
Kesheng
"John"
Wu
武克胜
 PhD graduate of the Computer
Science Department
 Professional associations
 ACM  Distinguished Scientist
 IEEE  Senior Member
 Current work (at Berkeley Lab,
Berkeley Lab on youTube)
 Bitmap Index: [FastBit source
code] [FastBit
documentation]
Our WAH compressed bitmap index, was
shown to be significantly faster than the best available commercial
DBMS product [SSDBM
2002], and can speed up visualization tasks as well [IEEE VIS
2005]. Extensive analyses show that our method is theoretical
optimal in the same sense that B*tree and B+tree are optimal for query
processing [ACM
TODS 2006, ACM
TODS 2010]. Best of all, the source code that implements this
indexing method and many others are available for free in a package
called FastBit. For its
capability in solving big data problems, FastBit has
received the R&D 100 Award in 2008 [Photo of
developers at R&D 100 Award event]. [Annotated References
on Bitmap Index]
 Connected Component
Labeling: This grows out of our work on feature tracking for
combustion data analysis. The key insight is that there is a way
to make use of an implicit
unionfind data structure to speed up the connected component
labeling algorithms, which in turn leads to faster algorithms for
finding regions of interest. In particular, using compressed bitmaps
as a representation of points in the regions of interest, we can find
these regions in time that is proportional to the the number of points
on the boundary of the regions. This is shown to be faster than the
best isocontouring algorithms and other region
finding algorithms. This is also the basis of some of the work on
visualization and visual analytics, e.g., finding flame front in
combustion data, find
distributed attacks in network traffic, and tracking particle in
Laser Wakefield Accelerator data.
 Eigenvalue
Computation: Thickrestart Lanczos method for symmetric
eigenvalue problems is a continuation of the work for solving eigenvalue
problems. A Fortran 90
implementation for symmetric eigenvalue problems and a C implement for
hermitian and symmetric eigenvalue problems are available under a BSDlike
license.
 Thesis work [PDF version of thesis]
[Publications]
 Title: Preconditioned Techniques for
Large Eigenvalue Problems
 Abstract This research focuses on
finding a large number of eigenvalues and eigenvectors of a sparse
symmetric or Hermitian matrix, for example, finding 1000 eigenpairs of a
100,000 × 100,000 matrix. These eigenvalue problems are challenging
because the matrix size is too large for traditional QR based algorithms
and the number of desired eigenpairs is too large for most common sparse
eigenvalue algorithms. In this thesis, we approach this problem in two
steps. First, we identify a sound preconditioned eigenvalue procedure
for computing multiple eigenpairs. Second, we improve the basic
algorithm through new preconditioning schemes and spectrum
transformations.
Through careful analysis, we see that both the Arnoldi and Davidson
methods have an appropriate structure for computing a large number of
eigenpairs with preconditioning. We also study three variations of
these two basic algorithms. Without preconditioning, these methods are
mathematically equivalent but they differ in numerical stability and
complexity. However, the Davidson method is much more successful when
preconditioned. Despite its success, the preconditioning scheme in the
Davidson method is seen as flawed because the preconditioner becomes
illconditioned near convergence. After comparison with other methods,
we find that the effectiveness of the Davidson method is due to its
preconditioning step being an inexact Newton method. We proceed to
explore other Newton methods for eigenvalue problems to develop
preconditioning schemes without the same flaws. We found that the
simplest and most effective preconditioner is to use the Conjugate
Gradient method to approximately solve equations generated by the Newton
methods. Also, a different strategy of enhancing the performance of the
Davidson method is to alternate between the regular Davidson iteration
and a polynomial method for eigenvalue problems. To use these
polynomials, the user must decide which intervals of the spectrum the
polynomial should suppress. We studied different schemes of selecting
these intervals, and found that these hybrid methods with polynomials
can be effective as well. Overall, the Davidson method with the CG
preconditioner was the most successful method the eigenvalue problems we
tested.
 Inforamtion available elsewhere on the web
 Google
Scholar,
Microsoft
Academic Search,
ResearchGate,
ACM Author
Profile,
DBLP
Academia.edu
 Rant and Rave
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