1@hpcgarage Georgia Institute of Technology
School of Computational Science and Engineeringrichie@cc.gatech.edu https://vuduc.org 

RichaRd (Rich) Vuduc
Note: I apologize for the absurd length of this CV. This format is required by the
Georgia Tech processes for promotion and tenure, and I am overwhelmed with lethargy
at the thought of maintaining multiple CVs. Thanks for your understanding!
Contents

1

I

Earned Degrees

1

II Employment History

1

III Honors and Awards

2

IV Research, Scholarship, and Creative Activities
IV-A Published Books, Book Chapters, and Edited Volumes IV-B Refereed Publications and Submitted Articles IV-C Other Publications and Creative Products
IV-D Presentations IV-E Grants and Contracts

3

V Education
V-A Courses Taught V-B Individual Student Guidance
novations and Other Contributions

32
V-C Educational In-

VI Service
VI-A Professional Contributions VI-B Public and Community Service
stitute Contributions46
VI-C In-I Earned Degrees
Ph.D., Computer Science, University of California, Berkeley, 2004.
Dissertation: Automatic performance tuning of sparse matrix kernels [PDF]
Advisors: James W. Demmel (main) and Katherine A. Yelick (co-advisor)

B.S. with honors, Computer Science, Cornell University, 1997.

II Employment History
Professor, School of Computational Science and Engineering, Georgia Institute of Technology, Aug. 2019–present.
Co-Director, CRNCH (Center for Research into Novel Computing Hierarchies), Jan. 2021–
present.
Associate Professor with tenure, ibid., Aug. 2013-2019.
Director of CSE Graduate Programs, ibid., Aug. 2013–Dec. 2016.

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2Associate Chair for Academic Affairs, ibid., Aug. 2013–Dec. 2016.
Assistant Professor, ibid., Aug. 2007–Jul. 2013.
Postdoctoral Scholar, Lawrence Livermore National Laboratory, Nov. 2004–Jul. 2007.
Advisor: Daniel J. Quinlan

Postdoctoral Scholar, University of California, Berkeley, Jan. 2004–Oct. 2004.
Advisors: James W. Demmel and Katherine A. Yelick

III Honors and Awards
24. November 2022: Finalist, Gordon Bell Prize, ACM/IEEE Conf. Supercomputing(SC)
23. June 2022: Finalist, Best paper, IEEE Parallel and Distributed Processing Symposium (IPDPS)
22. April 2022: Georgia Tech College of Computing, Outstanding Senior Faculty Research Award
21. February 2022: Winner, Best Paper Prize, SIAM Activity Group on Supercomputing (SIAG/SC)
20. September 2021: Finalist, R&D 100 Award (announcement)
19. November 2020: Finalist, Gordon Bell Prize, ACM/IEEE Conf. Supercomputing(SC)
18. October 2020: Finalist, edX Prize (announcement)
17. November 2018: Winner, Best student paper (lead author: Jiajia Li), ACM/IEEE
Conf. Supercomputing (SC)16. May 2015: Winner, Best paper (software track) at the IEEE Int’l. Parallel andDistributed Processing Symp. (IPDPS).
15. April 2013: Lockheed Martin Excellence in Teaching Award
14. April 2012: Winner, Best Paper at the SIAM Conference on Data Mining (SDM)
13. November 2010: Winner, Gordon Bell Prize at ACM/IEEE Conf. Supercomputing (SC)12. April 2010: College of Computing, Outstanding Junior Faculty Research Award
11. April 2010: Winner, Best paper (software track) at the IEEE Int’l. Parallel andDistributed Processing Symp. (IPDPS).
10. January 2010: NSF CAREER Awardee.
9. November 2009: Finalist, Best paper at SC
8. November 2009: Winner, R&D 100 Award for ROSE (joint with Lawrence Livermore National Laboratory)
7. April 2009: Panelist, DARPA Computer Science Study Panel (CSSP). One of twelve
panelists selected nationally among untenured US citizen professors.
6. July 2006: Winner, Best paper at Parallel and Distributed Testing and Debugging(PADTAD)
5. September 2004: Winner, Best paper at Int’l. Conf. Parallel Processing (ICPP)
4. June 2002: Winner, Best student presentation at Workshop on Performance Optimization of High-level Languages and Libraries, joint with the ACM Int’l. Conf.
Supercomputing (ICS)[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

3
3. June 2002: Winner, Best student paper at Workshop on Performance Optimization of High-level Languages and Libraries, joint with ICS
2. November 2002: Finalist, Best student paper at SC
1. December 2000: Winner, Best presentation at Workshop on Feedback-directed
Dynamic Optimization (FDDO), joint with the IEEE Int’l. Symp. Microarchitecture (MICRO)IV Research, Scholarship, and Creative Activities
The people whose names appear in boldface were students at the time the work was
carried out.
For citation counts per web-based indexes, see:
1. Google Scholar: http://scholar.google.com/citations?user=CCGI7x4AAAAJ&hl=en
2. Microsoft Academic Search: https://academic.microsoft.com/profile/0439fg93-jh42-4i8i-e648-eg4548293833/
RichardVuduc/

IV-A Published Books, Book Chapters, and Edited Volumes
IV-A.1 Books[B1] H. Kim, R. Vuduc, Sara Baghsorkhi, Jee Choi, and W.-m. Hwu, Performance
analysis and tuning for general purpose graphics processing units (GPGPU) (Synthesis Lectures on Computer Architecture), M. D. Hill, Ed. San Rafael, CA, USA:
Morgan & Claypool Publishers, Nov. 2012. doi: 10.2200/S00451ED1V01Y201209CAC020.
IV-A.2 Refereed Book Chapters(No data)
IV-A.3 Edited Volumes[EV1] Jee Choi and R. Vuduc, “A brief history and introduction to GPGPU,” in Modern Accelerator Technologies for Geographic Information Science, X. Shi, V. Kindratenko, and C. Yang, Eds., Boston, MA, USA: Springer, 2013, ch. 1, pp. 9–23.
doi: 10.1007/978-1-4614-8745-6_2.
[EV2] Seunghwa Kang, Nitin Arora, Aashay Shringarpure, R. W. Vuduc, and
D. A. Bader, “Evaluating multicore processors and accelerators for dense numerical computations,” in Multicore computing: Algorithms, architectures, and applications, S. Rajasekaran, L. Fiondella, M. Ahmed, and R. A. Ammar, Eds., Chapman and Hall/CRC, 2013. [Online]. Available: http : / / www . crcpress . com /product/isbn/9781439854341.
[EV3] R. W. Vuduc, “Autotuning (definition),” in Encyclopedia of Parallel Computing,
D. Padua, Ed., Springer, 2011. [Online]. Available: http://www.springer.com/
computer/swe/book/978-0-387-09765-7.

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4[EV4] S. Williams, N. Bell, Jee Choi, M. Garland, L. Oliker, and R. Vuduc, “Sparse
matrix vector multiplication on multicore and accelerator systems,” in Scientific
Computing with Multicore Processors and Accelerators, J. Kurzak, D. A. Bader, and
J. Dongarra, Eds., CRC Press, 2010.

IV-B Refereed Publications and Submitted Articles
IV-B.1 Published and Accepted Journal Articles(Papers [J20]–[J26] arefor work prior to myjoining Georgia Tech.)[J1] R. W. Vuduc, “Jack, the Autotuner,” IEEE Computing in Science and Engineering(CiSE), 2022, Invited article. doi: 10.1109/MCSE.2022.3212614.
[J2] Nicole Prindle, Ali Kazmi, Aman Jain, Albert Chen, Marissa Sorkin,
Sudhanshu Agarwal, R. Vuduc, and Vijay Thakkar, “Critique of “MemXCT:
Memory-centric X-ray CT reconstruction with massive parallelization” by SCC
Team from Georgia Tech,” IEEE Transactions on Parallel and Distributed Systems(TPDS), vol. 33, no. 9, Sep. 2022. doi: 10.1109/TPDS.2021.3108956.
[J3] Srinivas Eswar, R. Kannan, R. Vuduc, and H. Park, “ORCA: Outlier detection and Robust Clustering for Attributed graphs,” J. Global Optimization (JOGO),
2021. doi: 10.1007/s10898-021-01024-z.
[J4] E. Hein, Srinivas Eswar, Abdurrahman Yaşar, J. Li, J. S. Young, T. Conte,
Ü. Çatulyürek, R. Vuduc, J. Riedy, and B. Uçar, “Programming strategies for
irregular algorithms on the Emu Chick,” ACM Transactions on Parallel Computing(TOPC), vol. 7, no. 4, p. 25, Oct. 2020. doi: 10.1145/3418077.
[J5] Zhihao Li, H. Jia, Y. Zhang, Tun Chen, L. Yuan, and R. Vuduc, “Automatic
generation of high-performance FFT kernels on Arm and x86 CPUs,” IEEE
Transactions on Parallel and Distributed Systems (TPDS), vol. 31, no. 8, pp. 1925–
1941, Aug. 2020. doi: 10.1109/TPDS.2020.2977629.
[J6] J. S. Young, E. Hein, Srinivas Eswar, Patrick Lavin, J. Li, J. Riedy, R. Vuduc,
and T. Conte, “A microbenchmark characterization of the Emu Chick,” J. Parallel Computing (ParCo), vol. 87, pp. 60–69, Sep. 2019, Extends AsHES’18 workshop paper. doi: 10.1016/j.parco.2019.04.012.
[J7] P. Sao, X. S. Li, and R. Vuduc, “A communication-avoiding 3D algorithm forsparse LU factorization on heterogeneous systems,” J. Parallel and Distributed
Computing (JPDC), vol. 131, Sep. 2019. doi: 10 . 1016 / j . jpdc . 2019 . 03 . 004.
Winner, SIAG/SC Best Paper Prize (2022)[J8] Ioakeim Perros, E. E. Papalexakis, R. Vuduc, E. Searles, and J. Sun, “Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization,” Journal of Biomedical Informatics, 2019, (in press; available online). doi:
10.1016/j.jbi.2019.103125.
[J9] Yuchen Ma, Jiajia Li, C. Yang, J. Sun, and R. Vuduc, “Optimizing sparse tensor times matrix on GPUs,” J. Parallel and Distributed Computing (JPDC), 2018.
doi: 10.1016/j.jpdc.2018.07.018.

