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Computational Science and Engineering Seminar Series

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Now showing 1 - 10 of 35
  • Item
    Extending Hadoop to Support Binary-Input Applications
    (Georgia Institute of Technology, 2012-10-19) Hong, Bo ; College of Computing ; School of Computational Science and Engineering
    Many data-intensive applications naturally take multiple inputs, which is not well supported by some popular MapReduce implementations, such as Hadoop. In this talk, we present an extension of Hadoop to better support such applications. The extension is expected to provide the following benefits: (1) easy to program for such applications, (2) explores data localities better than native Hadoop, and (3) improves application performance.
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    Coordinate Sampling for Sublinear Optimization and Nearest Neighbor Search
    (Georgia Institute of Technology, 2011-04-22) Clarkson, Kenneth L. ; College of Computing ; School of Computational Science and Engineering
    I will describe randomized approximation algorithms for some classical problems of machine learning, where the algorithms have provable bounds that hold with high probability. Some of our algorithms are sublinear, that is, they do not need to touch all the data. Specifically, for a set of points a[subscript 1]...a[subscript n] in d dimensions, we show that finding a d-vector x that approximately maximizes the margin min[subscript i] a[subscript i dot x can be done in O(n+d)/epsilon[superscript 2] time, up to logarithmic factors, where epsilon>0 is an additive approximation parameter. This was joint work with Elad Hazan and David Woodruff. A key step in these algorithms is the use of coordinate sampling to estimate dot products. This simple technique can be an effective alternative to random projection sketching in some settings. I will discuss the potential of coordinate sampling for speeding up some data structures for nearest neighbor searching in the Euclidean setting, via fast approximate distance evaluations.
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    Efficient High-Order Discontinuous Galerkin Methods for Fluid Flow Simulations
    (Georgia Institute of Technology, 2010-02-22) Shahbazi, Khosro ; College of Computing ; School of Computational Science and Engineering
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    The Aha! Moment: From Data to Insight
    (Georgia Institute of Technology, 2014-02-07) Shahaf, Dafna ; College of Computing ; School of Computational Science and Engineering
    The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally. The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising. I will formulate both problems mathematically and provide efficient, scalable methods for solving them. User studies on real-world datasets demonstrate that our methods help users acquire insight efficiently across multiple domains.
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    Dependable direct solutions for linear systems using a little extra precision
    (Georgia Institute of Technology, 2009-08-21) Riedy, E. Jason ; College of Computing ; School of Computational Science and Engineering
    Solving a square linear system Ax=b often is considered a black box. It's supposed to "just work," and failures often are blamed on the original data or subtleties of floating-point. Now that we have an abundance of cheap computations, however, we can do much better. A little extra precision in just the right places produces accurate solutions cheaply or demonstrates when problems are too hard to solve without significant cost. This talk will outline the method, iterative refinement with a new twist; the benefits, small backward and forward errors; and the trade-offs and unexpected benefits.
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    Cyber Games
    (Georgia Institute of Technology, 2013-02-19) Vorobeychik, Yevgeniy ; College of Computing ; School of Computational Science and Engineering
    Over the last few years I have been working on game theoretic models of security, with a particular emphasis on issues salient in cyber security. In this talk I will give an overview of some of this work. I will first spend some time motivating game theoretic treatment of problems relating to cyber and describe some important modeling considerations. In the remainder, I will describe two game theoretic models (one very briefly), and associated solution techniques and analyses. The first is the "optimal attack plan interdiction" problem. In this model, we view a threat formally as a sophisticated planning agent, aiming to achieve a set of goals given some specific initial capabilities and considering a space of possible "attack actions/vectors" that may (or may not) be used towards the desired ends. The defender's goal in this setting is to "interdict" a select subset of attack vectors by optimally choosing among miti-gation options, in order to prevent the attacker from being able to achieve its goals. I will describe the formal model, explain why it is challenging, and present highly scalable decomposition-based integer programming techniques that leverage extensive research into heuristic formal planning in AI. The second model addresses the problem that defense decisions are typically decentralized. I describe a model to study the impact of decentralization, and show that there is a "sweet spot": for an intermediate number of decision makers, the joint decision is nearly socially optimal, and has the additional benefit of being robust to the changes in the environment. Finally, I will describe the Secure Design Competition (FIREAXE) that involved two teams of interns during the summer of 2012. The problem that the teams were tasked with was to design a highly stylized version of an electronic voting system. The catch was that after the design phase, each team would attempt to "attack" the other's design. I will describe some salient aspects of the specification, as well as the outcome of this competition and lessons that we (the designers and the students) learned in the process.
