(2024)
(2023)
GraphIte: Accelerating iterative
graph algorithms on ReRAM architectures via approximate computing.
Dwaipayan Choudhury, Ananth
Kalyanaraman, Partha Pande.
Proc. 2023 Design, Automation
& Test in Europe Conference & Exhibition (DATE), pp.
1-6, 2023. DOI: 10.23919/DATE56975.2023.10137001.
Best Paper Finalist.
PDF
(2022)
Dynamic community
detection decouples hierarchical timescale behavior of complex
chemical systems.
Neda Zarayeneh, Nitesh Kumar, Ananth
Kalyanaraman, Aurora Clark.
Journal of Chemical Theory and Computation (JCTC), Accepted/In press,
2022.
ChemRxiv preprint (not up-to-date):
DOI: 10.26434/chemrxiv-2022-3gmnp.
PDF
IMpart: A partitioning-based parallel
approach to accelerate influence maximization.
Reet Barik, Marco Minutoli, Mahantesh
Halappanavar, Ananth Kalyanaraman.
Proc. International Conference on
High Performance Computing, Data, and Analytics (HiPC), pp.
125-134,2022. DOI: 10.1109/HiPC56025.2022.00028.
PDF
Accelerating graph computations on 3D
NoC-enabled PIM architectures.
Dwaipayan Choudhury, Lizhi Xiang,
Aravind Sukumaran-Rajam, Ananth Kalyanaraman, Partha Pande.
IEEE Transactions on Design
Automation of Electronic Systems (TODAES), Accepted/In
press, 2022.
HBMax: Optimizing Memory Efficiency
for Parallel Influence Maximization on Multicore Architectures.
Xinyu Chen, Marco Minutoli, Jiannan
Tian, Mahantesh Halappanavar, Ananth Kalyanaraman, Dingwen Tao.
Proc. International Conference on
Parallel Architectures and Compilation Techniques (PACT),
Accepted, 2022.
Use of Contact Networks to Estimate
Potential Pathogen Risk Exposure in Hospitals.
Kaniz Fatema Madhobi, Ananth
Kalyanaraman, Deverick J. Anderson, Elizabeth Dodds Ashley, Rebekah W.
Moehring, Eric T. Lofgren.
JAMA Network Open,
5(8):e2225508, 2022. DOI: 10.1001/jamanetworkopen.2022.25508.
PDF
Special report: The AgAID AI
institute for transforming workforce and decision support.
Ananth Kalyanaraman, Margaret
Burnett, Alan Fern, Lav Khot, Joshua Viers.
Computers and Electronics in Agriculture (COMPAG), vol.
197, June 2022, 106944. DOI: 10.1016/j.compag.2022.106944.
PDF
Scalable and memory-efficient
algorithms for controlling networked epidemic processes using
multiplicative weights update method.
Prathyush Sambaturu, Marco Minutoli,
Mahantesh Halappanavar, Ananth Kalyanaraman, Anil Vullikanti.
Proc. International Joint
Conference on Artificial Intelligence (IJCAI-ECAI), Special
track on AI for Good, Accepted, 2022.
BOA: A partitioned view of
genome assembly.
Xiaojing An, Priyanka Ghosh, Patrick Keppler, Sureyya Emre
Kurt, Sriram Krishnamoorthy, Ponnuswamy Sadayappan, Aravind S. Rajam,
Umit Catalyurek, Ananth
Kalyanaraman.
iScience journal, Cell Press, 2022.
(Conference version appeared in the proceedings track of RECOMB-SEQ
2022)
Softtware/Hardware co-design
of 3D NoC-based GPU architectures for accelerated graph
computations.
Dwaipayan Choudhury, Reet Barik, Aravind S. Rajam, Ananth
Kalyanaraman, Partha Pande.
IEEE Transactions on Design Automation of Electronic Systems
(TODAES), Accepted/In press, 2022. DOI: 10.1145/3514354.
Towards scaling community
detection on distributed-memory heterogeneous systems.
Nitin Gawande, Sayan Ghosh, Mahantesh Halappanavar, Antonino
Tumeo, Ananth Kalyanaraman.
