This thesis develops and tests a classification of ‘near-natural’ European single-thread rivers, which are free to adjust to fluvial processes. The research involves subdividing rivers along a continuum of geomorphological characteristics to assign river reaches to geomorphologically-meaningful classes according to their channel dimensions and forms, and floodplain characteristics. The classification was developed and tested through three research components. First, a preliminary classification was developed using information entirely derived from a new information system containing remotely-sensed imagery and digital terrain data: Google Earth. This research stage required the development of rules for identifying, extracting and standardising information from this source for a large sample of river reaches. 221 single-thread river reaches distributed across 75 European rivers were investigated. Analysis of the derived information resulted in the development of a classification comprising six classes of European single thread river. Second, the robustness of the classification was explored including assessments of (i) the degree to which the classes were interpretable in relation to the geomorphic features they displayed; (ii) the degree to which sub-divisions of the six classes could be identified and justified; (iii) the accuracy of some specific types of information extracted from Google Earth; and (iv) the degree to which the six classes corresponded to expected gradients in two controlling variables: stream power and bed sediment calibre. Thirdly, bar theory was applied to a sample of rivers representative of the six classes. Since bars are an important contributor to river channel form and dynamics, the correspondence of the bars in the six river classes to their expected distribution as indicated by bar theory, provided further confirmation of the robustness of the classification. The outputs of the research are (i) a fully-tested classification of European single-thread rivers; and (ii) a demonstration of how Google Earth can provide valuable information for research in fluvial geomorphology. Some additional future research stages are proposed that could turn the classification into an operational tool in the context of river assessment and management.

Conventional CMOS scaling has been the engine of the technology revolution in most application domains. This trend has changed as in each technology generation, transistor densities continue to increase while due to the limits on threshold voltage scaling, per-transistor energy consumption decreases much more slowly than in the past. The power scaling issues will restrict the adaptability of designs to operate in different power and performance regimes. Consequently, future systems must employ more efficient architectures for optimizing every thread in the program across different power and performance regimes, rather than architectures that utilize more transistors. One solution is composable or dynamic multicore architectures that can span a wide range of energy/performance operating points by enabling multiple simple cores to compose to form a larger and more powerful core. Explicit Data Graph Execution (EDGE) architectures represent a highly scalable class of composable processors that exploit predicated dataflow block execution and distributed microarchitectures. However, prior EDGE architectures suffer from several energy and performance bottlenecks including expensive intra-block operand communication due to fine-grain instruction distribution among cores, the compiler-generated fanout trees built for high-fanout operand delivery, poor next-block prediction accuracy, and low speculation rates due to predicates and expensive refills after pipeline flushes. To design an energy-efficient and flexible dynamic multicore, this dissertation employs a systematic methodology that detects inefficiencies and then designs and evaluates solutions that maximize power and performance efficiency across different power and performance regimes. Some innovations and optimization techniques include: (a) Deep Block Mapping extracts more coarse-grained parallelism and reduces cross-core operand network traffic by mapping each block of instructions into the instruction queue of one core instead of distributing blocks across all composed cores as done in previous EDGE designs, (b) Iterative Path Predictor (IPP) reduces branch and predication overheads by unifying multi-exit block target prediction and predicate path prediction while providing improved accuracy for each, (c) Register Bypassing reduces cross-core register communication delays by bypassing register values predicted to be critical directly from producing to consuming cores, (d) Block Reissue reduces pipeline flush penalties by reissuing instructions in previously executed instances of blocks while they are still in the instruction queue, and (e) Exposed Operand Broadcasts (EOBs) reduce wide-fanout instruction overheads by extending the ISA to employ architecturally exposed low-overhead broadcasts combined with dataflow for efficient operand delivery for both high- and low-fanout instructions. These components form the basis for a third-generation EDGE microarchitecture called T3. T3 improves energy efficiency by about 2x and performance by 47% compared to previous EDGE architectures. T3 also performs in a highly power efficient manner across a wide spectrum of energy and performance operating points (low-power to high-performance), extending the domain of power/performance trade-offs beyond what dynamic voltage and frequency scaling offers on state-of-the-art conventional processors. This high level of flexibility and power efficiency makes T3 an attractive candidate for future systems which need to operate on a wide range of workloads under varying power and performance constraints. / text

Background. As newer computers are constructed, more advanced and powerful hardware come along with them. This leads to the enhancement of various program attributes and features by corporations to get ahold of the hardware, hence, improving performance. A relatively new API which serves to facilitate such logic, is Microsoft DirectX 12. There are numerous opinions about this specific API, and to get a slightly better understanding of its capabilities with hardware utilization, this research puts it under some tests. Objectives. This article’s aim is to steadily perform tests and comparisons in order to find out which method has better performance when using DirectX 12; single-threading, or multithreading. For performance measurements, the average CPU and GPU utilizations are gathered, as well as the average FPS and the speed of which it takes to perform the Render function. When all results have been collected, the comparison between the methods are assessed. Methods. In this research, the main method which is being used is experiments. To find out the performance differences between the two methods, they must undergo different trials while data is gathered. There are four experiments for the single-threaded and multithreaded application, respectively. Each test varies in the number of lights and objects that are rendered in the simulation environment, gradually escalading from 50; then 100; 1000; and lastly, 5000. Results. A similar pattern was discovered throughout the experiments, with all of the four tests, where the multithreaded application used considerably more of the CPU than the single-threaded version. And despite there being less simultaneous work done by the GPU in the one-threaded program, it appeared to be using more GPU utilization than multithreading. Furthermore, the system with many threads tended to perform the Render function faster than its counterpart, regardless of which test was executed. Nevertheless, both applications never differed in FPS. Conclusion. Half of the hypotheses stated in this article were contradicted after some unexpected tun of events. It was believed that the multithreaded system would utilize less of the CPU and more of the GPU. Instead, the outcome contradicted the hypotheses, thus, opposing them. Another theory believed that the system with multiple threads would execute the Render function faster than the other version, a hypothesis that was strongly supported by the results. In addition to that, more objects and lights inserted into the scene did increased the applications’ utilization in both the CPU and GPU, which also supported another hypothesis. In conclusion, the multithreaded program performs faster but still has no gain in FPS compared to single-threading. The multithreaded version also utilizes more CPU and less GPU