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School of Computational Science and Engineering

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Now showing 1 - 10 of 43
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    A Roofline Model of Energy
    (Georgia Institute of Technology, 2012) Choi, Jee Whan ; Vuduc, Richard ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Computational Science and Engineering ; Georgia Institute of Technology. School of Electrical and Computer Engineering
    We describe an energy-based analogue of the time-based roofline model of Williams, Waterman, and Patterson (Comm. ACM, 2009). Our goal is to explain—in simple, analytic terms accessible to algorithm designers and performance tuners—how the time, energy, and power to execute an algorithm relate. The model considers an algorithm in terms of operations, concurrency, and memory traffic; and a machine in terms of the time and energy costs per operation or per word of communication. We confirm the basic form of the model experimentally. From this model, we suggest under what conditions we ought to expect an algorithmic time-energy trade-off, and show how algorithm properties may help inform power management.
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    ExactMP: An Efficient Parallel Exact Solver for Phylogenetic Tree Reconstruction Using Maximum Parsimony
    (Georgia Institute of Technology, 2006-02-26) Bader, David A. ; Chandu, Vaddadi P. ; Yan, Mi
    Constructing phylogenetic trees in the study of the evolutionary history of a group organisms is an extremely challenging problem in computational biology. The problem becomes intractable with growing number of organisms. In this paper, we design and implement an efficient parallel solver (ExactMP) using a parsimony based approach for solving this problem. We create a testbed consisting of eighteen datasets of varying size (up to 27 taxa) and difficulty level (easy to hard), containing real (Eukaryotes, Metazoan, and rbcL) and randomly-generated synthetic genome sequences. We demonstrate our ExactMP Solver against this testbed and achieve a parallel speedup of up to 7.26 with 8 processors using an 8-way symmetric multiprocessor. The main contributions of this work are: (1) an efficient parallel solver ExactMP for the problem of phylogenetic tree reconstruction using maximum parsimony, (2) a new upper bounding methodology for this problem using heuristic and randomization techniques, and (3) a highly optimized branch and bound algorithm for this problem.
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    UnMask: Adversarial Detection and Defense in Deep Learning Through Building-Block Knowledge Extraction
    (Georgia Institute of Technology, 2019) Freitas, Scott ; Chen, Shang-Tse ; Chau, Duen Horng ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Computational Science and Engineering
    Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to biomedical diagnosis. However, recent research has demonstrated that many of these deep learning architectures are highly vulnerable to adversarial attacks—highlighting the vital need for defensive techniques to detect and mitigate these attacks before they occur. To combat these adversarial attacks, we developed UnMask, a knowledge-based adversarial detection and defense framework. The core idea behind UnMask is to protect these models by verifying that an image’s predicted class (“bird”) contains the expected building blocks (e.g., beak, wings, eyes). For example, if an image is classified as “bird”, but the extracted building blocks are wheel, seat and frame, the model may be under attack. UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its extracted building blocks. Our extensive evaluation shows that UnMask (1) detects up to 92.9% of attacks, with a false positive rate of 9.67% and (2) defends the model by correctly classifying up to 92.24% of adversarial images produced by the current strongest attack, Projected Gradient Descent, in the gray-box setting. Our proposed method is architecture agnostic and fast. To enable reproducibility of our research, we have anonymously open-sourced our code and large newly-curated dataset (~5GB) on GitHub (
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    Parallel Shortest Path Algorithms for Solving Large-Scale Instances
    (Georgia Institute of Technology, 2006-08-30) Madduri, Kamesh ; Bader, David A. ; Berry, Jonathan W. ; Crobak, Joseph R.
    We present an experimental study of parallel algorithms for solving the single source shortest path problem with non-negative edge weights (NSSP) on large-scale graphs. We implement Meyer and Sander's Δ-stepping algorithm and report performance results on the Cray MTA-2, a multithreaded parallel architecture. The MTA-2 is a high-end shared memory system offering two unique features that aid the efficient implementation of irregular parallel graph algorithms: the ability to exploit fine-grained parallelism, and low-overhead synchronization primitives. Our implementation exhibits remarkable parallel speedup when compared with a competitive sequential algorithm, for low-diameter sparse graphs. For instance, Δ-stepping on a directed scale-free graph of 100 million vertices and 1 billion edges takes less than ten seconds on 40 processors of the MTA-2, with a relative speedup of close to 30. To our knowledge, these are the first performance results of a parallel NSSP problem on realistic graph instances in the order of billions of vertices and edges.