Parallel computing (PARCO), Accepted/In press, 2022. DOI:
10.1016/j.parco.2022.102898.
(2021)
High-performance and energy-efficient 3D manycore GPU architecture for accelerating graph analytics.(2020)
PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems.
Interesting paths in the Mapper
Complex.
A.
Kalyanaraman, M. Kamruzzaman, B. Krishnamoorthy.
Journal of Computational Geometry (JoCG),
In Press, 2019, arXiV preprint arXiv:1712.10197. DOI:
10.20382/jocg.v10i1a17.
preprint
Hyppo-X: A scalable exploratory
framework for analyzing complex phenomics data.
M. Kamruzzaman, A. Kalyanaraman, B.
Krishnamoorthy, S. Hey, P. Schnable.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
(TCBB), 14 pages, In Press, 2019. DOI:
10.1109/TCBB.2019.2947500
PDF
Scaling and quality of modularity
optimization methods for graph clustering.
S. Ghosh, M. Halappanavar, A. Tumeo,
A. Kalyanaraman.
Proc. IEEE High Performance Extreme Computing (HPEC'19), 6
pages, 2019. DOI: 10.1109/HPEC.2019.8916299.
IEEE HPEC/MIT Graph Challenge: Innovation Award winning
entry.
PDF
Fast and scalable implementations of
influence maximization algorithms.
M. Minutoli, M. Halappanavar, A.
Kalyanaraman, A. Sathanur, R. Mcclure, J. McDermott.
Proc. IEEE Cluster conference (CLUSTER'19), 12 pages, 2019.
DOI: 10.1109/CLUSTER.2019.8890991.
PDF
NoC-enabled software/hardware
co-design framework for accelerating k-mer counting.
B.K. Joardar, P. Ghosh, P. Pande, A. Kalyanaraman, S.
Krishnamoorthy.
Proc. IEEE/ACM International Symposium on Networks-on-Chip
(NOCS'19), pp. 1-8, 2019. DOI: 10.1145/3313231.3352367.
Best Paper Award
PDF
A visual analytics framework for
analysis of patient trajectories.
K.
Madhobi, M. Kamruzzaman, A. Kalyanaraman, E. Lofgren, R. Moehring,
B. Krishnamoorthy.
Proc. ACM Conference on Bioinformatics, Computational Biology,
and Health Informatics (ACM-BCB'19), pp. 15-24, 2019.
DOI: 10.1145/3307339.3342143.
PDF
(A shorter unarchived version will be presented at the KDD'19
epiDAMIK workshop.)
A
fast and efficient incremental approach toward dynamic community
detection.
N. Zarayeneh, A. Kalyanaraman.
Proc. 2019 IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining (ASONAM'19), pp. 9-16, 2019.
DOI: 10.1145/3341161.3342877.
PDF
PaKman: Scalable assembly of
large genomes on distributed memory machines.
P. Ghosh, S. Sriram, A. Kalyanaraman.
Proc. IEEE International Parallel and Distributed Processing
Symposium (IPDPS'19), pp. 578-589, 2019.
DOI: 10.1109/IPDPS.2019.00067.
PDF
Exploring MPI communication models
for graph applications using graph matching as a case study.
S. Ghosh, M. Halappanavar, A.
Kalyanaraman, A. Khan, A. Gebremedhin.
Proc. IEEE International Parallel and Distributed Processing
Symposium (IPDPS'19), pp. 761-770, 2019.
DOI: 10.1109/IPDPS.2019.00085.
PDF
A
brief survey of algorithms, architectures, and challenges toward
extreme-scale graph analysis.
A. Kalyanaraman, P. Pande.
Proc. 2019 Design, Automation, Test in Europe conference (DATE'19),
pp. 1307-1312, 2019.
DOI: 10.23919/DATE.2019.8715024.
PDF
(2018)
miniVite:
A graph analytics benchmarking tool for massively
parallel systems.
S.
Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, A.
Gebremedhin.
Proc.