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5[J10] P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. K. Hollingsworth, B. Norris, and R. Vuduc, “Autotuning in high-performance computing applications,”
Proceedings of the IEEE, vol. 106, no. 11, pp. 2068–2083, Nov. 2018. doi: 10.1109/
JPROC.2018.2841200.
[J11] Mohammad M. Hossain, C. Nath, T. M. Tucker, R. W. Vuduc, and T. R. Kurfess, “A graphics processor unit-accelerated freeform surface offsetting method
for high-resolution subtractive three-dimensional printing (machining),” J. Manufacturing Science and Engineering, vol. 140, no. 4, p. 11, Apr. 2018. doi: 10.1115/
1.4038599.
[J12] Z. Du, R. Ge, V. W. Lee, R. Vuduc, D. A. Bader, and L. He, “Modeling thepower variability of core speed scaling on homogeneous multicore processors,”
Scientific Programming, vol. 2017, no. 8686971, p. 13, 2017. doi: 10.1155/2017/8686971.
[J13] Yang You, J. Demmel, Kenneth Czechowski, L. Song, and R. Vuduc, “Design and implementation of a communication-optimal classifier for distributed
kernel support vector machines,” IEEE Transactions on Parallel and Distributed
Systems (TPDS), vol. 28, no. 4, pp. 974–988, 2016, Extends conference version:
doi:10.1109/IPDPS.2015.117. doi: 10.1109/TPDS.2016.2608823.
[J14] Sangmin Park, R. Vuduc, and M. J. Harrold, “Unicorn: A unified approach for
localizing non-deadlock concurrency bugs,” Software: Testing, Verification, andReliability, vol. 25, no. 3, pp. 167–190, May 2015, Extends conference version:
doi:10.1109/ICST.2012.85. doi: 10.1002/stvr.1523.
[J15] Dongryeol Lee, Piyush Sao, R. Vuduc, and A. G. Gray, “A distributed kernel summation framework for general-dimension machine learning,” Statistical
Analysis and Data Mining, Oct. 2013, Journal version of doi:10.1137/1.9781611972825.34.
doi: 10.1002/sam.11207.
[J16] A. Rozga, T. Z. King, R. W. Vuduc, and D. L. Robins, “Undifferentiated facial electromyography responses to dynamic, audio-visual emotion displays inindividuals with autism spectrum disorders,” Developmental Science, 2013. doi:
10.1111/desc.12062.
[J17] Ilya Lashuk, Aparna Chandramowlishwaran, H. Langston, Tuan-Ahn
Nguyen, R. Sampath, Aashay Shringarpure, R. Vuduc, L. Ying, D. Zorin,
and G. Biros, “A massively parallel adaptive fast multipole method on heterogeneous architectures,” Communications of the ACM (CACM), vol. 55, no. 5, pp. 101–
109, May 2012, Extends conference version: doi:10.1145/1654059.1654118. doi:
10.1145/2160718.2160740.
[J18] Jaekyu Lee, H. Kim, and R. Vuduc, “When prefetching works, when it doesn’t,
and why,” ACM Trans. Architecture and Code Optimization (TACO), vol. 9, no. 1,
Mar. 2012. doi: 10.1145/2133382.2133384.
[J19] R. Vuduc and Kenneth Czechowski, “What GPU computing means for highend systems,” IEEE Micro, vol. 31, no. 4, pp. 74–78, Jul. 2011. doi: 10.1109/MM.
2011.78.

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6[J20] S. Williams, R. Vuduc, L. Oliker, J. Shalf, K. Yelick, and J. Demmel, “Optimizing sparse matrix-vector multiply on emerging multicore platforms,” J. Parallel
Computing (ParCo), vol. 35, no. 3, pp. 178–194, Mar. 2009, Extends conference
version: doi:10.1145/1362622.1362674. doi: 10 . 1016 / j . parco . 2008 . 12 . 006.
Most Downloaded Paper, Q1 2009: http://tinyurl.com/yllkfee[J21] Rajesh Nishtala, R. Vuduc, J. W. Demmel, and K. A. Yelick, “When cache
blocking sparse matrix vector multiply works and why,” Applicable Algebra inEngineering, Communication, and Computing: Special Issue on Computational Linear
Algebra and Sparse Matrix Computations, J. Wasniewski, J. Dongarra, S. Toledo,
and Z. Zlatev, Eds., Mar. 2007. doi: http://dx.doi.org/10.1007/s00200-0070038-9.
[J22] J. Demmel, J. Dongarra, V. Eijkhout, E. Fuentes, A. Petitet, R. Vuduc, R. C.
Whaley, and K. Yelick, “Self-adapting linear algebra algorithms and software,”Proc. IEEE, vol. 93, no. 2, pp. 293–312, Feb. 2005. doi: http://dx.doi.org/10.
1109/JPROC.2004.840848.
[J23] Eun-Jin Im, K. Yelick, and Richard Vuduc, “Sparsity: Optimization framework for sparse matrix kernels,” Int’l. J. High Performance Computing Applications(IJHPCA), vol. 18, no. 1, pp. 135–158, Feb. 2004. doi: http://dx.doi.org/10.
1177/1094342004041296.
[J24] Richard Vuduc, J. Demmel, and J. Bilmes, “Statistical models for empiricalsearch-based performance tuning,” Int’l. J. High Performance Computing Applications (IJHPCA), vol. 18, no. 1, pp. 65–94, 2004, Extends conference version:
doi:10.1007/3-540-45545-0_21. doi: 10.1177/1094342004041293.
[J25] B. Balko, I. W. Kay, J. D. Silk, Richard Vuduc, and J. W. Neuberger, “Superfluorescence in the presence of inhomogeneous broadening,” Hyperfine Interactions: Special Issue on the Gamma-Ray Laser, vol. 107, no. 1–4, pp. 369–379, Jun.
1997. doi: http://dx.doi.org/10.1023/A:1012020225589.
[J26] B. Balko, I. W. Kay, Richard Vuduc, and J. W. Neuberger, “Recovery of superfluorescence in inhomogeneously broadened systems through rapid relaxation,” Phys. Rev. B, vol. 55, no. 18, pp. 12 079–12 085, May 1997. doi: http ://dx.doi.org/10.1103/PhysRevB.55.12079.
IV-B.2 Conference Presentation with Proceedings (Refereed)(Papers [C71]–[C80] arefor work prior to myjoining Georgia Tech.)[C1] Patrick Lavin, J. S. Young, and R. W. Vuduc, “Multifidelity memory systemsimulation in SST,” in Proceedings of the International Symposium on Memory Systems (MEMSYS), (accepted), Oct. 2023.
[C2] Mikhail Isaev, N. McDonald, L. Dennison, and R. Vuduc, “Calculon: A methodology and tool for high-level co-design of systems and large language models,” in Proceedings of the ACM/IEEE International Conference for High-Performance
Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), (accepted),
Denver, CO, USA, Nov. 2023. [90/376=23.9%][git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

7[C3] S. Eswar, Benjamin Cobb, Koby Hayashi, R. Kannan, G. Ballard, R. Vuduc,and H. Park, “Distributed-memory parallel JointNMF,” in Proceedings of the ACM
International Conference on Supercomputing (ICS), (accepted), Jun. 2023.
[C4] R. Kannan, P. Sao, H. Lu, J. Kurzak, G. Schenk, Y. Shi, S.-H. Lim, S. Israni, Vijay Thakkar, G. Cong, R. Patton, S. E. Baranzini, R. Vuduc, and T. Potok, “Exaflops biomedical knowledge graph analytics,” in Proceedings of the ACM/IEEEInternational Conference for High-Performance Computing, Networking, Storage, andAnalysis (“Supercomputing” or SC), 2022. doi: 10.1109/SC41404.2022.00011. Finalist, Gordon Bell Prize[C5] Michael Isaev, N. McDonald, J. Young, and R. Vuduc, “ParaGraph: An applicationsimulator interface and toolkit for hardware-software co-design,” in Proceedingsof the International Conference on Parallel Processing (ICPP), Bordeaux, France, Sep.
2022. [Online]. Available: https : / / ssl . linklings . net / conferences / icpp /icpp2022_program/views/includes/files/pap390s3-file1.pdf. [84/311=27.0%][C6] Chunxing Yin, D. Zheng, I. Nisa, C. Faloutsos, G. Karypis, and R. Vuduc,“Nimble GNN embedding with tensor-train decomposition,” in Proceedings of
the SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington, DC, USA, Aug. 2022. doi: 10.1145/3534678.3539423. [254/1695=15.0%][C7] Sara Karamati, J. Young, C. Hughes, K. S. Hemmert, R. E. Grant, W. W.
Schonbein, S. L. Levy, T. M. Conte, and R. Vuduc, ““Smarter” NICs for fastermolecular dynamics: A case study,” in Proceedings of the IEEE International Paralleland Distributed Processing Symposium (IPDPS), Jun. 2022. doi: 10.1109/IPDPS53621.
2022.00063. [(46 first round, incl. this paper, plus 77 second round)/474=25.9%]
Finalist, Best Paper Award[C8] Raghul Duggal, C. Xiao, R. Vuduc, D. H. Chau, and J. Sun, “CUP: Cluster
pruning for compressing deep neural networks,” in Proceedings of the IEEE International Conference on Big Data (BigData), Dec. 2021. doi: 10.1109/BigData52589.
2021.9671980.
[C9] Sudhanshu Agarwal and R. Vuduc, “Is it Nemo or Dory? Fast and accurate
object detection for IoT and edge devices,” in Proceedings of the 11th International
Conference on the Internet of Things, ACM, Nov. 2021. [Online]. Available: https://iot-conference.org/iot2021/.
[C10] Patrick Lavin, J. S. Young, R. Vuduc, and J. Beard, “Online model swapping
for architectural simulation,” in Proceedings of the ACM International Conference
on Computing Frontiers (CF), (held virtually), Jun. 2021. [21/84=25%][C11] P. Sao, H. Lu, R. Kannan, Vijay Thakkar, R. Vuduc, and T. Potok, “Scalable
all-pairs shortest paths for huge graphs on multi-GPU clusters,” in Proceedingsof the International Symposium on High-Performance Parallel and Distributed Computing (HPDC), Stockholm, Sweden: ACM, Jun. 2021. [19%][git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