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    Workshop on Future Direction in Numerical Algorithms and Optimization
    (Georgia Institute of Technology, 2008-01-15) Park, Haesun ; Golub, Gene ; Wu, Weili ; Du, Ding-Zhu ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. School of Computational Science and Engineering
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    Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares
    (Georgia Institute of Technology, 2006) Kim, Hyunsoo ; Park, Haesun
    Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Non-negative matrix factorization (NMF) is a useful technique in approximating these high dimensional data. Sparse NMFs are also useful when we need to control the degree of sparseness in non-negative basis vectors or non-negative lower-dimensional representations. In this paper, we introduce novel sparse NMFs via alternating non-negativity-constrained least squares. We applied one of the proposed sparse NMFs to cancer class discovery and gene expression data analysis. Our experimental results illustrate that our proposed method achieves better clustering performance than NMF based on multiplicative update rules and sparse NMFs based on the gradient descent method.
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    Detecting Communities from Given Seeds in Social Networks
    (Georgia Institute of Technology, 2011-02-22) Riedy, Jason ; Bader, David A. ; Jiang, Karl ; Pande, Pushkar ; Sharma, Richa ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Computational Science and Engineering
    Analyzing massive social networks challenges both high-performance computers and human under- standing. These massive networks cannot be visualized easily, and their scale makes applying complex analysis methods computationally expensive. We present a region-growing method for finding a smaller, more tractable subgraph, a community, given a few example seed vertices. Unlike existing work, we focus on a small number of seed vertices, from two to a few dozen. We also present the first comparison between five algorithms for expanding a small seed set into a community. Our comparison applies these algorithms to an R-MAT generated graph component with 240 thousand vertices and 32 million edges and evaluates the community size, modularity, Kullback-Leibler divergence, conductance, and clustering coefficient. We find that our new algorithm with a local modularity maximizing heuristic based on Clauset, Newman, and Moore performs very well when the output is limited to 100 or 1000 vertices. When run without a vertex size limit, a heuristic from McCloskey and Bader generates communities containing around 60% of the graph's vertices and having a small conductance and modularity appropriate to the result size. A personalized PageRank algorithm based on Andersen, Lang, and Chung also performs well with respect to our metrics.
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    A Comparison of Generalized Linear Discriminant Analysis Algorithms
    (Georgia Institute of Technology, 2006-01-28) Park, Cheong Hee ; Park, Haesun
    Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply the LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make the LDA applicable, several generalizations of the LDA have been proposed recently. In this paper, we present theoretical and algorithmic relationships among several generalized LDA algorithms and compare their computational complexities and performances in text classification and face recognition. Towards a practical dimension reduction method for high dimensional data, an efficient algorithm is proposed, which reduces the computational complexity greatly while achieving competitive prediction accuracies. We also present nonlinear extensions of these LDA algorithms based on kernel methods. It is shown that a generalized eigenvalue problem can be formulated in the kernel-based feature space, and generalized LDA algorithms are applied to solve the generalized eigenvalue problem, resulting in nonlinear discriminant analysis. Performances of these linear and nonlinear discriminant analysis algorithms are compared extensively.
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    Parallel Algorithms for Evaluating Centrality Indices in Real-World Networks
    (Georgia Institute of Technology, 2006-04-14) Bader, David A. ; Madduri, Kamesh
    This paper discusses fast parallel algorithms for evaluating several centrality indices frequently used in complex network analysis. These algorithms have been optimized to exploit properties typically observed in real-world large scale networks, such as the low average distance, high local density, and heavy-tailed power law degree distributions. We test our implementations on real datasets such as the web graph, protein-interaction networks, movie-actor and citation networks, and report impressive parallel performance for evaluation of the computationally intensive centrality metrics (betweenness and closeness centrality) on high-end shared memory symmetric multiprocessor and multithreaded architectures. To our knowledge, these are the first parallel implementations of these widely-used social network analysis metrics. We demonstrate that it is possible to rigorously analyze networks three orders of magnitude larger than instances that can be handled by existing network analysis (SNA) software packages. For instance, we compute the exact betweenness centrality value for each vertex in a large US patent citation network (3 million patents, 16 million citations) in 42 minutes on 16 processors, utilizing 20GB RAM of the IBM p5 570. Current SNA packages on the other hand cannot handle graphs with more than hundred thousand edges.
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    To Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendation
    (Georgia Institute of Technology, 2013) Choo, Jaegul ; Lee, Daniel ; Dilkina, Bistra ; Zha, Hongyuan ; Park, Haesun ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Computational Science and Engineering
    Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding micro-financial transactions available at Kiva. Based on this approach, we achieved a competitive performance of 0.84 AUC value in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan’s geolocation, a borrower’s gender, a field partner’s reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders’ background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results.