IEEE International Workshop on Performance Modeling,
Benchmarking and Simulation (PMBS'18), held in conjunction
with ACM/IEEE Supercomputing (SC'18), pp. 51-56, 2018.
DOI: 10.1109/PMBS.2018.8641631.
PDF
Scalable
distributed memory community detection using Vite.
S.
Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, A.
Gebremedhin.
Proc.
IEEE High Performance Extreme Computing (HPEC'18), pp. 1-7,
2018.
DOI: 10.1109/HPEC.2018.8547534.
2018 IEEE
HPEC/MIT Graph Challenge: Innovation Award winner.
preprint
Detecting
divergent subpopulations in phenomics data using interesting flares.
M.
Kamruzzaman, A. Kalyanaraman, B. Krishnamoorthy.
Proc. ACM
Conference on Bioinformatics, Computational Biology, and Health
Informatics (ACM-BCB),
pp. 155-164, 2018.
PDF
Exploiting
intra-type information in bipartite community detection.
Paola
Pesantez-Cabrera, Ananth Kalyanaraman and Mahantesh Halappanavar.
Proc. SIAM Network Science workshop, (accepted as a short
paper), p.2, 2018.
preprint
Alignment-free
clustering of large data sets of unannotated protein conserved
regions Using MinHashing.
A. Abnousi, S.L.
Broschat, A. Kalyanaraman.
BMC
Bioinformatics, vol. 19,
no. 1, p. 83, 2018.
doi:
10.1186/s12859-18-2080-y
PDF
Distributed Louvain algorithm for
graph community detection.
S. Ghosh, M. Halappanavar, A. Tumeo, A.
Kalyanaraman, H. Lu, D. Chavarria-Miranda, A. Khan, A. Gebremedhin.
Proc. IEEE International Parallel and Distributed Processing
Symposium (IPDPS), pp. 885-895, 2018.
DOI: 10.1109/IPDPS.2018.00098.
PDF
Efficient detection of communities in
biological bipartite networks.
P. Pesantez,
A. Kalyanaraman.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
(TCBB), 16(1):258-271, 2017.
doi:
10.1109/TCBB.2017.2765319
preprint
FastEtch: A fast sketch-based
assembler for genomes.
P.Ghosh, A.
Kalyanaraman.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
(TCBB), In Press, 2017.
doi: 10.1109/TCBB.2017.2737999
preprint
Approximate computing techniques for
iterative graph algorithms.
A. Panyala, O. Subasi, M. Halappanavar, A. Kalyanaraman, D.
Chavarria-Miranda, S. Krishnamoorthy.
Proc. IEEE International Conference on High Performance Computing,
Data, and Analytics (HiPC'17), pp.23-32, 2017.
doi: 10.1109/HiPC.2017.00013
PDF
Scalable static and dynamic community
detection using Grappolo.
M. Halappanavar, H. Lu, A. Kalyanaraman, A. Tumeo.
2017 IEEE
HPEC/DARPA/MIT Graph Challenge Champion.
Proc. IEEE High Performance Extreme Computing (HPEC'17), pp.
1-6, 2017.
PDF
Accelerating Graph Community
Detection with Approximate Updates via an Energy-Efficient NoC.
K. Duraisamy,
H. Lu, P. Pande, A. Kalyanaraman.
Proc. Design Automation Conference (DAC), p.89, June 18-22,
2017.
doi: 10.1145/3061639.3062194.
PDF
Detecting Communities in Biological
Bipartite Networks.
P. Pesantez,
A. Kalyanaraman.
Proc. ACM Conference on Bioinformatics, Computational Biology, and
Health Informatics (ACM-BCB), pp. 98-107, 2016.
doi: 10.1145/2975167.2975177.
PDF
A Fast Sketch-based Assembler for
Genomes.
P. Ghosh, A.
Kalyanaraman.
Proc. ACM Conference on Bioinformatics, Computational Biology, and
Health Informatics (ACM-BCB), pp. 241-250, 2016.
doi:
10.1145/2975167.2975192.
Best Student Paper Award.
PDF
Characterizing the Role of
Environment on Phenotypic Traits using Topological Data
Analytics.
M.