8[C12] R. Kannan, P. Sao, H. Lu, D. Herrmannova, R. Patton, T. E. Potok, Vijay
Thakkar, and R. Vuduc, “Scalable knowledge-graph analytics at 136 petaflop/s,”
in Proceedings of the ACM/IEEE International Conference for High-Performance Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), Atlanta, GA,
USA, Nov. 2020. Finalist, Gordon Bell Prize[C13] Srinivas Eswar, Koby Hayashi, G. Ballard, R. Kannan, R. Vuduc, and H.
Park, “Distributed-memory parallel symmetric nonnegative matrix factorization,” in Proceedings of the ACM/IEEE International Conference for High-Performance
Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), (accepted),
Atlanta, GA, USA, Nov. 2020.
[C14] Tong Zhou, J. Shirako, Anirudh Jain, Sriseshan Srikanth, T. Conte, R.
Vuduc, and V. Sarkar, “Intrepydd: Performance, productivity, and portabillity
for data science application kernels,” in Onward! 2020: Proceedings of the 2020 ACM
SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on
Programming and Software, Nov. 2020, pp. 65–83. doi: 10.1145/3426428.3426915.
[C15] Xin Chen, T. M. Tucker, T. Kurfess, R. Vuduc, and L. Hu, “Max orientation coverage: Efficient path planning to avoid collisions in the CNC millingof 3D objects,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (accepted), Las Vegas, NV, USA, Oct. 2020.
[C16] Patrick Lavin, J. Riedy, J. Young, R. Vuduc, A. Vose, and D. Ernst, “Evaluating gather and scatter performance on CPUs and GPUs,” in Proceedings of theInternational Symposium on Memory Systems (MEMSYS), Washington, DC, USA,
Sep. 2020. doi: 10.1145/3422575.3422794.
[C17] P. Sao, R. Kannan, Prasun Gera, and R. Vuduc, “A supernodal all pairs-shortest
path algorithm,” in Proceedings of the ACM SIGPLAN Symposium on Principles andPractice of Parallel Programming (PPoPP), Wien, Austria, Feb. 2020. doi: 10.1145/
3332466.3374533.
[C18] Xin Chen, T. M. Tucker, T. Kurfess, and R. Vuduc, “Adaptive Deep Path:
Efficient coverage of a known environment under various configurations,” inIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau,
Nov. 2019.
[C19] Xin Chen, D. Konobrytskyi, T. M. Tucker, T. R. Kurfess, and R. Vuduc, “Fastercollision detection at high resolution for CNC milling applications on GPUs,” inProceedings of the International Conference on Parallel Processing (ICPP), (accepted),
Kyoto, Japan, Aug. 2019.
[C20] J. Li, B. Uçar, Ü. Çatulyürek, J. Sun, K. Barker, and R. Vuduc, “Efficient andeffective sparse tensor reordering,” in Proceedings of the ACM International Conference on Supercomputing (ICS), Phoenix, AZ, USA, Jun. 2019. doi: 10 . 1145 /
3330345.3330366.
[C21] P. Sao, R. Kannan, X. Li, and R. Vuduc, “A communication-avoiding 3D sparse
triangular solver,” in Proceedings of the ACM International Conference on Supercomputing (ICS), Phoenix, AZ, USA, Jun. 2019. doi: 10.1145/3330345.3330357.

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9[C22] Israt Nisa, J. Li, A. Sukumaran-Rajam, R. Vuduc, and P. Sadayappan, “Loadbalanced sparse MTTKRP on GPUs,” in Proceedings of the IEEE International
Parallel and Distributed Processing Symposium (IPDPS), May 2019. doi: 10.1109/
IPDPS.2019.00023.
[C23] Jiajia Li, J. Sun, and R. Vuduc, “HiCOO: Hierarchical storage of sparse tensors,”in Proceedings of the ACM/IEEE International Conference for High-Performance Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), Nov. 2018. doi:
10.1109/SC.2018.00022. Winner, Best Student Paper[C24] Ioakeim Perros, E. E. Papalexakis, H. Park, R. Vuduc, X. Yan, C. deFilippi,
W. F. Stewart, and J. Sun, “SUSTain: Scalable unsupervised scoring for tensors
and its application to phenotyping,” in Proceedings of the SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD), Conference version of arXiv:1803.05473,
London, UK, Aug. 2018. doi: 10.1145/3219819.3219999. [107/983=10.9%][C25] Yang You, C.-J. Hsieh, R. Vuduc, and J. Demmel, “Accurate, fast, and scalable
kernel ridge regression on parallel and distributed systems,” in Proceedings of the
ACM International Conference on Supercomputing (ICS), Beijing, China, Jun. 2018.
[36/193=18.7%][C26] Sara Karamati, J. Young, and R. Vuduc, “An energy-efficient single-source
shortest path algorithm,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Vancouver, BC, Canada, May 2018. [113/461=24.5%][C27] Piyush Sao, X. Li, and R. Vuduc, “A communication-avoiding 3D LU factorization algorithm for sparse matrices,” in Proceedings of the IEEE International
Parallel and Distributed Processing Symposium (IPDPS), Vancouver, BC, Canada,
May 2018. [113/461=24.5%][C28] Ioakeim Perros, E. E. Papalexakis, F. Wang, R. Vuduc, E. Searles, M. Thompson, and J. Sun, “SPARTan: Scalable PARAFAC2 for large and sparse data,” inProceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining(KDD), Conference version of arXiv:1703.04219v1, Halifax, Canada, Aug. 2017.
doi: 10.1145/3097983.3098014. [8.6%][C29] Jiajia Li, J. Choi, Ioakeim Perros, J. Sun, and R. Vuduc, “Model-driven sparse
CP decomposition for high-order tensors,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Orlando, FL, USA,May 2017. doi: 10.1109/IPDPS.2017.80. [23%][C30] Ioakeim Perros, F. Wang, P. Zhang, P. Walker, R. Vuduc, J. Pathak, and J.
Sun, “Polyadic regression and its application to chemogenomics,” in Proceedingsof the SIAM International Conference on Data Mining (SDM), Apr. 2017. doi: 10.
1137/1.9781611974973.9.
[C31] Mohammad M. Hossain, C. Nath, T. M. Tucker, T. R. Kurfess, and R. W.
Vuduc, “A graphical approach for freeform surface offsetting with GPU acceleration for subtractive 3D printing,” in Proceedings of the 11th ASME Manufacturing
Science and Engineering Conference (MSEC), Blacksburg, VA, USA, Jun. 2016.

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10[C32] Zhengkai Wu, T. M. Tucker, C. Nath, T. R. Kurfess, and R. W. Vuduc, “Stepring based 3D path planning via GPU simulation for subtractive 3D printing,”
in Proceedings of the 11th ASME Manufacturing Science and Engineering Conference(MSEC), Blacksburg, VA, USA, Jun. 2016.
[C33] Mohammad Hossain, T. Tucker, T. Kurfess, and R. Vuduc, “HDT: A hybrid
structure for extreme-resolution 3D sparse data modeling,” in Proceedings of theIEEE International Parallel and Distributed Processing Symposium (IPDPS), Chicago,
IL, USA, May 2016. [Online]. Available: http://ipdps.org. [114/496=23%][C34] Jiajia Li, Casey Battaglino, Ioakeim Perros, J. Sun, and R. Vuduc, “An inputadaptive and in-place dense tensor-times-matrix multiply,” in Proceedings of the
ACM/IEEE International Conference for High-Performance Computing, Networking,Storage, and Analysis (“Supercomputing” or SC), Austin, TX, USA, Nov. 2015. doi:
10.1145/2807591.2807671. [Online]. Available: http://dl.acm.org/citation.
cfm?id=2807671. [79/358=22.1%][C35] Ioakeim Perros, Robert Chen, R. Vuduc, and J. Sun, “Sparse hierarchical
Tucker factorization and its application to healthcare,” in Proc. IEEE Int’l. Conf.
Data Mining (ICDM), (short paper), Stony Brook, NY, USA, Nov. 2015. doi:
10.1109/ICDM.2015.29.
[C36] Oded Green, Marat Dukhan, and R. Vuduc, “Branch-avoiding graph algorithms,” in Proc. ACM Symp. Parallel Algorithms and Architectures (SPAA), Portland, OR, USA, Jun. 2015.
[C37] Yang You, J. Demmel, Kenneth Czechowski, L. Song, and R. Vuduc, “CASVM: Communication-avoiding support vector machines on clusters,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium(IPDPS), Hyderabad, India, May 2015. doi: 10.1109/IPDPS.2015.117. [108/496=21.8%]
Winner, Best Paper (algorithms track)[C38] Piyush Sao, Xing Liu, R. Vuduc, and X. Li, “A sparse direct solver for distributed memory Xeon Phi-accelerated systems,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Hyderabad, India,
May 2015. doi: 10.1109/IPDPS.2015.104. [108/496=21.8%][C39] Piyush Sao, R. Vuduc, and X. Li, “A distributed CPU-GPU sparse direct solver,”in Proceedings of the 20th International European Conference on Parallel Processing(Euro-Par), Porto, Portugual, Aug. 2014. doi: 10.1007/978-3-319-09873-9\_41.
[C40] Kenneth Czechowski, V. W. Lee, E. Grochowski, R. Ronen, R. Singhal, R.
Vuduc, and P. Dubey, “Improving the energy efficiency of big cores,” in Proc. ACM/IEEE
Int’l. Symp. on Computer Architecture (ISCA), Minneapolis, MN, USA, Jun. 2014.
doi: 10.1145/2678373.2665743.
[C41] Jee Choi, Marat Dukhan, Xing Liu, and R. Vuduc, “Algorithmic time, energy, and power on candidate HPC compute building blocks,” in Proceedingsof the IEEE International Parallel and Distributed Processing Symposium (IPDPS),
Phoenix, AZ, USA, May 2014. doi: 10.1109/IPDPS.2014.54.

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11[C42] Piyush Sao and R. Vuduc, “Self-stabilizing iterative solvers,” in Proc. 4th Wkshp.
Latest Advances in Scalable Algorithms for Large-scale Systems (ScalA), Denver, CO,
USA, Nov. 2013. doi: 10 . 1145 / 2530268 . 2530272. [Online]. Available: http ://www.csm.ornl.gov/srt/conferences/Scala/2013/.
[C43] Marat Dukhan and R. Vuduc, “Methods for high-throughput computationof elementary functions,” in Proc. 10th Int’l. Conf. Parallel Processing and Applied
Mathematics (PPAM), Sep. 2013.
[C44] Sangmin Park, M. J. Harrold, and R. Vuduc, “Griffin: Grouping suspicious
memory-access patterns to improve understanding of concurrency bugs,” inProceedings of the International Symposium on Software Testing and Analysis (ISSTA), Lugano, Switzerland, Jul. 2013. [32/124=25.8%][C45] Jee Choi, D. Bedard, R. Fowler, and R. Vuduc, “A roofline model of energy,” inProceedings of the IEEE International Parallel and Distributed Processing Symposium(IPDPS), This paper is a short peer-reviewed conference version of the following technical report: https://smartech.gatech.edu/xmlui/handle/1853/45737,
Boston, MA, USA, May 2013. doi: 10.1109/IPDPS.2013.77. [106/494=21.5%][C46] Kenneth Czechowski and R. Vuduc, “A theoretical framework for algorithmarchitecture co-design,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Boston, MA, USA, May 2013. doi: 10 .
1109/IPDPS.2013.99. [106/494=21.5%][C47] William B. March, Kenneth Czechowski, Marat Dukhan, T. Benson, Dongryeol Lee, A. J. Connolly, R. Vuduc, E. Chow, and A. G. Gray, “Optimizing thecomputation of n-point correlations on large-scale astronomical data,” in Proceedings of the ACM/IEEE International Conference for High-Performance Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), Nov. 2012. [Online]. Available: http://dl.acm.org/citation.cfm?id=2389097. [100/472=21.2%][C48] Aparna Chandramowlishwaran, Jee Whan Choi, K. Madduri, and R. Vuduc,“Towards a communication optimal fast multipole method and its implications
for exascale,” in Proc. ACM Symp. Parallel Algorithms and Architectures (SPAA),
Brief announcement., Pittsburgh, PA, USA, Jun. 2012. doi: 10.1145/2312005.
2312039.
[C49] Kenneth Czechowski, Chris McClanahan, Casey Battaglino, Kartik Iyer,
P.-K. Yeung, and R. Vuduc, “On the communication complexity of 3D FFTs
and its implications for exascale,” in Proceedings of the ACM International Conference on Supercomputing (ICS), San Servolo Island, Venice, Italy, Jun. 2012. doi:
10.1145/2304576.2304604. [36/161=22.4%][C50] Dongryeol Lee, R. Vuduc, and A. G. Gray, “A distributed kernel summation
framework for general-dimension machine learning,” in Proc. SIAM Int’l. Conf.
Data Mining (SDM), Anaheim, CA, USA, Apr. 2012. doi: 10.1137/1.9781611972825.
34. [53 (talks)/363=14.6%] Winner, Best Paper[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

