin and validity of information / knowledge on the Web, by means of modeling and maintaining the information sources, information dependencies, and trust structures. We conceptualize and axiomatize KP ontology including static KP and dynamic KP. The proposed KP ontology, provides a formal representation of linking trust in information creators and belief in the information created; lays a foundation for further study of knowledge provenance; provides logical systems for provenance reasoning by machines. The web ontology of KP can be used to annotate web information; and KP reasoner can be used as a tool to trace the origin and to determine the validity of Web information. Since knowledge provenance is based on trust in information sources, this thesis also proposes a logical theory of trust in epistemic logic and situation calculus. In particular, we formally define the semantics of trust; from it, we identify two types of trust: trust in belief and trust in performance; reveal and prove that trust in belief is transitive; trust in performance is not, but by trust in belief, trust in performance can propagate in social networks; by using situation calculus in trust formalization, the context of trust is formally represented by reified fluents; we also propose a distributed logical model for trust reasoning using social networks, by which each agent’s private data about trust relationships can be protected. This study provides a formal theoretical analysis on the transitivity of trust, which supports trust propagation in social networks. This study of trust supports not only knowledge provenance but also the general trust modeling in cyberspace.

The absorption of the energy in the form of heat from electromagnetic radiation is strongly dependent on the shape of the surface. Also, the transfer of this generated thermal energy is dependent on the surface area of the object in contact with the surrounding medium. Here in this thesis, we present a structural optimization method for metal nanostructures based on the shape dependency of their electromagnetic heat dissipation and thermodynamic transfer to the surroundings. We have used a parallel genetic algorithm (GA) in conjunction with a coupled electromagnetic (FDTD) and thermodynamic modeling of the metallic nanostructures for the optimization. The optimized nano-structure demonstrates significant improvement in electromagnetic heating in the spectral window of optimization as well as expedited cooling properties. The symmetry of the structures which is inherent in the design procedure makes them independent of the polarization at normal incidence and insensitive to the azimuthal direction of incidence.

The absorption of the energy in the form of heat from electromagnetic radiation is strongly dependent on the shape of the surface. Also, the transfer of this generated thermal energy is dependent on the surface area of the object in contact with the surrounding medium. Here in this thesis, we present a structural optimization method for metal nanostructures based on the shape dependency of their electromagnetic heat dissipation and thermodynamic transfer to the surroundings. We have used a parallel genetic algorithm (GA) in conjunction with a coupled electromagnetic (FDTD) and thermodynamic modeling of the metallic nanostructures for the optimization. The optimized nano-structure demonstrates significant improvement in electromagnetic heating in the spectral window of optimization as well as expedited cooling properties. The symmetry of the structures which is inherent in the design procedure makes them independent of the polarization at normal incidence and insensitive to the azimuthal direction of incidence.

A peer-to-peer (P2P) system is a networked system characterized by the lack of centralized control, in which all or most communication is symmetric. Also, a P2P system is supposed to handle frequent arrivals and departures of nodes, and is expected to scale to very large network sizes. These requirements make the design of P2P systems particularly challenging. We investigate two central issues pertaining to the design of P2P systems: load balancing and routing. In the first part of this thesis, we study the problem of load balancing in the context of Distributed Hash Tables (DHTs). Briefly, a DHT is a giant hash table that is maintained in a P2P fashion: Keys are mapped to a hash space I --- typically the interval [0,1), which is partitioned into blocks among the nodes, and each node stores the keys that are mapped to its block. Based on the position of their blocks in I, the nodes also set up connections among themselves, forming a routing network, which facilitates efficient key location. Typically, in a DHT it is desirable that the nodes' blocks are roughly of equal size, since this usually implies a balanced distribution of the load of storing keys among nodes, and it also simplifies the design of the routing network. We propose and analyze a simple distributed scheme for partitioning I, inspired by the multiple random choices paradigm. This scheme guarantees that, with high probability, the ratio between the largest and smallest blocks remains bounded by a small constant. It is also message efficient, and the arrival or departure of a node perturbs the current partition of I minimally. A unique feature of this scheme is that it tolerates adversarial arrivals and departures of nodes. In the second part of the thesis, we investigate the complexity of a natural decentralized routing protocol, in a broad family of randomized networks. The network family and routing protocol in question are inspired by a framework proposed by Kleinberg to model small-world phenomena in social networks, and they capture many designs that have been proposed for P2P systems. For this model we establish a general lower bound on the expected message complexity of routing, in terms of the average node degree. This lower bound almost matches the corresponding known upper bound.