Kamruzzaman, A. Kalyanaraman, B. Krishnamoorthy.
Proc. ACM Conference on Bioinformatics, Computational Biology, and
Health Informatics (ACM-BCB), pp. 487-488, 2016.
doi: 10.1145/2975167.2985646.
PDF
High performance and energy efficient
Network-on-Chip architectures for graph analytics.
K. Duraisamy,
H. Lu, P. Pande, A. Kalyanaraman.
ACM Transactions on Embedded Computing Systems (TECS), vol. 15,
no. 4, p. 66, 2016.
doi: 10.1145/2961027.
PDF
On the Impact of Widening Vector
Registers on Sequence Alignment.
J. Daily, A.
Kalyanaraman, S. Krishnamoorthy, B. Ren.
Proc. International Conference on Parallel Processing, pp.
506-515, 2016.
PDF
CisSERS: Cutomizable in silico Sequence
Evaluation
for Restriction Sites.
R. Sharpe, T.
Koepke, A. Harper, J. Grimes, M. Galli, M. Satoh-Cruz, A.
Kalyanaraman, K. Evans, D. Kramer, A. Dhingra.
PLOS ONE, 11(4):e0152404, 2016.
doi: 10.1371/journal.pone.015404.
PDF
Fast uncovering of graph communities
on a chip: Toward scalable community detection on multicore and
manycore platforms.
A. Kalyanaraman, M. Halappanavar, D. Chavarria-Miranda, H. Lu, K.
Duraisamy, P. Pande.
Foundations and Trends in Electronic Design Automation
(FnTEDA), Paperback 118 pages. now Publishers, ISBN-10: 1680831321,
ISBN-13: 978-1680831320, 2016.
Online
access
Fast SVD computations for
synchrophasor algorithms.
T. Wu, S.A.N.
Sarmadi, V. Venkatasubramanian, A. Pothen, A. Kalyanaraman.
IEEE Transactions on Power Systems, 31(2):1651-1652,
2016.
doi: 10.1109/TPWRS.2015.2412679
Online
access
(2015)
High performance and energy efficient
wireless NoC-enabled multicore architecture for graph analytics.
K. Duraisamy, H. Lu, P. Pande, A. Kalyanaraman.
Proc. International Conference on Compilers, Architectures and
Synthesis of Embedded Systems (CASES), pp. 147-156, 2015. Best Paper Finalist.
PDF
An
alignment-free approach to cluster proteins using frequency of
conserved k-mers.
A. Abnousi,
S.L. Broschat, A. Kalyanaraman.
Proc. ACM Conference on Bioinformatics, Computational Biology and
Health Informatics (ACM-BCB), pp. 597-606, 2015.
doi: 10.1145/2808719.2812223
PDF
On-Chip Network-Enabled Many-Core
Architectures for Computational Biology Applications.
T. Majumder,
P. Pande, A. Kalyanaraman.
Proc. Design, Automation and Test in Europe (DATE), 2015, pp.
259-264.
PDF
Parallel
heuristics for scalable community detection.
H. Lu, M.
Halappanavar, A. Kalyanaraman.
Parallel Computing, vol. 47, pp. 19-37, 2015.
doi: 10.1016/j.parco.2015.03.003
Online
access
Balanced coloring for parallel
computing applications.
H. Lu, M.
Halappanavar, D. Chavarria, A. Gebremedhin, A. Kalyanaraman.
Proc. IEEE International Parallel and Distributed Processing
Symposium (IPDPS), pp. 7-16, 2015.
doi: 10.1109/IPDPS.2015/113.
PDF
A work
stealing based approach for enabling scalable optimal sequence
homology detection.
J. Daily, A.
Kalyanaraman, S. Krishnamoorthy, A. Vishnu.
Journal of Distributed and Parallel Computing (JPDC),
Vol. 79-80, pp. 132-142, May 2015.
doi: 10.1016/j.jpdc.2014.08.009.
PDF
(2014)
Scaling graph community detection on
the Tilera Many-core architecture.
