Doctor of Philosophy

dissertationEmerging trends such as growing architectural diversity and increased emphasis on energy and power efficiency motivate the need for code that adapts to its execution context (input dataset and target architecture). Unfortunately, writing such code remains difficult, and is typically attempted only by a small group of motivated expert programmers who are highly knowledgeable about the relationship between software and its hardware mapping. In this dissertation, we introduce novel abstractions and techniques based on automatic performance tuning that enable both experts and nonexperts (application developers) to produce adaptive code. We present two new frameworks for adaptive programming: Nitro and Surge. Nitro enables expert programmers to specify code variants, or alternative implementations of the same computation, together with meta-information for selecting among them. It then utilizes supervised classification to select an optimal code variant at runtime based on characteristics of the execution context. Surge, on the other hand, provides a high-level nested data-parallel programming interface for application developers to specify computations. It then employs a two-level mechanism to automatically generate code variants and then tunes them using Nitro. The resulting code performs on par with or better than handcrafted reference implementations on both CPUs and GPUs. In addition to abstractions for expressing code variants, this dissertation also presents novel strategies for adaptively tuning them. First, we introduce a technique for dynamically selecting an optimal code variant at runtime based on characteristics of the input dataset. On five high-performance GPU applications, variants tuned using this strategy achieve over 93% of the performance of variants selected through exhaustive search. Next, we present a novel approach based on multitask learning to develop a code variant selection model on a target architecture from training on different source architectures. We evaluate this approach on a set of six benchmark applications and a collection of six NVIDIA GPUs from three distinct architecture generations. Finally, we implement support for combined code variant and frequency selection based on multiple objectives, including power and energy efficiency. Using this strategy, we construct a GPU sorting implementation that provides improved energy and power efficiency with less than a proportional drop in sorting throughput

Understanding the Effect of the Long Tail on Neural Network Compression

Network compression is now a mature sub-field of neural network research: over the last decade, significant progress has been made towards reducing the size of models and speeding up inference, while maintaining the classification accuracy. However, many works have observed that focusing on just the overall accuracy can be misguided. E.g., it has been shown that mismatches between the full and compressed models can be biased towards under-represented classes. This raises the important research question, \emph{can we achieve network compression while maintaining ``semantic equivalence'' with the original network?} In this work, we study this question in the context of the ``long tail'' phenomenon in computer vision datasets observed by Feldman, et al. They argue that \emph{memorization} of certain inputs (appropriately defined) is essential to achieving good generalization. As compression limits the capacity of a network (and hence also its ability to memorize), we study the question: are mismatches between the full and compressed models correlated with the memorized training data? We present positive evidence in this direction for image classification tasks, by considering different base architectures and compression schemes

Sustainability, Transformational Leadership, and Social Entrepreneurship

This article examines the extent to which culturally endorsed transformational leadership theories (CLTs) and the sustainability of society, both considered societal level institutional indicators, impact the emergence of social entrepreneurship. Using 107,738 individual-level responses from 27 countries for the year 2009 obtained from the Global Entrepreneurship Monitor (GEM) survey, and supplementing with country-level data obtained from Global Leadership and Organizational Behavior Effectiveness (GLOBE) and Sustainability Society Foundation (SSF), our findings from multilevel analysis show that transformational CLTs and sustainability conditions of society positively influence the likelihood of individuals becoming social entrepreneurs. Further, the effectiveness of transformational CLTs matters more for social entrepreneurship when the sustainability of society is low, which suggests the interaction between cultural leadership styles and societal sustainability. This article contributes to comparative entrepreneurship research by introducing strong cultural antecedents of social entrepreneurship in transformational CLTs and societal sustainability. We discuss various implications and limitations of our study, and we suggest directions for future research

Consequences of Cultural Leadership Styles for Social Entrepreneurship: A Theoretical Framework

The purpose of this conceptual article is to understand how the interplay of national-level institutions of culturally endorsed leadership styles, government effectiveness, and societal trust affects individual likelihood to become social entrepreneurs. We present an institutional framework comprising cultural leadership styles (normative institutions), government effectiveness (regulatory institutions), and societal trust (cognitive institutions) to predict individual likelihood of social entrepreneurship. Using the insight of culture⁻entrepreneurship fit and drawing on institutional configuration perspective we posit that culturally endorsed implicit leadership theories (CLTs) of charismatic and participatory leadership positively impact the likelihood of individuals becoming social entrepreneurs. Further, we posit that this impact is particularly pronounced when a country’s regulatory quality manifested by government effectiveness is supportive of social entrepreneurship and when there exist high levels of societal trust. Research on CLTs and their impact on entrepreneurial behavior is limited. We contribute to comparative entrepreneurship research by introducing a cultural antecedent of social entrepreneurship in CLTs and through a deeper understanding of their interplay with national-level institutions to draw the boundary conditions of our framework

Internationalization by technology entrepreneurs: A multi-level study

Early internationalization by new technology ventures may depend upon home country-specific factors, such as the institutional environment. Those with supportive home country conditions may be more likely to go international in order to gain access to new markets. Using the Global Entrepreneurship Monitor (GEM) survey from 2005-2008, this multi-level study uses a sample of nascent and new technology entrepreneurs from 45 countries and predicts which will internationalize early based on two contextual conditions present in the home country. The results indicate that a strong regulatory environment and smaller home market size both support early internationalization. Interaction results also indicate that in the case of a smaller home market size a strong regulatory environment is required for early internationalization of technology entrepreneurs

Home country factors and the decision to internationalize technology-based new ventures: A multi-level study of early-stage entrepreneurs

Using Global Entrepreneurship Monitor (GEM) survey of over 25,000 nascent and new entrepreneurs from 2005-2008 and 45 countries, we predict their internationalization decisions based on three domestic institutions. Results indicate that stronger regulatory environment, smaller home market, and weaker innovation environment favor internationalization. Interaction results indicate that strong regulatory environment helps overcome the negative effect of a large home market towards internationalization and that it also facilitates internationalization by aiding to acquire resources necessary for innovation that may be lacking domestically. Finally, a larger home market size reduces the need to internationalize to compensate for lacking innovation resources