12[C51] Sangmin Park, R. Vuduc, and M. J. Harrold, “A unified approach for localizing non-deadlock concurrency bugs,” in Proc. IEEE Int’l. Conf. Software Testing,
Verification, and Validation (ICST), Montréal, Canada, Apr. 2012. doi: 10.1109/
ICST.2012.85. [39/145=26.9%][C52] Cong Hou, George Vulov, D. Quinlan, D. Jefferson, R. Fujimoto, and R. Vuduc,“A new method for program inversion,” in Proc. Int’l. Conf. Compiler Construction(CC), Tallinn, Estonia, Mar. 2012. [Online]. Available: http://www.cc.gatech.
edu/~chou3/ProgramInversion.pdf. [13/51=25.5%][C53] Jaewoong Sim, Aniruddha Dasgputa, H. Kim, and R. Vuduc, “A performance analysis framework for identifying performance benefits in GPGPU applications,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), New Orleans, LA, USA, Feb. 2012. doi:
10.1145/2145816.2145819. [26/175=14.9%][C54] Sooraj Bhat, A. Agarwal, R. Vuduc, and A. Gray, “A type theory for probability
density functions,” in ACM SIGACT-SIGPLAN Symp. Principles of Programming
Languages (POPL 2012), Philadelpha, PA, USA, Jan. 2012. doi: 10.1145/2103656.
2103721. [44/205=21.5%][C55] George Vulov, Cong Hou, R. Vuduc, D. Quinlan, R. Fujimoto, and D. Jefferson, “The Backstroke framework for source level reverse computation applied
to parallel discrete event simulation,” in Proc. Winter Simulation Conf. (WSC), S.
Jain, R. R. Creasey, J. Himmelspach, K. White, and M. Fu, Eds., Phoenix, AZ,
USA: IEEE, Dec. 2011. [Online]. Available: http : / / www . informs - sim . org /wsc11papers/264.pdf.
[C56] Kenneth Czechowski, Chris McClanahan, Casey Battaglino, Kartik Iyer,
P.-K. Yeung, and R. Vuduc, “Prospects for scalable 3D FFTs on heterogeneous
exascale systems,” in Proceedings of the ACM/IEEE International Conference forHigh-Performance Computing, Networking, Storage, and Analysis (“Supercomputing”
or SC), (poster; extended version available as Georgia Tech report GT-CSE-1102, Nov. 2011.
[C57] Jaekyu Lee, Nagesh B. Lakshminarayana, H. Kim, and R. Vuduc, “Manythread aware prefetching mechanisms for GPGPU applications,” in Proc. IEEE/ACM
Int’l. Symp. Microarchitecture (MICRO), Atlanta, GA, USA, Dec. 2010. doi: 10 .
1109/MICRO.2010.44. [45/248=18.1%][C58] Aparna Chandramowlishwaran, K. Madduri, and R. Vuduc, “Diagnosis,
tuning, and redesign for multicore performance: A case study of the fast multipole method,” in Proceedings of the ACM/IEEE International Conference for HighPerformance Computing, Networking, Storage, and Analysis (“Supercomputing” orSC), New Orleans, LA, USA, Nov. 2010. doi: 10.1109/SC.2010.19. [51/253=20.2%][C59] Abtin Rahimian, I. Lashuk, Aparna Chandramowlishwaran, Dhairya
Malhotra, Logan Moon, R. Sampath, Aashay Shringarpure, S. Veerapaneni, J. Vetter, R. Vuduc, D. Zorin, and G. Biros, “Petascale direct numericalsimulation of blood flow on 200k cores and heterogeneous architectures,” inProceedings of the ACM/IEEE International Conference for High-Performance Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), New Orleans,[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

13LA, USA, Nov. 2010. doi: 10 . 1109 / SC . 2010 . 42. [51/253=20.2%] Winner,
Gordon Bell Prize[C60] Sangmin Park, R. W. Vuduc, and M. J. Harrold, “Falcon: Fault localization for
concurrent programs,” in Proceedings of the ACM/IEEE International Conference
on Software Engineering (ICSE), Cape Town, South Africa, May 2010. doi: 10 .
1145/1806799.1806838. [52/380=13.7%][C61] Aparna Chandramowlishwaran, K. Knobe, and R. Vuduc, “Performance
evaluation of Concurrent Collections on high-performance multicore computing systems,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Atlanta, GA, USA, Apr. 2010. doi: 10.1109/IPDPS.
2010.5470404. [127/527=24.1%] Winner, Best Paper (software track)[C62] Aparna Chandramowlishwaran, S. Williams, L. Oliker, Ilya Lashuk, G.
Biros, and R. Vuduc, “Optimizing and tuning the fast multipole method forstate-of-the-art multicore architectures,” in Proceedings of the IEEE International
Parallel and Distributed Processing Symposium (IPDPS), Atlanta, GA, USA, Apr.
2010. doi: 10.1109/IPDPS.2010.5470415. [127/527=24.1%][C63] Jee Whan Choi, Amik Singh, and R. W. Vuduc, “Model-driven autotuningof sparse matrix-vector multiply on GPUs,” in Proceedings of the ACM SIGPLAN
Symposium on Principles and Practice of Parallel Programming (PPoPP), Bangalore,
India, Jan. 2010. doi: 10.1145/1693453.1693471. [29/173=16.8%][C64] Aparna Chandramowlishwaran, K. Knobe, and R. Vuduc, “Applying theConcurrent Collections programming model to asynchronous parallel dense
linear algebra,” in Proceedings of the ACM SIGPLAN Symposium on Principles andPractice of Parallel Programming (PPoPP), (poster), Bangalore, India, Jan. 2010.
doi: 10.1145/1693453.1693506. [Papers+posters: 45/173=26.1%][C65] Ilya Lashuk, Aparna Chandramowlishwaran, H. Langston, Tuan-Anh
Nguyen, R. Sampath, Aashay Shringarpure, R. Vuduc, L. Ying, D. Zorin,
and G. Biros, “A massively parallel adaptive fast multipole method on heterogeneous architectures,” in Proceedings of the ACM/IEEE International Conference
for High-Performance Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), Portland, OR, USA, Nov. 2009. doi: http://doi.acm.org/10.1145/
1654059.1654118. [59/261=22.6%] Finalist, Best Paper[C66] Nitin Arora, Aashay Shringarpure, and R. Vuduc, “Direct n-body kernels
for multicore platforms,” in Proceedings of the International Conference on Parallel
Processing (ICPP), Vienna, Austria, Sep. 2009. doi: http://dx.doi.org/10.1109/
ICPP.2009.71. [71/220=32.3%][C67] Nitin Arora, R. P. Russell, and R. W. Vuduc, “Fast sensitivity computations
for numerical optimizations,” in Proc. AAS/AIAA Astrodynamics Specialist Conference, ser. AAS 09-435, Pittsburgh, PA, USA, Aug. 2009. [Online]. Available: http : / / soliton . ae . gatech . edu / people / rrussell / FinalPublications /ConferencePapers/09AugAAS_09-392_p2pLowthrust.pdf.

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14[C68] Sundaresan Venkatasubramanian and R. W. Vuduc, “Tuned and wildly
asynchronous stencil kernels for hybrid CPU/GPU platforms,” in Proceedings of
the ACM International Conference on Supercomputing (ICS), New York, NY, USA,
Jun. 2009. doi: http://dx.doi.org/10.1145/1542275.1542312. [47/191=25%][C69] Seunghwa Kang, D. Bader, and R. Vuduc, “Understanding the design tradeoffs among current multicore systems for numerical computations,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS),
Rome, Italy, May 2009. doi: http://doi.ieeecomputersociety.org/10.1109/
IPDPS.2009.5161055. [101/440=23.0%][C70] Manisha Gajbe, A. Canning, J. Shalf, L.-W. Wang, H. Wasserman, and R.
Vuduc, “Auto-tuning distributed-memory 3-dimensional fast Fourier transforms on the Cray XT4,” in Proc. Cray User’s Group (CUG) Meeting, Atlanta, GA,
USA, May 2009. [Online]. Available: http://www.cug.org/5- publications/proceedings _ attendee _ lists / CUG09CD/ S09 _ Proceedings / pages / authors / 11 15Wednesday/14C-Gajbe/GAJBE-paper.pdf.