D. Chavarria,
M. Halappanavar, A. Kalyanaraman.
Proc. IEEE International Conference on High Performance Computing
(HiPC), December 17-20, 2014, Goa, India. 11 pages.
doi: 10.1109/HiPC.2014.7116708
PDF
BioEarth: Envisioning and developing
a new regional earth system model to inform natural and agricultural
resource management.
J. Adam et
al.
Climatic Change, pp. 1-17, 2014.
doi: 10.1007/s10584-014-1115-2
PDF
Parallel heuristics for scalable
community detection.
H. Lu, M.
Halappanavar, A. Kalyanaraman, S. Choudhury.
Proc. International Workshop on Multithreaded Architectures and
Applications (MTAAP), IPDPS Workshops, pp. 1375-1385,
2014.
PDF
Design and implementation of Kepler
workflows for BioEarth.
T. Mullis, M. Liu, A. Kalyanaraman, J. Vaughan, C. Tague, J. Adam.
Proc. 2014 International Conference on Computational Science (ICCS), Procedia
Computer
Science, vol. 29, pp. 1722-1732, 2014.
doi: 10.1016/j.procs.2014.05.157
PDF
Hardware Accelerators in
Computational Biology: Application, Potential and Challenges.
T. Majumder,
P.P. Pande, A. Kalyanaraman. IEEE Design and Test of Computers:
Special Issue on Hardware Acceleration, 31(1):8-18, 2014.
doi: 10.1109/MDAT.2013.2290118.
PDF
Parallel algorithms for clustering
biological graphs on distributed and shared memory architectures.
I. Rytsareva,
T. Chapman, and A. Kalyanaraman.
International Journal of High Performance Computing and Networking:
Special issue on Architectures and Algorithms for Irregular
Applications (IJHPCN), 7(4):241-257, 2014.
PDF
Wireless NoC platforms with dynamic
task allocation for maximum likelihood phylogeny reconstruction.
T. Majumder,
P.P. Pande, A. Kalyanaraman.
IEEE Design and Test of Computers, 31(3):54-64, 2014.
doi: 10.1109/MDAT.2013.2288778
PDF
(2013)
Computational challenges in stability
monitoring of power systems using large number of PMUs.
M.V.
Venkatasubramanian, A. Pothen, A. Kalyanaraman, D.J. Sobajic.
Proc. National Workshop on Energy Cyber-physical Systems,
organized by NSF, Arlington, VA, pp.1-3, 2013.
Empirical
analysis of space-filling curves for scientific computing
applications.
D. Deford, A. Kalyanaraman.
Proc. International Conference on Parallel Processing (ICPP), Lyon,
France,
pp. 170-179, 2013.
doi: 10.1109/ICPP.2013.26
PDF
Scalable heuristics for clustering
biological graphs.
I. Rytsareva, A. Kalyanaraman, K. Konwar, S. Hallam.
Proc. IEEE International Conference on Computational Advances in
Bio and Medical Sciences (ICCABS), pp. 1-6, 2013.
doi: 10.1109/ICCABS.2013.6629214
PDF
On clustering heterogeneous networks.
F. Poursabzi, A. Kalyanaraman.
Proc. SIAM Workshop on Network Science (NetSci13), (held in
conjunction with 2013 SIAM Annual Meeting), San Diego, pp.1-2, 2013.
preprint
Network-on-chip with long-range wireless links for high-throughput
scientific computation.
T. Majumder, P.P. Pande, A. Kalyanaraman.
Proc. 3rd Workshop on Communication Architecture for
Scalable Systems (CASS’13), held in conjunction with IPDPS'13,
pp. 781-790, 2013.
doi: 10.1109/IPDPSW.2013.72
PDF
GPU-accelerated protein family
identification for metagenomics.
C. Wu, A.
Kalyanaraman.
Proc. 12th IEEE International Workshop on High
Performance Computational Biology (HiCOMB’11), held in
conjunction with IPDPS'13, pp. 559-568, 2013. (invited paper).
doi:10.1109/IPDPSW.2013.185
PDF
Comparison
of
clustering algorithms: An example with proteomic data.