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    High-performance-computing challenges for heart simulations
    (Georgia Institute of Technology, 2012-08-31) Fenton, Flavio H. ; College of Computing ; School of Computational Science and Engineering
    The heart is an electro-mechanical system in which, under normal conditions, electrical waves propagate in a coordinated manner to initiate an efficient contraction. In pathologic states, propagation can destabilize and exhibit chaotic dynamics mostly produced by single or multiple rapidly rotating spiral/scroll waves that generate complex spatiotemporal patterns of activation that inhibit contraction and can be lethal if untreated. Despite much study, little is known about the actual mechanisms that initiate, perpetuate, and terminate spiral waves in cardiac tissue. In this talk, I will motivate the problem with some experimental examples and then discuss how we study the problem from a computational point of view, from the numerical models derived to represent the dynamics of single cells to the coupling of millions of cells to represent the three-dimensional structure of a working heart. Some of the major difficulties of computer simulations for these kinds of systems include: i) Different orders of magnitude in time scales, from milliseconds to seconds; ii) millions of degrees of freedom over millions of integration steps within irregular domains; and iii) the need for near-real-time simulations. Advances in these areas will be discussed as well as the use of GPUs over the web using webGL?
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    Load-Balanced Bonded Force Calculations on Anton
    (Georgia Institute of Technology, 2010-03-15) Franchetti, Franz ; College of Computing ; School of Computational Science and Engineering
    Spiral (www.spiral.net) is a program and hardware design generation system for linear transforms such as the discrete Fourier transform, discrete cosine transforms, filters, and others. We are currently extending Spiral beyond its original problem domain, using coding algorithms (Viterbi decoding and JPEG 2000 encoding) and image formation synthetic aperture radar, SAR) as examples. For a user-selected problem specification, Spiral autonomously generates different algorithms, represented in a declarative form as mathematical formulas, and their implementations to find the best match to the given target platform. Besides the search, Spiral performs deterministic optimizations on the formula level, effectively restructuringthe code in ways unpractical at the code or design level. Spiral generates specialized single-size implementations or adaptive general-size autotuning libraries, and utilizes special instructions and multiple processor cores. The implementation generated by Spiral rival the performance of expertly hand-tuned libraries. In this talk, we give a short overview on Spiral. We explain how Spiral generates efficient programs for parallel platforms including vector architectures, shared and distributed memory platforms, and GPUs; as well as hardware designs (Verilog) and automatically partitioned software/hardware implementations. We overview how Spiral targets the Cell BE and PowerXCell 8i, the BlueGene/P PPC450d processors, as well as Intel's upcoming Larrabee GPU and AVX vector instruction set. As all optimizations in Spiral, parallelization and partitioning are performed on a high abstraction level of algorithm representation, using rewriting systems.
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    Accurate Inference of Phylogenetic Relationships from Multi-locus Data
    (Georgia Institute of Technology, 2010-03-09) Nakhleh, Luay ; College of Computing ; School of Computational Science and Engineering
    Accurate inference of phylogenetic relationships of species, and understanding their relationships with gene trees are two central themes in molecular and evolutionary biology. Traditionally, a species tree is inferred by (1) sequencing a genomic region of interest from the group of species under study, (2) reconstructing its evolutionary history, and (3) declaring it to be the estimate of the species tree. However, recent analyses of increasingly available multi-locus data from various groups of organisms have demonstrated that different genomic regions may have evolutionary histories (called "oegene trees") that may disagree with each other, as well as with that of the species. This observation has called into question the suitability of the traditional approach to species tree inference. Further, when some, or all, of these disagreements are caused by reticulate evolutionary events, such as hybridization, then the phylogenetic relationship of the species is more appropriately modeled by a phylogenetic network than a tree. As a result, a new, post-genomic paradigm has emerged, in which multiple genomic regions are analyzed simultaneously, and their evolutionary histories are reconciled in order to infer the evolutionary history of the species, which may not necessarily be treelike. In this talk, I will describe our recent work on developing mathematical criteria and algorithmic techniques for analyzing incongruence among gene trees, and inferring phylogenetic relationships among species despite such incongruence. This includes work on lineage sorting, reticulate evolution, as well as simultaneous treatment of both. If time permits, I will describe our recent work on population genomic analysis of bacterial data, and the implications on the evolutionary forces shaping the genomic diversity in these populations.
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    New Approaches to Protein Functional Inference and Ligand Screening: Application to the Human Kinome
    (Georgia Institute of Technology, 2011-01-14) Skolnick, Jeffrey ; College of Computing ; School of Computational Science and Engineering