[C71] Sam Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, and J. Demmel, “Optimization of sparse matrix-vector multiplication on emerging multicore platforms,” in Proceedings of the ACM/IEEE International Conference for High-Performance
Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), 2007. doi:
http://dx.doi.org/10.1145/1362622.1362674. [54/268=20.1%][C72] D. Quinlan, R. Vuduc, T. Panas, Jochen Härdtlein, and Andreas Sæbjørnsen,“Support for whole-program analysis and the verification of the one-definition
rule in C++,” in Proc. Static Analysis Summit (SAS), vol. NIST Special Publication
500-262, 2006, pp. 27–35. [Online]. Available: http://samate.nist.gov/docs/
NIST_Special_Publication_500-262.pdf.
[C73] D. Quinlan, S. Ur, and R. Vuduc, “An extensible open-source compiler infrastructure for testing,” in Proc. IBM Haifa Verification Conf. (VC), vol. LNCS 3875,
Haifa, Israel: Springer Berlin / Heidelberg, Nov. 2005, pp. 116–133. doi: 10 .
1007/11678779\_9.
[C74] R. W. Vuduc and Hyun-Jin Moon, “Fast sparse matrix-vector multiplication
by exploiting variable block structure,” in Proc. High-Performance Computing andCommunications Conf. (HPCC), vol. LNCS 3726, Sorrento, Italy: Springer, Sep.
2005, pp. 807–816. doi: 10.1007/11557654\_91. [116/387=30%][C75] R. Vuduc, J. W. Demmel, and K. A. Yelick, “OSKI: A library of automaticallytuned sparse matrix kernels,” in Proc. SciDAC, J. Physics: Conf. Ser., vol. 16, 2005,
pp. 521–530. doi: http://dx.doi.org/10.1088/1742-6596/16/1/071.
[C76] Benjamin C. Lee, R. Vuduc, J. Demmel, and K. Yelick, “Performance models
for evaluation and automatic tuning of symmetric sparse matrix-vector multiply,” in Proceedings of the International Conference on Parallel Processing (ICPP),
Montreal, Canada, Aug. 2004. doi: http://dx.doi.org/10.1109/ICPP.2004.
1327917. [65/190=34.2%] Winner, Best Paper[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

15[C77] Richard Vuduc, J. W. Demmel, K. A. Yelick, Shoaib Kamil, Rajesh Nishtala, and Benjamin Lee, “Performance optimizations and bounds for sparse
matrix-vector multiply,” in Proceedings of the ACM/IEEE International Conference for High-Performance Computing, Networking, Storage, and Analysis (“Supercomputing” or SC), Baltimore, MD, USA, Nov. 2002. [Online]. Available: http://portal.acm.org/citation.cfm?id=762822. Finalist, Best Student Paper[C78] Richard Vuduc, J. W. Demmel, and J. A. Bilmes, “Statistical models for empirical search-based performance tuning,” in Proc. Int’l. Conf. Computational Science(ICCS), Extends workshop version: http : / / www . eecs . harvard . edu / ~smith /fddo3 / papers / 107 . ps, vol. LNCS 2073, San Francisco, CA, USA: Springer
Berlin / Heidelberg, May 2001, pp. 117–126. doi: 10.1007/3-540-45545-0\_21.
[C79] Danyel Fisher, Kris Hildrum, Jason Hong, Mark Newman, Megan Thomas,
and Richard Vuduc, “SWAMI: A framework for collaborative filtering algorithm development and evaluation,” in Proc. ACM Conf. Research and Development
in Information Retrieval (SIGIR), (poster), Athens, Greece, Jul. 2000, pp. 366–368.
doi: http://dx.doi.org/10.1145/345508.345658.
[C80] B. Balko, I. Kay, Richard Vuduc, and J. Neuberger, “An investigation of thepossible enhancement of nuclear superfluorescence through crystalline and hyperfine interaction effects,” in Proc. Lasers ’95, 1996, p. 308.
IV-B.3 Other Refereed Material, e.g., Workshops(Papers [W22]–[W31]
are for work prior to myjoining Georgia Tech.)[W1] M. Isaev, N. McDonald, and R. Vuduc, “Scaling infrastructure to support multitrillion parameter LLM training,” in ASSYST: Workshop on Architecture and System Support for Transformer Models, (accepted), Orlando, FL, USA, Jun. 2023.
[Online]. Available: https://openreview.net/forum?id=rqn2v1Ltgn0.
[W2] P. Sao, C. Engelmann, S. Eswar, O. Green, and R. Vuduc, “Self-stabilizing connected components,” in Proceedings of the Workshop on Fault-Tolerance for HPC
at Extreme Scale (FTXS), (to appear), Denver, CO, USA, Nov. 2019. [Online].
Available: https://sites.google.com/site/ftxsworkshop/home/ftxs-2019.
[W3] Eric Hein, J. S. Young, Srinivas Eswar, Jiajia Li, Patrick Lavin, R. Vuduc,and J. Riedy, “An initial characterization of the Emu Chick,” in Proceedings of the
IPDPS Workshops: 8th International Workshop on Accelerators and Hybrid Exascale
Systems (AsHES), Vancouver, BC, Canada, 2018. [Online]. Available: http : / /www.mcs.anl.gov/events/workshops/ashes/2018/.
[W4] Jiajia Li, Yuchen Ma, Chenggang Yan, and R. Vuduc, “Optimizing sparsetensor times matrix on multicore and manycore architectures,” in Proceedingsof the 6th Workshop on Irregular Applications: Architectures and Algorithms (IA3 ),co-located with SC16, Salt Lake City, UT, USA, Nov. 2016. [Online]. Available:
http://hpc.pnl.gov/IA3.

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16[W5] J. Young and R. Vuduc, “Finding balance in the post-Moore’s Law era (a position paper),” in Proceedings of the 1st Post-Moore’s Era Supercomputing (PMES)
Workshop, co-located with SC16, Salt Lake City, UT, USA, Nov. 2016. [Online]. Available: https : / / github . com / jyoung3131 / research - publications /blob/main/publications/2016/young_vuduc_finding_balance_post_moores_
era_pmes_2016.pdf.

[W6] Marat Dukhan, R. Vuduc, and J. Riedy, “Wanted: Floating-point add roundoff error instruction,” in Proceedings of the 2nd International Workshop on Performance Modeling: Methods and Applications (PMAA), Frankfurt, Germany, Jun.
2016. [Online]. Available: http://arxiv.org/abs/1603.00491.
[W7] Piyush Kumar Sao, O. Green, Chirag Jain, and R. Vuduc, “A self-correcting
connected components algorithm,” in Proceedings of the Fault-Tolerance for HPC
at eXtreme Scale (FTXS) Workshop, Kyoto, Japan, May 2016. [Online]. Available:
https://sites.google.com/site/ftxsworkshop/home/ftxs-2016.
[W8] Jee W. Choi and R. W. Vuduc, “Analyzing the energy-efficiency of the fastmultipole method using a DVFS-aware energy model,” in Proceedings of theHeterogeneous Computing Workshop (HCW), at IPDPS’16, Chicago, IL, USA, May
2016. [Online]. Available: http://hcw.eecs.wsu.edu/.
[W9] Mohammad M. Hossain, T. M. Tucker, T. R. Kurfess, and R. W. Vuduc, “AGPU-parallel construction of volumetric trees,” in Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms (IA3 ), Austin, TX, USA,
Nov. 2015. doi: 10.1145/2833179.2833191. [Online]. Available: http://hpc.pnl.
gov/IA3/IA3/IA3-2015/IA3/Program.html.
[W10] Casey Battaglino, Robert Pienta, and R. Vuduc, “GraSP: Distributed streaming graph partitioning,” in Proc. 11th Workshop on High-Performance Graph Mining (HPGM), co-located with KDD’15, Sydney, Australia, Aug. 2015.
[W11] Marat Dukhan, Robert Guthrie, Robertson Taylor, and R. Vuduc, “Furious.js: A model for offloading compute-intensive javascript applications,” inProceedings of the 1st Workshop on High-Performance Scripting Languages, M. J.
Garzaran, S. Midkiff, and D. Padua, Eds., San Francisco, CA, USA, Feb. 2015.
[Online]. Available: http://polaris.cs.uiuc.edu/hpsl/.
[W12] Jee Choi, Aparna Chandramowlishwaran, K. Madduri, and R. Vuduc, “A
CPU-GPU hybrid implementation and model-driven scheduling of the fast multipole method,” in Proc. 7th Wkshp. on General-purpose Processing using GPUs (GPGPU7), Salt Lake City, UT, USA: ACM, Mar. 2014. doi: http://doi.acm.org/10.
1145/2576779.2576787. [Online]. Available: http://www.ece.neu.edu/groups/
nucar/GPGPU/GPGPU7/.
[W13] Piyush Sao and R. Vuduc, “Self-stabilizing iterative solvers,” in Proc. 4th Wkshp.
Latest Advances in Scalable Algorithms for Large-scale Systems (ScalA), Denver, CO,
USA, Nov. 2013. doi: 10 . 1145 / 2530268 . 2530272. [Online]. Available: http ://www.csm.ornl.gov/srt/conferences/Scala/2013/.

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17[W14] Cong Hou, D. Quinlan, D. Jefferson, R. Fujimoto, and R. Vuduc, “Synthesizing
loops for program inversion,” in Proc. 4th Wkshp. Reversible Computation, Copenhagen, Denmark, Jul. 2012. doi: 10 . 1007 / 978 - 3 - 642 - 36315 - 3 _ 6. [Online].
Available: http://www.reversible-computation.org/2012/cms. [23/46=50%][W15] R. Vuduc, Kenneth Czechowski, Aparna Chandramowlishwaran, andJee Whan Choi, “Courses in high-performance computing for scientists andengineers,” in Proc. NSF/TCPP Wkshp. Parallel and Distributed Computing Education (EduPar), co-located with IPDPS’12, Shanghai, China, May 2012.
[W16] Raghul Gunasekaran, D. Dillow, G. Shipman, R. Vuduc, and E. Chow, “Characterizing application runtime behavior from system logs and metrics,” in Proc. Int’l.
Wkshp. Characterizing Applications for Heterogeneous Exascale Systems (CACHES),
Tucson, AZ, USA, Jun. 2011.
[W17] Kenneth Czechowski, Casey Battaglino, Chris McClanahan, Aparna Chandramowlishwaran, and R. Vuduc, “Balance principles for algorithm-architecture
co-design,” in Proc. USENIX Wkshp. Hot Topics in Parallelism (HotPar), Berkeley,
CA, USA, May 2011. [Online]. Available: http : / / www . usenix . org / events /hotpar11/tech/final_files/Czechowski.pdf. [Talks: 16/45=35.5%][W18] R. Vuduc, Aparna Chandramowlishwaran, Jee Whan Choi, Murat Efe
Guney, and Aashay Shringarpure, “On the limits of GPU acceleration,” inProc. USENIX Wkshp. Hot Topics in Parallelism (HotPar), Berkeley, CA, USA, Jun.
2010. [Talks: 16/68=23.5%][W19] Sooraj Bhat, A. Agarwal, A. Gray, and R. Vuduc, “Toward interactive statistical modeling,” Procedia Computer Science, vol. 1, no. 1, pp. 1829–1838, May 2010,
Proc. Int’l. Conf. Computational Science (ICCS), Wkshp. Automated Program
Generation for Computational Science (APGCS). doi: 10.1016/j.procs.2010.
04.205. [10/21=47.6%][W20] C. Liao, D. J. Quinlan, R. Vuduc, and T. Panas, “Effective source-to-source outlining to support whole program empirical optimization,” in Proc. Int’l. Wkshp.
Languages and Compilers for Parallel Computing (LCPC), vol. LNCS, Newark, DE,
USA, Oct. 2009. doi: 10.1007/978-3-642-13374-9\_21.
[W21] Aparna Chandramowlishwaran, Abhinav Karhu, Ketan Umare, and R.
Vuduc, “Numerical algorithms with tunable parallelism,” in Proc.Wkshp. Software Tools for Multicore Systems (STMCS), at IEEE/ACM Int’l. Symp. Code Generation and Optimization (CGO), Boston, MA, USA, Apr. 2008. [Online]. Available:
http://people.csail.mit.edu/rabbah/conferences/08/cgo/stmcs/papers/
vuduc-stmcs08.pdf.

