Stephen Curial

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We study the problem of visualizing large networks and develop techniques for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduction techniques are based on sampling, where only a sample instead of the full network is visualized. We propose a randomized notion of “focus” that specifies a part of the(More)
This paper describes Memory-Pooling-Assisted Data Splitting (MPADS), a framework that combines data structure splitting with memory pooling --- Although it MPADS may call to mind memory padding, a distintion of this framework is that is does not insert padding. MPADS relies on pointer analysis to ensure that splitting is safe and applicable to type-unsafe(More)
Visual display of networks can lead to both better understanding and clear presentation of patterns that can often be hidden [3]. However, effectively visualizing large networks has proven to be difficult, due to the limitations of the screen, the complexity of layout algorithms and the limitations of human visual perception. A good layout algorithm (eg.(More)
This paper presents a profiling-based analysis to determine the traversal orientation of link-based tree data structures. Given the very-high memory-hierarchy latencies in modern computers, once the compiler has identified that a pointer-based data structure represents a tree, it would be useful to determine the predominant orientation of traversal for the(More)
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