Ananth Kalyanaraman @ WSU

Peer-reviewed Publications

(in reverse chronological order)

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(2024)

An Efficient Parallel Sketch-based Algorithmic Workflow forMapping Long Reads.
Tazin Rahman, Oieswarya Bhowmik, Ananth Kalyanaraman.
ACM/IEEE Transactions on Computational Biology and Bioinformatics (TCBB), Accepted/in press, 2024.
Preprint PDF

A multi-task learning approach for predicting spatio-temporal patient variables.
Kaniz Madhobi, Eric Lofgren, Ananth Kalyanaraman.
Proc. 15th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB '24), Accepted, 2024.

Maptcha: An efficient parallel workflow for hybrid genome scaffolding.

Oieswarya Bhowmik, Tazin Rahman, Ananth Kalyanaraman.
BMC Bioinformatics, 25(1):263, 2024. DOI: 10.1186/s12859-024-05878-4.

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Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach.

Mohammed Amine Gharsallaoui, Bhupinderjeet Singh, Supriya Savalkar, Aryan Deshwal, Yan Yan, Ananth Kalyanaraman, Kirti Rajagopalan, Janardhan Rao Doppa.
Prov. Thirty-third International Joint Conference on Artificial Intelligence (IJCAI), pp. 7269-7277, 2024. DOI: 10.24963/ijcai.2024/804.
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A data-flow aware network-on-interposer for CNN inferencing in the presence of defective chiplets.
Harsh Sharma, Umit Ogras, Ananth Kalyanaraman, Partha Pande.
Proc. International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES, ESWEEK), Accepted, 2024.

FuseIM: Fusing probabilistic traversals for influence maximization on exascale systems.
Reece Neff, Mostafa Zarch, Marco Minutoli, Mahantesh Halappanavar, Antonino Tumeo, Ananth Kalyanaraman, Michela Becchi.
ACM International Conference on Supercomputing (ICS), pp. 38-49, 2024. DOI: 10.1145/3650200.36566.
Best paper finalist.
Preprint

AgAID Institute - AI for agricultural labor and decision support.
Alan Fern, Margaret Burnett, Joe Davidson, Jana Doppa, Paola Pesantez-Cabrera, Ananth Kalyanaraman.
AI Magazine, pp. 1-6, 2024, DOI: 10.1002/aaai.12156.
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Analysis of cytosine deamination events in excision repair-sequencing reads reveals mechanisms of incision site selection in NER.
Benjamin Morledge-Hampton, Ananth Kalyanaraman, John Wyrick.

Nucleic Acids Research (NAR), Volume 52, Issue 4, pp. 1720-1735, 2024. DOI: 10.1093/nar/gkad1195.
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FARe: Fault-Aware GNN Training on ReRAM-based PIM Accelerators.
Pratyush Dhingra, Chukwufumnanya Ogbogu, Biresh Kumar Joardar, Jana Doppa, Ananth Kalyanaraman and Partha Pande.
Proc. Design, Automation and Test in Europe Conference (DATE), Accepted, 2024.
Preprint

Attention-based models for snow-water equivalent prediction.
Krishu Thapa, Bhupinderjeet Singh, Supriya Savalkar, Alan Fern, Kirti Rajagopalan, Ananth Kalyanaraman.
Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), pp. 22969-22975, 2024. DOI: 10.1609/aaai.v38i21.30337.
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(2023)

Florets for Chiplets: Data flow-aware high-performance and energy-efficient Network-on-Interposer for CNN inference tasks.
Harsh Sharma, Lukas Pfromm, Rasit Onur Topaloglu, Jana Doppa, Umit Ogras, Ananth Kalyanaraman, Partha Pande.
Proc. 2023 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS, ESWEEK), 22(5s):1-21, 2023. DOI: 10.1145/3608098.

Best paper award.
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An efficient parallel sketch-based algorithm for mapping long reads to contigs.
Tazin Rahman, Oieswarya Bhowmik, Ananth Kalyanaraman.
Proc. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (HiCOMB), pp. 157-166, 2023. DOI: 10.1109/IPDPSW59300.2023.00037.
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Persistent homology to study cold hardiness of grape cultivars.
Sejal Welankar, Paola Pesantez, Bala Krishnamoorthy, Lynn Mills, Markus Keller, Ananth Kalyanaraman.
Proc. 2023 AAAI Workshop on Agriculture and Food Systems (AIAFS), 4 pages, 2023. DOI: 10.48550/arXiv.2302.05600.
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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.
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(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.
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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.
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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.
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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.
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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.
Dwaipayan Choudhury, Aravind S. Rajam, Ananth Kalyanaraman, Partha Pande.
Journal of Emerging Technologies in Computing Systems (JETC), Accepted/In press, 2021.