[W22] T. Panas, D. Quinlan, and R. Vuduc, “Analyzing and visualizing whole program architectures,” in Proc. Wkshp. Aerospace Software Engineering (AeroSE), at
ACM/IEEE Int’l. Conf. Software Eng. (ICSE), Also: Lawrence Livermore National
Laboratory Technical Report UCRL-PROC-231453, Minneapolis, MN, USA,May 2007. [Online]. Available: http://www.osti.gov/bridge/servlets/purl/
909924-c8K5TR/909924.pdf.

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18[W23] T. Panas, D. Quinlan, and R. Vuduc, “Tool support for inspecting the code
quality of HPC applications,” in Proc. Wkshp. Software Eng. for High-Performance
Computing Applications (SE-HPC), at ACM/IEEE Int’l. Conf. Software Eng. (ICSE),
Minneapolis, MN, USA, May 2007. doi: http : / / dx . doi . org / 10 . 1109 / SE HPC.2007.8.
[W24] D. Quinlan, R. Vuduc, and Ghassan Misherghi, “Techniques for specifying
bug patterns,” in Proc. ACM Wkshp. Parallel and Distributed Systems: Testing and
Debugging (PADTAD), at Int’l. Symp. Software Testing and Analysis (ISSTA), Portland, ME, USA, Jul. 2007. doi: http://doi.acm.org/10.1145/1273647.1273654.
Winner, Best Paper[W25] Q. Yi, K. Seymour, H. You, R. Vuduc, and D. Quinlan, “POET: Parameterized
Optimizations for Empirical Tuning,” in Proc. Wkshp. Performance Optimizationof High-level Languages and Libraries (POHLL), at IEEE Int’l. Par. Distrib. Processing
Symp. (IPDPS), Long Beach, CA, USA, Mar. 2007, pp. 1–8. doi: http://dx.doi.
org/10.1109/IPDPS.2007.370637.
[W26] D. Quinlan, M. Schordan, R. Vuduc, and Q. Yi, “Annotating user-defined abstractions for optimization,” in Proc. Wkshp. Performance Optimization of Highlevel Languages and Libraries (POHLL), at IEEE Int’l. Par. Distrib. Processing Symp.
(IPDPS), Rhodes, Greece, Apr. 2006. doi: http://dx.doi.org/10.1109/IPDPS.
2006.1639722.
[W27] R. Vuduc, M. Schulz, D. Quinlan, and B. de Supinski, “Improving distributed
memory applications testing by message perturbation,” in Proc. ACM Wkshp.
Parallel and Distributed Systems: Testing and Debugging (PADTAD), at Int’l. Symp.
Software Testing and Analysis (ISSTA), Portland, ME, USA, Jul. 2006. doi: http://dx.doi.org/10.1145/1147403.1147409. Winner, Best Paper[W28] Richard Vuduc, Attila Gyulassy, J. W. Demmel, and K. A. Yelick, “Memory
hierarchy optimizations and bounds for sparse AT Ax,” in Proc. Wkshp. Parallel Linear Algebra (PLA), at Int’l. Conf. Computational Sci. (ICCS), vol. LNCS 2659,
Melbourne, Australia: Springer Berlin / Heidelberg, Jun. 2003, pp. 705–714.
doi: 10.1007/3-540-44863-2\_69.
[W29] Richard Vuduc, Shoaib Kamil, Jen Hsu, Rajesh Nishtala, J. W. Demmel,
and K. A. Yelick, “Automatic performance tuning and analysis of sparse triangular solve,” in Proc. Wkshp. Performance Optimization of High-level Languages andLibraries (POHLL), at ACM Int’l. Conf. Supercomputing (ICS), New York, USA, Jun.
2002. [Online]. Available: http: / / www . ece . lsu . edu / jxr / pohll - 02/ papers /vuduc.pdf. Winner, Best Presentation; Winner, Best Student Paper[W30] Richard Vuduc, J. Demmel, and J. Bilmes, “Statistical modeling of feedback
data in an automatic tuning system,” in Proc. ACM Wkshp. Feedback-Directed Dynamic Optimization (FDDO), at Int’l. Symp. Microarchitecture (MICRO), Monterey,
CA, USA, Dec. 2000. [Online]. Available: http : / / www . eecs . harvard . edu /~smith/fddo3/papers/107.ps. Winner, Best Presentation[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

19[W31] Richard Vuduc and J. W. Demmel, “Code generators for automatic tuning of
numerical kernels: Experiences with FFTW,” in Proc. Semantics, Applications, andImplementation of Program Generation (SAIG), at ACM SIGPLAN Int’l. Conf. Functional Programming (ICFP), Montréal, Canada, Sep. 2000. doi: 10.1007/3- 54045350-4_14.
IV-B.4 Submitted Conference or Journal Articles

IV-C Other Publications and Creative Products
IV-C.1 Non-refereed Publications[O1] D. N. Parikh, M. E. Myers, R. Vuduc, and R. A. van de Geijn, A simple methodology for computing families of algorithms, arXiv, Aug. 2018. [Online]. Available:
https://arxiv.org/abs/1808.07832.
[O2] Ioakeim Perros, E. E. Papalexakis, F. Wang, R. Vuduc, E. Searles, M. Thompson, and J. Sun, SPARTan: Scalable PARAFAC2 for large and sparse data, Mar.
2017. doi: arXiv : 1703 . 04219. [Online]. Available: https : / / arxiv . org / abs /1703.04219v1.
[O3] Ioakeim Perros, Robert Chen, R. Vuduc, and J. Sun, “Sparse hierarchical
Tucker factorization and its application to healthcare,” Tech. Rep. arXiv:1610.07722,
Oct. 2016, Extended version of a conference short paper. [Online]. Available:
https://arxiv.org/abs/1610.07722.
[O4] Marat Dukhan, R. Vuduc, and J. Riedy, Wanted: Floating-point add round-off
error instruction, arXiv:1603.00491, Mar. 2016. [Online]. Available: http : / /arxiv.org/abs/1603.00491.
[O5] Y. Zhao, Q. Yi, K. Kennedy, D. Quinlan, and R. Vuduc, “Parameterizing loop fusion for automated empirical tuning,” Center for Applied Scientific Computing,
Lawrence Livermore National Laboratory, California, USA, Tech. Rep. UCRLTR-217808, Dec. 2005. doi: http://dx.doi.org/10.2172/890608.
[O6] E. Jason Riedy and Richard Vuduc, Microbenchmarking the Tera MTA, http://vuduc.org/pubs/riedy99-tera-report.pdf, May 1998.
IV-C.2 Software[F1] M. Dukhan, NNPACK: Neural Network Package, Jan. 2017. [Online]. Available:
https://github.com/Maratyszcza/NNPACK.
[F2] C. McClanahan, DiGPUFFT (”dig-puffed”): Distributed GPU FFTs, Jul. 2011. [Online]. Available: https://github.com/mcclanahoochie/digpufft.
[F3] R. Vuduc, OSKI: Optimized Sparse Kernel Interface, Jun. 2007. [Online]. Available:
https://bebop.cs.berkeley.edu/oski/about.html.
[F4] D. J. Quinlan, C. Liao, T. Panas, R. Matzke, M. Schordan, R. Vuduc, and Q. Yi,
ROSE Compiler Infrastructure, Oct. 2005. [Online]. Available: http://rosecompiler.
org/ROSE_UserManual/ROSE-UserManual.pdf.

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IV-C.3 Datasets[D1] R. Vuduc, Dataset: Jack, the autotuner, Zenodo, Oct. 2022. doi: 10.5281/zenodo.
7140941. [Online]. Available: https://doi.org/10.5281/zenodo.7140941.

IV-D Presentations
IV-D.1 Invited and Keynote Talks[T1] Data-movement accelerators for scientific computing, Invited keynote at the 45th
Advanced Supercomputing Environments (ASE) Seminar, Tokyo, Japan, Nov.
2023. [Online]. Available: https://hpcgarage.org/ase45.
[T2] Embracing communication, Salishan Conference on High-Speed Computing, Gleneden Beach, OR, USA, Apr. 2023. [Online]. Available: https://hpcgarage.org/
salishan23.
[T3] How I learned to stop worrying and love communication, Workshop on Clusters,
Clouds, and Data for Scientific Computing, Lyon, France, Sep. 2022. [Online].
Available: http://hpcgarage.org/ccdsc22.
[T4] A communication-avoiding sparse direct solver for linear systems and graph problems, Coordinated Science Laboratory Student Conference, University of Illinois Urbana-Champaign (UIUC), Feb. 2020. [Online]. Available: http://hpcgarage.
org/uiuc20/.
[T5] Is deep learning wasting our time, energy, and power? Invited talk at the University of Illinois Urbana-Champaign, Feb. 2020. [Online]. Available: http ://hpcgarage.org/uiuc20/.
[T6] A communication-avoiding sparse direct solver for linear systems on CPU+GPU platforms, SIAM Conf. Parallel Processing (SIAM PP), Seattle, WA, USA, Feb. 2020.
[Online]. Available: http://hpcgarage.org/pp20/.
[T7] Deep learning may be wasting our time, energy, and power, Invited talk at theWorkshop on Future Directions in Extreme-Scale Computing for Scientific
Grand Challenges, Texas Advanced Computing Center (TACC), Austin, TX,
USA, Jan. 2020. [Online]. Available: http://hpcgarage.org/tacc20/.
[T8] Performance engineering for sparse matrix, tensor, and graph computation, Invited
plenary at ICPP, Kyoto, Japan, Aug. 2019. [Online]. Available: https://www.
hpcs.cs.tsukuba.ac.jp/icpp2019/timetable/event/keynote-3/index.html.
[T9] An algorithm, a data structure, and a machine that all (try to) move fewer bits, Invited
keynote at VECPAR, São Pedro, Brazil, Sep. 2018. [Online]. Available: https://hpcgarage.org/vecpar18.
[T10] A communication-avoiding sparse direct solver, Talk at the ICES Seminar, University of Texas at Austin, Austin, TX, USA, Apr. 2018.
[T11] How much time, energy, power, and resilience does my algorithm need? Talk at the
ECE Seminar, University of Texas at Austin, Austin, TX, USA, Apr. 2018.

