David A. Bader - Georgia Institute of Technology

David A. Bader
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Contact Details

Name
David A. Bader
Affiliation
Georgia Institute of Technology
City
Atlanta
Country
United States

Pubs By Year

Pub Categories

 
Computer Science - Data Structures and Algorithms (4)
 
Computer Science - Distributed; Parallel; and Cluster Computing (2)
 
Computer Science - Computational Engineering; Finance; and Science (2)
 
Computer Science - Mathematical Software (2)
 
Computer Science - Databases (1)
 
Computer Science - Software Engineering (1)
 
Computer Science - Performance (1)
 
Computer Science - Numerical Analysis (1)
 
Quantitative Biology - Genomics (1)
 
Instrumentation and Methods for Astrophysics (1)
 
Computer Science - Discrete Mathematics (1)

Publications Authored By David A. Bader

The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-Indel-Exemplar distance; versus the other by gene matching, namely DCJ-Indel-Matching distance. Read More

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. Read More

Many common methods for data analysis rely on linear algebra. We provide new results connecting data analysis error to numerical accuracy, which leads to the first meaningful stopping criterion for two way spectral partitioning. More generally, we provide pointwise convergence guarantees so that blends (linear combinations) of eigenvectors can be employed to solve data analysis problems with confidence in their accuracy. Read More

2014Aug
Affiliations: 1Intel Corporation, 2Georgia Institute of Technology, 3Sandia National Laboratory, 4Lawrence Berkeley National Laboratory, 5University of Tennessee, 6Carnegie Melon University, 7Pacific Northwest National Laboratory, 8University of California at Santa Barbara, 9University of California at Berkeley, 10Sandia National Laboratory, 11Massachusetts Institute of Technology, 12Massachusetts Institute of Technology, 13Indiana University, 14University of Illinois at Urbana-Champaign, 15Oak Ridge National Laboratory, 16Cray Corporation, 17Massachusetts Institute of Technology, 18Convey Corporation, 19Lawrence Livermore National Laboratory

It is our view that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks. This paper is a position paper defining the problem and announcing our intention to launch an open effort to define this standard. Read More

DNA sequence analysis is fundamental to life science research. The rapid development of next generation sequencing (NGS) technologies, and the richness and diversity of applications it makes feasible, have created an enormous gulf between the potential of this technology and the development of computational methods to realize this potential. Bridging this gap holds possibilities for broad impacts toward multiple grand challenges and offers unprecedented opportunities for software innovation and research. Read More

With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. Read More

The Cell Broad Engine (BE) Processor has unique memory access architecture besides its powerful computing engines. Many computing-intensive applications have been ported to Cell/BE successfully. But memory-intensive applications are rarely investigated except for several micro benchmarks. Read More