A Finite Domain Constraint Approach for Placement and Routing of Coarse-Grained Reconfigurable Architectures

Scheduling, placement, and routing are important steps in Very Large Scale Integration (VLSI) design. Researchers have developed numerous techniques to solve placement and routing problems. As the complexity of Application Specific Integrated Circuits (ASICs) increased over the past decades, so did the demand for improved place and route techniques. The primary objective of these place and route approaches has typically been wirelength minimization due to its impact on signal delay and design performance. With the advent of Field Programmable Gate Arrays (FPGAs), the same place and route techniques were applied to FPGA-based design. However, traditional place and route techniques may not work for Coarse-Grained Reconfigurable Architectures (CGRAs), which are reconfigurable devices offering wider path widths than FPGAs and more flexibility than ASICs, due to the differences in architecture and routing network. Further, the routing network of several types of CGRAs, including the Field Programmable Object Array (FPOA), has deterministic timing as compared to the routing fabric of most ASICs and FPGAs reported in the literature. This necessitates a fresh look at alternative approaches to place and route designs. This dissertation presents a finite domain constraint-based, delay-aware placement and routing methodology targeting an FPOA. The proposed methodology takes advantage of the deterministic routing network of CGRAs to perform a delay aware placement

Classification in high-dimensional feature spaces: Random subsample ensemble

Abstract-This paper presents application of machine learning ensembles, that randomly project the original high dimensional feature space onto multiple lower dimensional feature subspaces, to classification problems with highdimensional feature spaces. The motivation is to address challenges associated with algorithm scalability, data sparsity and information loss due to the so-called curse of dimensionality. The original high dimensional feature space is randomly projected onto a number of lower-dimensional feature subspaces. Each of these subspaces constitutes the domain of a classification subtask, and is associated with a base learner within an ensemble machine-learner context. Such an ensemble conceptualization is called as random subsample ensemble. Simulation results performed on data sets with up to 20,000 features indicate that the random subsample ensemble classifier performs comparably to other benchmark machine learners based on performance measures of prediction accuracy and cpu time. This finding establishes the feasibility of the ensemble and positions it to tackle classification problems with even much higher dimensional feature spaces

Geographic Information Science

This chapter begins with a definition of geographic information science (GIScience). We then discuss how this research area has been influenced by recent developments in computing and data-intensive analysis, before setting out its core organizing principles from a practical perspective. The following section reflects on the key characteristics of geographic information, the problems posed by large data volumes, the relevance of geographic scale, the remit of geographic simulation, and the key achievements of GIScience to date. Our subsequent review of changing scientific practices and the changing problems facing scientists addresses developments in high-performance computing, heightened awareness of the social context of geographic information systems (GISystems), and the importance of neogeography in providing new data sources, in driving the need for new techniques, and in heightening a human-centric perspective

D2P: Automatically Creating Distributed Dynamic Programming Codes

Dynamic Programming (DP) algorithms are common targets for parallelization, and, as these algorithms are applied to larger inputs, distributed implementations become necessary. However, creating distributed-memory solutions involves the challenges of task creation, program and data partitioning, communication optimization, and task scheduling. In this paper we present D2P, an end-to-end system for automatically transforming a specification of any recursive DP algorithm into distributed-memory implementation of the algorithm. When given a pseudo-code of a recursive DP algorithm, D2P automatically generates the corresponding MPI-based implementation. Our evaluation of the generated distributed implementations shows that they are efficient and scalable. Moreover, D2P-generated implementations are faster than implementations generated by recent general distributed DP frameworks, and are competitive with (and often faster than) hand-written implementations

Query-by-Pointing: Algorithms and Pointing Error Compensation

People typically communicate by pointing, talking, sketching, writing, and typing. Pointing can be used to visualize or exchange information about an object when there is no other mutually understood way of communication. Despite its proven expressiveness, however, it has not yet become a frequently used modality to interact with computer systems. With the rapid move towards the adoption of mobile technologies, geographic information systems (GISs) have a particular need for advanced forms of interaction that enable users to query the geographic world directly. To enable pointing-based query system on a handheld device, a number of fundamental technical challenges have to be overcome. For such a system to materialize we need models stored in the device\u27s knowledge base that can be used as surrogate of real world objects. These computations, however, assume that (1) the pointing direction matches with the line-of-sight and (2) the observations about location and direction are precise enough so that a computational model will determine the same object as what the user points at. Both assumptions are not true. This thesis, therefore, develops an efficient error compensation model to reduce the discrepancy between the line-of-sight of the eye and the pointer direction. The model is based on a coordinate system centered at the neck and distances measured from neck to eye, neck to shoulder, shoulder to handheld pointer, and the pointing direction. An experiment was conducted using a gyro-enhanced sensor and three subjects who pointed at marked targets in a given room. It showed that the error compensation algorithm significantly reduces errors in pointing with arms outstretched