N. Dasgupta,
Y. Chen, A. Kalyanaraman, S. Daoud.
Advances and Applications in Statistics, 33(1):p63, 2013.
Online access
High-throughput,
energy-efficient
network-on-chip-based hardware accelerators.
T. Majumder,
P.P. Pande, A. Kalyanaraman.
Sustainable Computing: Informatics and Systems (SUSCOM),
3(1):36-46, 2013.
doi: 10.1016/j.suscom.2013.01.001.
PDF
(2012)
Towards Scalable Optimal Sequence
Homology Detection.
J. Daily, S.
Krishnamoorthy, and A. Kalyanaraman.
Proc. Workshop on Parallel Algorithms and Software for Analysis of
Massive Graphs (ParGraph'12), pp.1-8, 2012.
doi: 10.1109/HiPC.2012.6507523
PDF
Evaluating socio-technical
coordination in open-source communities: A cluster-based approach.
I. Rytsareva,
Q. Le, E. Conner, A. Kalyanaraman, J. Panchal.
Proc. ASME International Design Engineering Technical Conferences
& Computers and Information in Engineering Conference
(IDETC/CIE), pp.2777-286, 2012.
doi: 10.1115/DETC2012-70604
PDF
On-Chip network-enabled multi-core
platforms targeting maximum likelihood phylogeny reconstruction.
T. Majumder,
M. Borgens, P.P. Pande, A. Kalyanaraman.
IEEE Transactions on Computer-Aided Design of Integrated Circuits
and Systems (TCAD), 31(7):1061-1073, 2012.
doi: 10.1109/TCAD.2012.2188401
PDF
pGraph:
Efficient
parallel construction of large-scale protein sequence homology
graphs.
C. Wu, A.
Kalyanaraman, W.R. Cannon.
IEEE Transactions on Parallel and Distributed Systems (TPDS),
23(10):1923-1933, 2012.
doi: 10.1109/TPDS.2012.19
PDF (suppl.
material available on publisher's website)
Proteotyping of
microbial communities using high performance optimization of
proteome-spectra matches.
A. Hugo, D.J.
Baxter, W.R. Cannon, A. Kalyanaraman, G. Kulkarni, S.J. Callister.
Proc. Pacific Symposium on Biocomputing (PSB), 2012.
doi: 10.1142/9789814366496_0022
PDF
NoC-based hardware accelerator for
breakpoint phylogeny.
T. Majumder, P. Pande, A. Kalyanaraman.
IEEE Transactions on Computers, 2012, 61(6):857-869.
doi: 10.1109/TC.2011.100
PDF
(2011)
An efficient MapReduce algorithm for
parallelizing large-scale graph clustering.
I. Rytsareva,
A. Kalyanaraman.
ParGraph - Workshop on Parallel Algorithms and Software for
Analysis of Massive Graphs, Held in conjunction with
HiPC'11, India, p.1-9, 2011.
PDF
An OpenMP algorithm and
implementation for clustering biological graphs.
T. Chapman,
A. Kalyanaraman.
IA3 - Workshop on Irregular Applications: Architectures
& Algorithms (Held in conjunction with SC'11), Seattle, WA,
pp. 3-10, 2011.
doi: 10.1145/2089142.2089146
PDF
MapReduce
implementation of a hybrid spectral library-database search
method for large-scale peptide identification.
A.
Kalyanaraman, W.R. Cannon, B. Latt, D.J. Baxter.
Bioinformatics, 2011, 27(21):3072-3073.
doi:10.1093/bioinformatics/btr523.
PDF (suppl. material available on
publisher's website)
Accelerating Maximum Likelihood based
phylogenetic kernels using Network-on-chip.
T. Majumder,
P. Pande, A. Kalyanaraman.
Proc. International Symposium on Computer Architecture and High
Performance Computing (SBAC-PAD), pp. 17-24, 2011.
doi: 10.1109/SBAC-PAD.2011.17
PDF
Attenuation of
virulence in an Apicomplexan hemoparasite results in reduced genomic
diversity at the population level.