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21[T12] Designing an algorithm with a tuning knob that controls its power consumption, Talk
at the SIAM Conference on Parallel Processing (PP), Tokyo, Japan, Mar. 2018.
[Online]. Available: http://meetings.siam.org/sess/dsp_talk.cfm?p=89160.
[T13] A power-tunable algorithm to compute single-source shortest paths, Invited talk at
the University of Colorado, Colorado Springs (UCCS), Colorado Springs, CO,
USA, Nov. 2017.
[T14] A power-tunable single-source shortest path algorithm, Invited keynote for the
2nd International Workshop on Deepening Performance Models for Automatic Tuning (DPMAT), Nagoya, Japan, Aug. 2017. [Online]. Available: http://www.abc-lib.org/Workshop/DPMAT2017.html.
[T15] How much time, energy, and power does my algorithm need? Invited talk for the
Texas A&M University Computer Engineering and Systems Group (CESG)
Seminar, College Station, TX, USA, Apr. 2017. [Online]. Available: http : / /hpcgarage.org/aggies.
[T16] How much time, energy, and power does my algorithm need? Invited talk for University of Alabama, Birmingham, Dept. of Computer and Information Sciences
Seminar, Birmingham, AL, USA, Sep. 2016. [Online]. Available: http://hpcgarage.
org/uab.
[T17] How much time, energy, and power does my algorithm need? Invited keynote
at the International Symposium on Memory Systems (MEMSYS, memsys.io),
Alexandria, VA, USA, Oct. 2016. [Online]. Available: http://hpcgarage.org/
memsys16.
[T18] High-speed data analysis through specialization, Panelist, HPC “Power Users,”
Georgia Tech HPC External Advisory Group Meeting, May 2016. [Online].
Available: http://hpcgarage.org/eag.
[T19] How much time, energy, and power does my algorithm need? Invited talk at the
University of California, Irvine, Apr. 2016. [Online]. Available: http://hpcgarage.
org/uci.
[T20] How much time, energy, and power does my algorithm need? Invited talk at the
University of Texas at Austin, Mar. 2016. [Online]. Available: http://hpcgarage.
org/longhorns.
[T21] Performance understanding, Invited talk at SPPEXA’16, Munich, Germany, Jan.
2016. [Online]. Available: http://hpcgarage.org/sppexa16.
[T22] Is there life after Moore? Invited keynote at Post-Moore Computing Workshop
in Japan, Tokyo, Japan, Dec. 2015. [Online]. Available: http://hpcgarage.org/
jppm15/.
[T23] On algorithmic time, energy, and power, Invited keynote at the 11th Workshop
on High-Performance, Power-Aware Computing (HPPAC), at IPDPS’15, Salt
Lake City, UT, USA, Mar. 2015. [Online]. Available: http://hpcgarage.org/
ipdps15.

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22[T24] Computer science aspects of the fast multipole method, Featured Minisymposium
Talk at the SIAM Meeting on Computational Science and Engineering (CSE),
Salt Lake City, UT, USA, Mar. 2015. [Online]. Available: http://hpcgarage.
org/cse15.
[T25] A roofline model of energy, Platform for Advanced Scientific Computing (PASC)
meeting, Zürich, Switzerland, Jun. 2014. [Online]. Available: http://hpcgarage.
org/pasc14/.
[T26] (position talk) execution under a power constraint is about optimal control, Invited
panelist at the High-Performance, Power-Aware Computing (HPPAC) Workshop at IPDPS’14, Phoenix, AZ, USA, May 2014. [Online]. Available: http ://hpcgarage.org/hppac14/.
[T27] On the time, energy, and power to execute an algorithm, Invited keynote at the
SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP),
Portland, OR, USA, Feb. 2014. [Online]. Available: http : / / www . siam . org /meetings/pp14/.
[T28] Performance engineering practice, Invited talk at the IEEE VIS Workshop on Visualization and Analysis of Performance of Large-scale Software, http://hpcgarage.
org/vapls13, Atlanta, GA, USA, Oct. 2013. [Online]. Available: http://www.
sci.utah.edu/vapls2013.
[T29] Generalized roofline analysis? Dagstuhl Seminar 13401: Automatic application
autotuning for HPC architectures, http : / / hpcgarage . org / 13401; http : / /drops . dagstuhl . de / opus / volltexte / 2014 / 4423 / pdf / dagrep _ v003 _ i009 _p214 _ s13401 . pdf, Dagstuhl, Germany, Sep. 2013. [Online]. Available: http ://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13401.

[T30] How much (execution) time, energy, and power will my algorithm cost? Invited keynote
at the Int’l. Conf. Parallel Processing and Applied Mathematics (PPAM), http:/ / hpcgarage . org / ppam13, Warsaw, Poland, Sep. 2013. [Online]. Available:
http://ppam.pl/.
[T31] Generalized roofline analysis, Invited talk at ModSim: the US Department of Energy Workshop on Modeling and Simulation of Exascale Systems and Applications, http://hpcgarage.org/modsim13, Seattle, WA, USA, Sep. 2013. [Online].
Available: http://hpc.pnl.gov/modsim/2013/.
[T32] Can (your) algorithms inform architectures? Invited talk at ExaMath: The US Department of Energy Applied Mathematics Research for Exascale Computing,
http://hpcgarage.org/examath13/, Washington, DC, USA, Aug. 2013. [Online]. Available: https://collab.mcs.anl.gov/display/examath.
[T33] What do first principles of algorithms and architectures say about hybrid systems?
Invited keynote at the 3rd Int’l. Wkshp. Accelerators and Hybrid Exascale
Systems (AsHES), http://hpcgarage.org/ashes2013/, Boston, MA, USA, May
2013. [Online]. Available: http://www.mcs.anl.gov/events/workshops/ashes/
2013.

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23[T34] A theory for co-designing algorithms and architectures under power and chip-area constraints, Invited keynote at the 10th Int’l. Mtg. on High-Performance Computing for Computational Science (VECPAR), Kobe, Japan. Jul. 2012. [Online].
Available: http://nkl.cc.u-tokyo.ac.jp/VECPAR2012.
[T35] A theory for co-designing algorithms and architectures under power and chip-area constraints, Invited talk at the Young and Bright HPC Researchers session, International Supercomputing Conference (ISC), Hamburg, Germany, Jun. 2012.
[Online]. Available: http://www.isc-events.com/isc12/.
[T36] Will exascale computing really require new algorithms and programming models?
Invited panelist at the IEEE Int’l. Parallel and Distributed Processing Symposium (IPDPS), Shanghai, China, May 2012. [Online]. Available: http://ipdps.
org.
[T37] How might algorithms inform architectures at exascale? Invited keynote at the
Scalable Hierarchical Algorithms for eXtreme Computing (SHAX-C) Workshop, King Abdullah University of Science and Technology (KAUST), Saudi
Arabia, Apr. 2012. [Online]. Available: http://vuduc.org/talks/2012-SHAX-C/.
[T38] A theory of co-design? Invited keynote at the Partnership for Advanced Computing in Europe (PRACE) Future Technologies Workshop, Dansbury, UK,
Apr. 2012. [Online]. Available: http://vuduc.org/talks/2012-PRACE/.
[T39] Principles of algorithm-architecture co-design, Invited talk at the Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University, Providence, RI, USA, Jan. 2012. [Online]. Available: http : / / icerm .
brown.edu/tw12-1-exascale.
[T40] Balance principles for algorithm-architecture co-design, ECE Seminar, Carnegie Mellon University, Oct. 2011.
[T41] Balance principles for algorithm-architecture co-design, Invited keynote at Facing the Multicore Challenge II: A Conference for Young Scientists, Sep. 2011.
[Online]. Available: http://www.multicore-challenge.org/.
[T42] State-of-the-art and future directions in autotuning, Invited keynote at the 9th
Int’l. Conf. Parallel Processing and Applied Mathematics (PPAM), Toruń, Poland,
Sep. 2011. [Online]. Available: http://ppam.pl.
[T43] CnC for HPC, Int’l. Conf. Industrial and Applied Mathematics (ICIAM), Minisymposium on Creating the Next Generation of High Performance Numerical
Computing Capabilities, Vancouver, BC, Canada, http://meetings.siam.org/
sess/dsp_programsess.cfm?SESSIONCODE=12156, Jul. 2011.
[T44] Performance and correctness of software for large-scale heterogeneous systems, Invited keynote at the Wkshp. Exploiting Concurrency Efficiently and Correctly (EC2 ), http://www.cse.psu.edu/~swarat/ec2/, Jul. 2011.
[T45] Performance principles and practice, Invited talk at the Int’l. Supercomputing Conference (ISC), Hamburg, Germany, http://www.supercomp.de/isc11, Jun. 2011.
[T46] Parallel performance analysis principles and practice, Invited talk at the Parallel Algorithms, Programming, Architectures (PAPA) Workshop, at the Design Automation Conference (DAC), http://papaworkshop.com, Jun. 2011.

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24[T47] Performance principles and practice, Supercomputing Interest Community Seminar, IBM TJ Watson Laboratory, Yorktown Heights, NY, USA, May 2011.
[T48] Performance principles and practice, Invited talk at the Salishan Conference on
High-Speed Computing, sponsored by the US Dept. of Energy, Gleneden, OR,
USA, http://www.lanl.gov/orgs/hpc/salishan/, Apr. 2011.
[T49] Performance engineering of a petascale blood flow simulator, Invited seminar at the
University of California, Berkeley, Apr. 2011.
[T50] Performance principles and practice, Invited talk at the Wkshp. High-Performance
Computing and Emerging Architectures, Institute for Mathematics and Its Applications at the University of Minnesota, organized by L.A. Barba, E. Darve,
D. Keyes, http://www.ima.umn.edu/2010-2011/W1.10-14.11/ – Talk cancelled
due to weather, Jan. 2011.
[T51] Should I port my code to a GPU? Scientific Computing and Numerics (SCAN)
Seminar, organized by D. Bindel and A. Vladimirsky, Cornell University, http://www.math.cornell.edu/~scan/index-f10.html, Nov. 2010.
[T52] Opportunities and challenges in GPU co-processing, National Science Foundation
Workshop on Accelerators for Data-Intensive Applications, organized by V.
Prasanna and D. Bader, Oct. 2010.
[T53] On the opportunities and limits of GPU co-processing, Invited talk at the 2nd NSF
Wkshp. Frontiers of Multicore Computing (FMC-II), organized by S. Zhou, Y.
Yesha, and M. Halem, at the University of Maryland, Baltimore County, http://www.mc2.umbc.edu/workshops/fmc2.php, Sep. 2010.
[T54] Should I port my code to a GPU? Invited talk at the Clusters, Clouds, and Grids for
Scientific Computing (CCGSC), organized by J. Dongarra and B. Tourancheau,
Flat Rock, NC, USA, http://web.eecs.utk.edu/~dongarra/ccgsc2010/, Sep.
2010.
[T55] On the limits of and opportunities for GPU acceleration, Invited talk at the Scientific Discovery through Advanced Computing (SciDAC), US Department ofEnergy, Chattanooga, Tennessee, http : / / computing . ornl . gov / workshops /scidac2010, Jul. 2010.
[T56] Generalized n-body problems: From fast algorithms to fast code, Dagstuhl Seminar
10191 on Program Composition and Optimization: Autotuning, Scheduling,
Metaprogramming and Beyond, http://www.dagstuhl.de/en/program/calendar/
semhp/?semnr=10191, May 2010.
[T57] Optimizing and tuning the fast multipole method for state-of-the-art multicore architectures, SIAM Conf. Parallel Processing (SIAM PP), Seattle, WA, USA, http://www.siam.org/meetings/pp10/, Feb. 2010.
[T58] Parallel n-body solvers: Lessons learned in the multicore/manycore era, Yahoo! Labs,
Bangalore, India, Jan. 2010.
[T59] Parallel n-body solvers: Lessons learned in the multicore/manycore era, NeurIPS Wkshp. Large-scale Machine Learning: Parallelism and Massive Datasets, http://
www.select.cs.cmu.edu/meetings/biglearn09/, Vancouver, British Columbia,
Canada, Dec. 2009.