AVOIDIT IRS: An Issue Resolution System To Resolve Cyber Attacks

Cyber attacks have greatly increased over the years and the attackers have progressively improved in devising attacks against specific targets. Cyber attacks are considered a malicious activity launched against networks to gain unauthorized access causing modification, destruction, or even deletion of data. This dissertation highlights the need to assist defenders with identifying and defending against cyber attacks. In this dissertation an attack issue resolution system is developed called AVOIDIT IRS (AIRS). AVOIDIT IRS is based on the attack taxonomy AVOIDIT (Attack Vector, Operational Impact, Defense, Information Impact, and Target). Attacks are collected by AIRS and classified into their respective category using AVOIDIT.Accordingly, an organizational cyber attack ontology was developed using feedback from security professionals to improve the communication and reusability amongst cyber security stakeholders. AIRS is developed as a semi-autonomous application that extracts unstructured external and internal attack data to classify attacks in sequential form. In doing so, we designed and implemented a frequent pattern and sequential classification algorithm associated with the five classifications in AVOIDIT. The issue resolution approach uses inference to educate the defender on the plausible cyber attacks. The AIRS can work in conjunction with an intrusion detection system (IDS) to provide a heuristic to cyber security breaches within an organization. AVOIDIT provides a framework for classifying appropriate attack information, which is fundamental in devising defense strategies against such cyber attacks. The AIRS is further used as a knowledge base in a game inspired defense architecture to promote game model selection upon attack identification. Future work will incorporate honeypot attack information to improve attack identification, classification, and defense propagation.In this dissertation, 1,025 common vulnerabilities and exposures (CVEs) and over 5,000 lines of log files instances were captured in the AIRS for analysis. Security experts were consulted to create rules to extract pertinent information and algorithms to correlate identified data for notification. The AIRS was developed using the Codeigniter [74] framework to provide a seamless visualization tool for data mining regarding potential cyber attacks relative to web applications. Testing of the AVOIDIT IRS revealed a recall of 88%, precision of 93%, and a 66% correlation metric

A metadata-enhanced framework for high performance visual effects

This thesis is devoted to reducing the interactive latency of image processing computations in visual effects. Film and television graphic artists depend upon low-latency feedback to receive a visual response to changes in effect parameters. We tackle latency with a domain-specific optimising compiler which leverages high-level program metadata to guide key computational and memory hierarchy optimisations. This metadata encodes static and dynamic information about data dependence and patterns of memory access in the algorithms constituting a visual effect – features that are typically difficult to extract through program analysis – and presents it to the compiler in an explicit form. By using domain-specific information as a substitute for program analysis, our compiler is able to target a set of complex source-level optimisations that a vendor compiler does not attempt, before passing the optimised source to the vendor compiler for lower-level optimisation. Three key metadata-supported optimisations are presented. The first is an adaptation of space and schedule optimisation – based upon well-known compositions of the loop fusion and array contraction transformations – to the dynamic working sets and schedules of a runtimeparameterised visual effect. This adaptation sidesteps the costly solution of runtime code generation by specialising static parameters in an offline process and exploiting dynamic metadata to adapt the schedule and contracted working sets at runtime to user-tunable parameters. The second optimisation comprises a set of transformations to generate SIMD ISA-augmented source code. Our approach differs from autovectorisation by using static metadata to identify parallelism, in place of data dependence analysis, and runtime metadata to tune the data layout to user-tunable parameters for optimal aligned memory access. The third optimisation comprises a related set of transformations to generate code for SIMT architectures, such as GPUs. Static dependence metadata is exploited to guide large-scale parallelisation for tens of thousands of in-flight threads. Optimal use of the alignment-sensitive, explicitly managed memory hierarchy is achieved by identifying inter-thread and intra-core data sharing opportunities in memory access metadata. A detailed performance analysis of these optimisations is presented for two industrially developed visual effects. In our evaluation we demonstrate up to 8.1x speed-ups on Intel and AMD multicore CPUs and up to 6.6x speed-ups on NVIDIA GPUs over our best hand-written implementations of these two effects. Programmability is enhanced by automating the generation of SIMD and SIMT implementations from a single programmer-managed scalar representation