Parameterized and multi-level tiled loop generation

Department Head: L. Darrell Whitley.2010 Summer.Includes bibliographical references.Tiling is a loop transformation that decomposes computations into a set of smaller computation blocks. The transformation has been proven to be useful for many high-level program optimizations, such as data locality optimization and exploiting coarse-grained parallelism, and crucial for architecture with limited resources, such as embedded systems, GPUs, and the Cell architecture. Data locality and parallelism will continue to serve as major vehicles for achieving high performance on modern architecture in multi-core era. In parameterized tiling the size of blocks is not fixed at compile time but remains a symbolic constant so that it can be selected/changed even at runtime. Parameterized tiled loops facilitate iterative and runtime optimizations, such as iterative compilation, auto-tuning and dynamic program adaption. In this dissertation we present a collection of techniques for generating parameterized and multi-level tiled loops from affine control loops and their parallelization. The tiled loop generation problem even for perfectly nested loops has been believed to have an exponential time complexity due to the heavy machinery like Fourier-Motzkin elimination. Disproving this decade-long belief, we provide a simple technique for generating tiled loop nests even from imperfectly nested loops. Our technique for perfectly nested loops consists of only syntactic processing that is applied only once and independently to each loop bound. Our approach to imperfectly nested loops is composed of a direct extension of the tiled code generation technique for perfectly nested loops and three simple optimizations on the resulting parameterized tiled loops. The generation as well as the optimizations are achieved only with purely syntactic processing, hence loop generation time remains negligible. We also present three schemes for multi-level tiling where tiling is applied more than once. All the schemes are scalable with respect to the number of tiling levels and can be combined to achieve better performance. To facilitate parallelization of parameterized tiled loops, we generate outermost tile-loops that are perfectly nested. We also provide a technique for statically restructuring parameterized tiled loops to the wavefront scheduling on shared memory system. Because the formulation of parameterized tiling does not fit into the well established polyhedral framework, such static restructuring has been a great challenge. However, we achieve this limited restructuring through a syntactic processing without any sophisticated machinery

The Mind Behind Crowdfunding: An Empirical Study of Speech Emotion in Fundraising Success

In online crowdfunding, individuals gather information from two primary sources, video pitches and text narratives. However, while the attributes of the attached video may have substantial effects on fundraising, previous literature has largely neglected effects of the video information. Therefore, this study focuses on speech information embedded in videos. Employing the machine learning techniques including speech recognition and linguistic style classifications, we examine the role of speech emotion and speech style in crowdfunding success, compared to that of text narratives. Using Kickstarter dataset in 2016, our preliminary results suggest that speech information –the linguistic styles– is significantly associated with the crowdfunding success, even after controlling for text and other project-specific information. More interestingly, linguistic styles of the speech have a more profound explanatory power than text narratives do. This study contributes to the growing body of crowdfunding research by providing the unexplored aspect of retrieving speech information from the video

Are All Spillovers Created Equal? The Impact of Blockbusters and the Composition of Backers in Online Crowdfunding

Crowdfunding has emerged alongside the IT development. It is believed that overwhelmingly successful projects, blockbusters, would have significant impacts on the overall crowdfunding platform. However, there are notable limitations in previous studies. First, we consider how the advent of blockbusters impact according to the projects’ similarity with inside and outside clusters, rather than pre-determined category. Second, we examine the blockbusters’ heterogeneity with the type of backers that bring different effects. We use project-level dataset and apply novel clustering method to analyze blockbuster effects. We find empirical evidence that blockbusters have a spillover effect on same categories, especially inside clusters experience larger effects than outside clusters. In the long run, these spillover effects decay faster in outside clusters, but last long for inside cluster. Furthermore, this result changes according to the composition of backers. Our study presents a promising avenue for the application of semantic network analysis to the crowdfunding context

Identity and Status: When Counterspeech Increases Hate Speech Reporting and Why

Much has been written about how social media platforms enable the rise of networked activism. However, few studies have examined how these platforms’ low-information environments shape how social movement activists, their opponents, and social media platforms interact. Hate speech reporting is one understudied area where such interactions occur. This article fills this gap by examining to what extent and how the gender and popularity of counterspeech in comment sections influence social media users’ willingness to report hate speech on the #MeToo movement. Based on a survey experiment (n = 1250) conducted in South Korea, we find that YouTube users are more willing to report such sexist hate speech when the counterspeech is delivered by a female rather than a male user. However, when the female user’s counterspeech received many upvotes, this was perceived to signal her enhanced status and decreased the intention to report hate speech, particularly among male users. No parallel patterns were found regarding other attitudes toward hate speech, counterspeech, YouTube, the #MeToo movement, and gender discrimination and hate speech legislation. These findings inform that users report hate speech based on potentially harmful content as well as their complex social interactions with other users and the platform.1

Parameterized Tiled Loops for Free

Parameterized tiled loops—where the tile sizes are not fixed at compile time, but remain symbolic parameters until later—are quite useful for iterative compilers and “auto-tuners” that produce highly optimized libraries and codes. Tile size parameterization could also enable optimizations such as register tiling to become dynamic optimizations. Although it is easy to generate such loops for (hyper) rectangular iteration spaces tiled with (hyper) rectangular tiles, many important computations do not fall into this restricted domain. Parameterized tile code generation for the general case of convex iteration spaces being tiled by (hyper) rectangular tiles has in the past been solved with bounding box approaches or symbolic Fourier Motzkin approaches. However, both approaches have less than ideal code generation efficiency and resulting code quality. We present the theoretical foundations, implementation, and experimental validation of a simple, unified technique for generating parameterized tiled code. Our code generation efficiency is comparable to all existing code generation techniques including those for fixed tile sizes, and the resulting code is as efficient as, if not more than, all previous techniques. Thus the technique provides parameterized tiled loops for free! Our “one-size-fits-all” solution, which is available as open source software can be adapted for use in production compilers