A.O.T. Lau,
A. Kalyanaraman, I. Echaide, G.H. Palmer, R. Bock, M.J. Pedroni, M.
Rameshkumar, M.B. Ferreira, T.I. Fletcher, T.F. McElwain.
BMC Genomics. 12:410, 2011.
doi:10.1186/1471-2164-12-410
PDF
Genome assembly.
A. Kalyanaraman.
Encyclopedia of Parallel Computing, D. Padua (ed.), Springer
Science+Business Media LLC, pp. 755-768, 2011.
doi: 10.1007/978-0-387-09766-4
PDF
(2010)
The genome of the
domesticated apple (Malus domestica Borkh.).
R. Velasco,
A. Zharkikh, J. Affourtit, A. Dhingra, A. Cestaro, A. Kalyanaraman, P.
Fontana et al. (expanded
author
list)
Nature Genetics, 42:833-839, 2010.
doi:10:1038/ng.654
PDF
A scalable parallel algorithm for
large-scale protein sequence homology detection.
C. Wu, A.
Kalyanaraman, W. Cannon.
Proc. International Conference on Parallel Processing (ICPP),
2010, pp. 333-342.
doi: 10.1109/ICPP.2010.41
PDF
An optimized NoC architecture for
accelerating TSP kernels in breakpoint median problem.
T. Majumder,
S. Sarkar, P. Pande, A. Kalyanaraman.
Proc. IEEE International Conference on Application-specific
Systems, Architectures and Processors (ASAP), pp. 89-96, 2010.
doi: 10.1109/ASAP.2010.5540797
PDF
Hardware accelerators for
biocomputing: A survey.
S. Sarkar, T. Majumder, A. Kalyanaraman, P. Pande.
Proc. IEEE International Symposium on Circuits and Systems (ISCAS),
pp. 3789-3792, 2010.
doi: 10.1109/ISCAS.2010.5537736
PDF
Genome sequencing
and analysis of the model grass Brachypodium distachyon.
The International Brachypodium Initiative. Nature, 463,
763-768, 2010.
(expanded
author
list)
doi:10.1038/nature08747
PDF
Network-on-chip hardware accelerators
for biological sequence alignments.
S. Sarkar, G. Kulkarni, P. Pande, A. Kalyanaraman.
IEEE Transactions on Computers, 59(1):29-41, 2010.
doi: 10.1109/TC.2009.133
PDF
(2009)
Using clouds for for data-intensive
computing in proteomics.
A. Kalyanaraman, D. Baxter, W. Cannon.
Proc. Workshop on Using Clouds for Parallel Computations in Systems
Biology, held in conjunction with SC|09, Portland,
OR, November 16, 2009.
PDF
The B73 Maize
Genome: Complexity, diversity and dynamics.
P.S. Schnable
et al. (expanded author list)
Science, 326(5956):1112-1115, 2009.
doi: 10.1126/science.1178534
PDF
Detailed
analysis of a contiguous 22-Mb region of the maize genome.
F. Wei et
al. (expanded
author
list)
PLoS Genetics, 5(11):e1000728, 2009.
doi:10.1371/journal.pgen.1000728
PDF
DNAjig: A new approach for building
DNA nanostructures.
Md. Muksitul
Haque, A. Kalyanaraman, A. Dhingra, N. Abu-lail, K. Graybeal.
Proc. IEEE International Conference on Bioinformatics &
Biomedicine (BIBM), Washington D.C., November 1-4, pp. 379-383,
2009.
doi: 10.1109/BIBM.2009.71
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A scalable parallel approach for
peptide identification from large-scale mass spectrometry data.
G. Kulkarni,
A. Kalyanaraman, W. Cannon, D. Baxter.
Proc. International Conference on Parallel Processing Workshops
(ICPP-W), pp. 423-430, September 22-25, 2009.
doi: 10.1109/ICPPW.2009.41
PDF
(2008)
An efficient parallel approach for
identifying protein families in large-scale metagenomic data sets.
C. Wu, A.
Kalyanaraman.
Proc. ACM/IEEE conference on Supercomputing (SC|08), Austin,
TX, November 15-21, pp. 1-10, 2008, ISBN 978-1-4244-2835-9, IEEE
Press, Piscataway, NJ, USA.
