The
key focus of my work is the multidisciplinary area of research known
as Combinatorial Scientific Computing (CSC), which brings together
key ideas from combinatorial optimization and algorithmics to solve
difficult problems in scientific computing. The design and
implementation of efficient parallel algorithms is critical in CSC.
Beginning with my PhD thesis, I have worked on several variants of
the graph matching problem. My collaborative work has not only
resulted in novel approximate algorithms but also in scalable
implementations on state-of-the-art computer systems. Since joining
PNNL, I have continued this work to develop new algorithms and
scalable implementations for coloring, clustering, network
alignment, stochastic coordinate descent, extraction of chordal
subgraphs and influence maximization. Scalable implementations have
targeted distributed-memory systems, traditional multicore and
manycore (GPUs; Tilera) platforms and massively multithreaded (XMT)
architectures. Establishment of the ECP
Codesign Center on graph algorithms (ExaGraph) is a testament
to the importance of this work in the context of scientific
computing. It is also worth to note that the same set of algorithms
are equally important in the emerging areas of graph analytics and
graph-based semi-supervised learning.
I portion of my work has
covered topics in game theory, numerical optimization, machine
learning and uncertainty quantification. I have explored several
application areas including the study of electric power grid,
computational biology, fault tolerance and scientific workflows.
Projects
Externally funded projects
(PI/Co-PI/Senior Personnel):
- Department of Energy-Advanced
Scientific Computing Research (DOE-ASCR): Exascale Computing
Projects. “ExaGraph: Combinatorial Methods for Enabling Exascale
Applications Co-Design Center.” (FY2017–FY2021)
- Department
of Energy-Advanced Scientific Computing Research (DOE-ASCR):
“IPPD: Integrated End-to-End Performance Prediction and
Diagnosis for Extreme Scientific Workflows.” (FY2015–FY2018) --
(Renewed: FY2019-FY2021)
- Department
of Energy-Advanced Research Projects Agency-Energy (DOE-ARPAE):
GRID DATA Program. Generating Realistic Information for the
Development of Distribution and Transmission Algorithms.
“Sustainable Data Evolution Technology for Power Grid
Optimization.” (FY2017–FY2019)
- DARPA Hierarchical Identify
Verify Exploit (HIVE) Program
- Department of Defense: “HPDA:
High Performance Data Analytics.” (FY2016–FY2017)
- Department of Energy-Advanced
Scientific Computing Research (DOE-ASCR): “M2ACS: Multifaceted
Mathematics for Complex Energy Systems.” (FY2012–FY2017)
- Department of Defense:
“CASS-MT: Center for Adaptive Supercomputing
Software-Multithreaded.” (FY2009–FY2015)
Laboratory Directed Research
and Development (LDRD) Projects, Pacific Northwest National
Laboratory (PI/Co-PI/Senior Personnel):
- LDRD Seed: Towards Automated Vulnerability and Mitigation of
Critical Infrastructure (FY2018)
- NSD LDRD: Cyber-based Contingency Analysis of Interdependent
Infrastructure Networks under Uncertainty (FY2019–FY2020)
- MinT-Net: Novel and Scalable Network-enabled Comparative Tools for
Stress Studies of Microbiomes in Transition (FY2017)
- Rendezvous: Optimization and Stochastic Algorithms for Asymmetric
Resilient Infrastructure (FY2015–FY2017)
- Concurrent Design and Control of Complex Systems: Controllability,
Observability and Performance Metrics (FY2017–FY2018)
- Resilience in Large-Scale Distributed Control Systems
(FY2015–FY2018)
- Scaling Multisubject Cortical Parcellation (FY2016)
- M&Ms4Graphs: A Multi-scale, Multi-Dimensional Graph Analytics
Framework for Cyber Security (FY2013–FY2016)
- Shyre: Streaming Hypothesis Reasoning (FY2014–FY2015)
- Theory of Resilience (FY2014–FY2015)
- Future Power Grid Control Paradigm (FY2012–FY2015)
- Linear Algebra Solvers and Associated Matrix-Vector Kernels for
Power Grid Simulations (FY2010–FY2015)
- Comparing Performance on Different High Performance Computing
Architectures (FY2010)
Synergistic Activities
- Co-organizing GrAPL
2019: Workshop on Graphs, Architectures, Programming, and Learning,
Co-Located with IPDPS 2019. Rio De Janeiro, Brazil
- Co-organized GraML
2018: Second Workshop on the Intersection of Graph Algorithms and
Machine Learning, Co-located with IPDPS 2018 May 25, 2018.
Vancouver, British Columbia, Canada
- Co-organized 2017:GraML’17:
First Workshop on the Intersection of Graph Algorithms and Machine
Learning, Co-located with IPDPS 2017. Orlando, Florida, USA.
- Co-organized the Workshop on
Parallel Algorithms and Software for Analysis of Massive Graphs
(ParGraph), held in conjunction with HiPC, India, 2011 and 2012.
- Co-organized mini-symposiums
at SIAM conferences (Annual Meeting, Parallel Processing for
Scientific Computing, Computational Science and Engineering).
- Participated on the program
committees for several conferences and workshops.
Doctoral Thesis
Abstract: A
matching M in a graph is a subset of edges such that no
two edges in M are incident on the same vertex. Matching
is a fundamental combinatorial problem with many applications. In
the first part of this thesis, we develop exact and approximation
algorithms for vertex weighted matchings, an under-studied variant
of the weighted matching problem. We propose three exact algorithms,
three half approximation algorithms, and a two-third approximation
algorithm. We exploit inherent properties of this problem such as
lexicographical orders, decomposition into sub-problems, and the
reachability property, not only to design efficient algorithms, but
also to provide simple proofs of correctness of the proposed
algorithms. In the second part of this thesis, we describe work on a
new parallel half-approximation algorithm for weighted matching.
Algorithms for computing optimal matchings are not amenable to
parallelism, and hence we consider approximation algorithms here. We
extend the existing work on a parallel half approximation algorithm
for weighted matching and provide an analysis of its time
complexity. We support the theoretical observations with
experimental results obtained with MatchBoxP, toolkit designed and
implemented in C++ and MPI using modern software engineering
techniques. The work in this thesis has resulted in better
understanding of matching theory, a functional public-domain
software toolkit, and modeling of the sparsest basis problem as a
vertex-weighted matching problem.