UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item Essays on Speculation, Joint Bidding, and Dynamic Entry in Auctions(2023) Deng, Shanglyu; Ausubel, Lawrence; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation consists of three essays on auction design. In Chapter 1, I provide an introduction for the following chapters. In Chapter 2, I examine speculation in procurement auctions, where speculators may have the incentive to acquire items from multiple sellers prior to the auction in order to increase their market power and reduce competition during the auction. I show that the profitability of the speculation scheme hinges on the auction format: Speculation always generates a positive expected profit in second-price auctions but could be unprofitable in first-price auctions. This comparison in profitability is driven by different competition patterns in the two auction mechanisms. In terms of welfare, speculation causes private value destruction and harms efficiency. Sellers benefit from the acquisition offer made by the speculator. Therefore, speculation comes at the expense of the auctioneer. In Chapter 3, I consider a procurement setting where suppliers may be functionally complementary, meaning they need to collaborate to complete a complex project. I compare two methods for incorporating complementary firms into procurement auctions: allowing them to bid jointly or using combinatorial auctions, such as the VCG auction, to coordinate their collaboration. The joint bidding approach leads to a double marginalization problem, as the prime contractor must elicit private cost information from subcontractors, and then submit a bid on behalf of the group. Consequently, the joint bidding approach often underperforms the VCG auction in several aspects, including efficiency, procurement price, and support for small businesses. Chapter 4 presents both theoretical and empirical analyses for recurring auctions. Auctions for durable assets, such as land, house, or artwork, are commonly recurring, as the seller often holds a subsequent auction after a previous attempt fails. Theoretical results show that recurring auctions outperform single-round auctions in terms of efficiency and revenue when potential buyers face costly entry. This occurs because recurring auctions allow potential buyers with different values to enter at different times, which generates savings in entry costs and increases the overall probability of sale. Additionally, optimal reserve price sequences are derived for recurring auctions based on whether the seller aims to maximize efficiency or revenue. In the empirical analysis, the theory is applied to home foreclosure auctions in China, where foreclosed homes are auctioned up to three times in a row. The study identifies the structural parameters in a recurring auction model and compares the observed recurring auctions to counterfactual single-round auctions. The results are in line with theoretical predictions, showing a significant improvement in efficiency and revenue for recurring auctions over single-round auctions. Using the optimal reserve price sequences derived from our model can further enhance the performance of recurring auctions in practice.Item Speculative Data Distribution in Shared Memory Multiprocessors(2008-04-16) Leventhal, Sean; Franklin, Manoj; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work explores the possibility of using speculation at the directories in a cache coherent non-uniform memory access multiprocessor architecture to improve performance by forwarding data to their destinations before requests are sent. It improves on previous consumer prediction techniques, showing how to construct a predictor that can handle a tradeoff of accuracy and coverage. This dissertation then explores the correct time to perform consumer prediction, and show how a directory protocol can incorporate such a scheme. The consumer prediction enhanced protocol that is developed is able to reduce the runtime of a set of scientific benchmarks by 10%-20%, without substantially reducing the runtime of other benchmarks; specifically, those benchmarks feature simple phased behavior and regularly distribute data to more than two processors. This work then explores the interaction of consumer prediction with two other forms of prediction, migratory prediction and last touch prediction. It demonstrates a mechanism by which migratory prediction can be implemented using only the storage elements already present in a consumer predictor. By combining this migratory predictor with a consumer predictor, it is possible to produce greater speedups than did either individually. Finally, the signatures of the last touch predictor can be applied to improve the performance of consumer prediction.Item Applying Perceptrons to Speculation in Computer Architecture(2007-04-05) Black, Michael David; Franklin, Manoj; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Speculation plays an ever-increasing role in optimizing the execution of programs in computer architecture. Speculative decision-makers are typically required to have high speed and small size, thus limiting their complexity and capability. Because of these restrictions, predictors often consider only a small subset of the available data in making decisions, and consequently do not realize their potential accuracy. Perceptrons, or simple neural networks, can be highly useful in speculation for their ability to examine larger quantities of available data, and identify which data lead to accurate results. Recent research has demonstrated that perceptrons can operate successfully within the strict size and latency restrictions of speculation in computer architecture. This dissertation first studies how perceptrons can be made to predict accurately when they directly replace the traditional pattern table predictor. Several weight training methods and multiple-bit perceptron topologies are modeled and evaluated in their ability to learn data patterns that pattern tables can learn. The effects of interference between past data on perceptrons are evaluated, and different interference reduction strategies are explored. Perceptrons are then applied to two speculative applications: data value prediction and dataflow critical path prediction. Several new perceptron value predictors are proposed that can consider longer or more varied data histories than existing table-based value predictors. These include a global-based local predictor that uses global correlations between data values to predict past local values, a global-based global predictor that uses global correlations to predict past global values, and a bitwise predictor that can use global correlations to generate new data values. Several new perceptron criticality predictors are proposed that use global correlations between instruction behaviors to accurately determine whether instructions lie on the critical path. These predictors are evaluated against local table-based approaches on a custom cycle-accurate processor simulator, and are shown on average to have both superior accuracy and higher instruction-per-cycle performance. Finally, the perceptron predictors are simulated using the different weight training approaches and multiple-bit topologies. It is shown that for these applications, perceptron topologies and training approaches must be selected that respond well to highly imbalanced and poorly correlated past data patterns.