PDF
(2007)
Assembling genomes on large-scale
parallel computers.
A.
Kalyanaraman, S.J. Emrich, P.S. Schnable, S. Aluru.
Journal of Parallel and Distributed Computing (JPDC),
67(12):1240-1255, 2007.
PDF
Massively parallel clustering of
Expressed Sequence Tags.
S.J. Emrich, A. Kalyanaraman, S. Aluru.
Proc. ISCA 20th International Conference on Parallel and
Distributed Computing Systems (PDCS'07), Las Vegas,
NV, September 24-26, 2007.
PDF
(2006)
Efficient algorithms and software for
detection of full-length LTR retrotransposons.
A. Kalyanaraman, S. Aluru. Journal of Bioinformatics and
Computational Biology (JBCB), 4(2):197-216, 2006.
PDF
Turning repeats to advantage:
Scaffolding genomic contigs using LTR retrotransposons.
A. Kalyanaraman, S. Aluru, P.S. Schnable.
Proc. LSS Computational Systems Bioinformatics (CSB'06),
167-178, 2006.
PDF
Assembling genomes on large-scale
parallel computers.
A.
Kalyanaraman, S.J. Emrich, P.S. Schnable, S. Aluru.
Proc. IEEE International Parallel and Distributed Processing
Symposium (IPDPS'06), 2006. (Best
Paper Award)
PDF
(2005
or
earlier)
Efficient algorithms and software for
detection of full-length LTR retrotransposons.
A. Kalyanaraman, S. Aluru.
IEEE Computational Systems Bioinformatics Conference (CSB'05),
pp.
56-64, 2005. (Best Paper
Award)
PDF
Expressed Sequence Tags: Clustering
and applications.
A. Kalyanaraman, S. Aluru.
In Handbook of
Computational Molecular Biology,
Edited by S. Aluru, Chapman & Hall/CRC Computer and Information
Science Series, 2005.
Algorithms for large-scale clustering
and assembly of biological sequence data.
S. Emrich, A.
Kalyanaraman, S. Aluru.
In Handbook of
Computational Molecular Biology,
Edited by S. Aluru, Chapman & Hall/CRC Computer and Information
Science Series, 2005.
A survey of SL1-spliced transcripts
from the root-lesion nematode Pratylenchus penetrans.
M. Mitreva, A.A. Elling, M. Dante, A.P. Kloek, A. Kalyanaraman, S.
Aluru, S.W. Clifton, D.M. Bird, T.J. Baum, J.P. McCarter.
Molecular Genetics and Genomics (MGG), 272:138-148,
2004.
PDF
Space-conserving optimal DNA-protein
alignment.
P. Ko, M.
Narayanan, A. Kalyanaraman, S. Aluru.
Proc. IEEE Computational Systems Bioinformatics Conference (CSB'04),
pp.
77-85, 2004.
PDF
Space and time efficient parallel
algorithms and software for EST clustering.
A. Kalyanaraman, S. Aluru, V. Brendel, S. Kothari.
IEEE Transactions on Parallel and Distributed Systems (TPDS),
14(12):1209-1221, 2003.
PDF
Efficient clustering of large EST
data sets on parallel computers.
A. Kalyanaraman, S. Aluru, S. Kothari, V. Brendel.
Nucleic Acids Research (NAR), 31(11):2963-2974, 2003.
PDF
Space and time efficient parallel
algorithms and software for EST clustering.
A.
Kalyanaraman, S. Aluru, S. Kothari.
Proc. International Conference on Parallel Processing (ICPP'02),
331-339,
2002.
PDF
Parallel EST clustering.
A.
Kalyanaraman, S. Aluru, S. Kothari.
Proc. First International Workshop on High Performance
Computational Biology (HiCOMB'02), held in conjunction
with IPDPS '02, 2002.
PDF
Ph.D. DISSERTATION
Title: Large-scale methods in computational
genomics
Graduation date: Summer 2006
Advisor: Prof. Srinivas Aluru
Institution: Iowa State University
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