Single-node partitioned-memory for huge graph analytics: Cost and performance tradeoffs.
Sayan Ghosh, Nathan Tallent, Marco Minutoli, Mahantesh Halappanavar, Ramesh Peri, Ananth Kalyanaraman.
IEEE/ACM international Conference for High Performance Computing, Networking, Storage, and Analysis (SC'21), pp. 1-14, 2021.

Pheno-Mapper: An Interactive Toolbox for the Visual Exploration of Phenomics Data.
Youjia Zhou, Methun Kamruzzaman*, Patrick Schnable, Bala Krishnamoorthy, Ananth Kalyanaraman, Bei Wang.
Proc. ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB'21), pp. 1-10, 2021. DOI: 10.1145/3459930.3469511. PDF

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications.
Seher Acer, Ariful Azad, Erik G. Boman, Aydin Buluc, Karen D. Devine, Nitin Gawande, Sayan Ghosh, Mahantesh Halappanavar, Arif Khan, Ananth Kalyanaraman, Marco Minutoli, Alex Pothen, Sivasankaran Rajamanickam, Oguz Selvitopi, Nathan Tallent, Antonino Tumeo.
International Journal of High Performance Computing Applications (IJHPCA), 35(6):553-571, 2021.

Delta-Screening: A fast and efficient technique to update communities in dynamic graphs.
N. Zarayeneh, A. Kalyanaraman.
IEEE Transactions on Network Science and Engineering (TNSE), Accepted/In press, 16 pages, 2021. DOI: 10.1109/TNSE.2021.3067665.

PaKman: A scalable algorithm for generating genomic contigs on distributed memory machines.
P. Ghosh, S. Krishnamoorthy, A. Kalyanaraman.
IEEE Transactions on Parallel and Distributed Processing Systems (TPDS), vol. 32, no. 5, pp. 1191-1209, 2021. DOI: 10.1109/TPDS.2020.3043241.

(2020)
PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems.
M. Minutoli, P. Sambaturu, M. Halappanavar, A. Tumeo, A. Kalyanaraman, A. Vullikanti.
IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'20), pp. 765-779, 2020. DOI: 10.1109/SC41405.2020.00059.

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Vertex Reordering for Real-world Graphs and Applications: An Empirical Evaluation.
Reet Barik, Marco Minutoli, M. Halappanavar, N. Tallent, A. Kalyanaraman.
IEEE International Symposium on Workload Characterization (IISWC'20), pp. 240-251, 2020. DOI: 10.1109/IISWC50251.2020.00031.

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cuRipples: Influence Maximization on Multi-GPU Systems.
M. Minutoli, Maurizio Dracco, Mahantesh Halappanavar, Antonino Tumeo, and A. Kalyanaraman.
ACM International Conference on Supercomputing (ICS'20), pp. 1-11, 2020. DOI: 10.1145/3392717.3392750.
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(2019)

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
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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.
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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.
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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
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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.
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(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.
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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.
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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.
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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.
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(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.
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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.
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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
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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.
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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

(2017)

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
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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.
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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.
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Algorithms for Balanced Colorings with Applications in Parallel Computing.
H. Lu, M. Halappanavar, D. Chavarria-Miranda, A. Gebremedhin, A. Panyala, A. Kalyanaraman.
IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 5, pp. 1240-1256, May 1 2017.
doi: 10.1109/TPDS.2016,2620142.
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(2016)

A Fast Alignment-Free Approach for de novo Detection of Protein Conserved Regions.
A. Abnousi, S.L. Broschat, A. Kalyanaraman.
PLOS ONE, 11(8), p.e0161338, 2016.
doi: 10.1371/journal.pone.0161338.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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(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
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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
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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.
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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
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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.
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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.
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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
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(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
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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
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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. 3^rd 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
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GPU-accelerated protein family identification for metagenomics.
C. Wu, A. Kalyanaraman.
Proc. 12^th 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
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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.
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(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
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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
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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
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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
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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
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(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.
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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
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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
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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
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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
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(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
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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
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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
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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
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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
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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
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(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.
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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
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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
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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
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(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.
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An information theoretic approach for the discovery of irregular and repetitive patterns in genomic data.
W. Davis, A. Kalyanaraman, D. Cook.
Proc. IEEE Computational Intelligence in Bioinformatics and Bioengineering (CIBCB'08), Sun Valley, ID, September 15-17, 2008.
doi: 10.1109/CIBCB.2008.4675756
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(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.
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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.
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(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.
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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.
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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)
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(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)
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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.
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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.
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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.
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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.
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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.
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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.
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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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