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25[T60] Toward scalable particle methods for physics and data analysis, Wkshp. Performance Analysis on Extreme-Scale Systems and Applications, at the Los Alamos
Computer Science Symposium (LACSS), Santa Fe, NM, USA, http : / / www .
lanl.gov/conferences/lacss/2009/agenda/workshops.shtml, Oct. 2009.
[T61] Toward a programming model for data analysis and mining, Minisymposium on
The Potential of Computational Algorithms in Datamining, at the SIAM Conf.
Computational Sci. Eng. (SIAM CSE), Miami, FL, USA, http://meetings.siam.
org/sess/dsp_programsess.cfm?SESSIONCODE=8103, Mar. 2009.
[T62] Recent progress in autotuning, Intel Corporation, Hudson, MA, USA, Aug. 2008.
[T63] Expressing data structure transformations for an automatically tuned sparse matrix library, Lua Workshop, George Washington University, Washington, DC, USA,
http://www.lua.org/wshop08.html#vuduc, Jul. 2008.
[T64] Tuning sparse matrix-vector multiply for multicore, Courant Institute, New York
University, USA, Apr. 2008.
[T65] Tuning sparse matrix-vector multiply for multicore, Oak Ridge National Laboratory, Oak Ridge, TN, USA, Apr. 2008.
[T66] Tuning sparse matrix-vector multiply for multicore, Minisymposium on Algorithms
and Optimizations Targeting Multi-Core Architectures, SIAM Conf. Parallel
Processing (SIAM PP), Atlanta, GA, USA, http://meetings.siam.org/sess/
dsp_programsess.cfm?SESSIONCODE=6762, Mar. 2008.
[T67] Automated data structure transformations for sparse matrix kernels, Minisymposium on Tools for the Development of High-performance Scientific Applications, SIAM Conf. Parallel Processing (SIAM PP), Atlanta, GA, USA, http :/ / meetings . siam . org / sess / dsp _ programsess . cfm ? SESSIONCODE = 6765, Mar.
2008.
[T68] OSKI: A library of automatically tuned sparse matrix kernels, Invited keynote
at the Int’l. Wkshp. Automatic Performance Tuning (iWAPT), Tōkyō, Japan,
http://iwapt.org/2006/, Oct. 2006.
[T69] OSKI: A library of automatically tuned sparse matrix kernels, Kyōtō University,
Japan, Oct. 2006.
[T70] Automatically tuned sparse matrix kernels, High-Performance Computing Seminar, Pomona College, USA, Sep. 2006.
[T71] The Optimized Sparse Kernel Interface, Bay Area Scientific Computing Day, Livermore, CA, USA, https://computation.llnl.gov/casc/workshops/bascd/2006/,
Mar. 2006.
[T72] Tutorial: The ROSE source-to-source infrastructure, IEEE Int’l. Conf. Parallel Architecture and Compilation Techniques (PACT), St. Louis, MO, USA, http ://www.informatik.uni- trier.de/~ley/db/conf/IEEEpact/IEEEpact2005.html,
Sep. 2005.
[T73] Automatically tuned sparse matrix kernels, University of Rome, Tor Vegata, Italy,
Jul. 2005.

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IV-E Grants and Contracts
IV-E.1 As Principal Investigator

14. Evaluation of communication and partitioning strategies for HPC applications
PI: Richard Vuduc
Co-PI: Jeffrey Young (CS)
Sandia National Laboratories
October 2022–presentAwarded: $100k per year
13. Faster, smaller, and more energy-efficient inference using codebook-based quantization
and FPGAs
PI: Richard Vuduc
Co-PIs: Jeffrey Young (CS+CSE), Michael Isaev (CS)
Facebook: Caffe2 Research Award Competition
September 2017
Awarded: $40k
12. A practical lower-bound on the execution time of certain stencil kernels
PI: Richard Vuduc
Co-PI: Jeffrey Young (CS+CSE)
ExxonMobil
July 2017–June 2018
Awarded: $92k
11. Post-GPU architectures and software for tensor computations
PI: Richard Vuduc
Co-PI: Jeffrey Young (CS+CSE)
Sandia National Laboratories, LDRD subcontract
October 2016–October 2018
Awarded: $184k
10. XPS: FULL: DSD: A Parallel Tensor Infrastructure (ParTI!) for Data Analysis
PI: Richard Vuduc
Co-PI: Jimeng Sun
National Science Foundation, Exploiting Parallelism and Scalability (XPS), Award
1533768
August 2015–July 2019Awarded: $750,000 over 4 years
9. SHF: Small: How much execution time, energy, and power does an algorithm need?
PI: Richard Vuduc (CoC/CSE)
National Science Foundation, Award 1422935
June 2014–May 2018
Funded: $515,375
8. A new theory of algorithm-architecture co-design
PI: Richard Vuduc
Defense Advanced Research Projects Agency, DARPA-BAA-11-34 (I2O Officewide)
Computer Science Study Group, Phase 3 option[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

27
December 2012–December 2014
Funded: $250,000
7. Compiler-support for Reverse Computation
PI: Richard Vuduc (CoC/CSE)
Lawrence Livermore National Laboratory
January 2011–May 2011
Funded: $49,496.00
6. Toward Exascale Pseudo-Spectral Codes for Turbulence Simulations on GPGPUs
PI: Richard Vuduc (CoC/CSE)
Co-PI: P.K. Yeung (CoE/AE)
Georgia Institute of Technology, Institute for Data and HPC Type I Seed Grant
Program
AY 2010–2011
Funded: $25,000
5. CAREER: Autotuning Foundations for Exascale Systems
PI: Richard Vuduc
National Science Foundation, Award 0953100
June 2010–May 2014
Funded: $460,000
4. Foundations for a High-Speed, Low-Power Data Analytics Appliance
PI: Richard Vuduc
Defense Advanced Research Projects Agency
Computer Science Study Group, Phase 2
June 2010–May 2012
Funded: $389,111
3. Autotuning Applications for Emerging Multicore Platforms
PI: Richard Vuduc
Co-PI: Chi-Keung Luk (Intel Corporation)
2009–2010
Funded: $50,000
2. Algorithms and Software with Tunable Parallelism
PI: Richard Vuduc
Defense Advanced Research Projects Agency
Computer Science Study Group (RA-08-52)
April 2009–April 2010
Funded: $91,748
1. THOR: A New Programming Model for Data Analysis and Mining
PI: Richard Vuduc
Co-PI: Alexander G. Gray (CoC/CSE)
National Science Foundation, Award 0833136
September 2008–August 2010
Funded: $686,647[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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IV-E.2 As Co-Principal Investigator

23. Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data
Movement Accelerators (RUN-DMX)
PI: Hyesoon Kim
Co-PIs: Bahar Asgari (University of Maryland), Haesun Park, Richard Vuduc, Jeffrey Young
National Science Foundation
September 2023–August 2026
Funded: $4 million
22. FORZA: Scalable Graph Analytics with Flow-Optimized, Reconfigurable Zones of Acceleration
PI: Vivek Sarkar
Co-PIs: Thomas M. Conte, Alex Daglis, Marty Deneroff (Lucata), David Donofrio(Tactical Computing Labs), Hyesoon Kim, Peter Kogge (Notre Dame), Richard
Vuduc, Jeffrey Young
Army Research Office + IARPA
July 2022–June 2025
Funded: $15 million
21. Collaborative Research: OAC Core: Robust, Scalable, and Practical Low Rank Approximation
PI: Haesun Park
Co-PIs: Grey Ballard (Wake Forest University), Richard Vuduc
National Science Foundation
July 2021–June 2024
Funded: $275k to Georgia Tech
20. CCRI: Medium: Rogues Gallery: A Community Research Infrastructure for Post-Moore
Computing
PI: Jeffrey Young
Co-PIs: Thomas Conte, Jennifer Hasler, Ada Gavrilovska, Jason Riedy, Richard
Vuduc
National Science Foundation
July 2020–June 2023
Funded: $1.3 million over 3 yars
19. SOLSTiCe: Statistical Opportunities for Locality in Streaming Tensor Computations
PI: Eric Phipps (previously: Tamara Kolda) (Sandia National Laboratories)
Co-PIs: Richard Vuduc
Army Research Office, W911NF-19-S-0007
April 2020–April 2024
Funded: $120k per year to Georgia Tech
18. MRI: Acquisition of an HPC System for Data-Driven Discovery in Computational Astrophysics, Biology, Chemistry, and Materials Science
PI: Srinivas Aluru
Co-PIs: Richard Vuduc, Deirdre Shoemaker, David Sherrill, Surya Kalidindi
National Science Foundation
October 2018–September 2021

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Funded: $3.7 million over 3 years
17. DDARING: Dynamic Data-Aware Reconfiguration, Integration, and Generation
PI: Vivek Sarkar
Co-PIs: David Bader, Tom Conte, Richard Vuduc (GT); Deming Chen, Wen-mei
Hwu (UIUC); Scott Malkhe (Michigan)
Defense Advanced Research Projects Agency (DARPA), Software-Defined Hardware
June 2018–November 2020
Funded: $4.5 million over 2 years
16. CDS&E: SuperSTARLU–STacked, AcceleRated Algorithms for Sparse Linear Systems
PI: Jeffrey Young (CS+CSE)
Co-PIs: Jason Riedy (CSE), Richard Vuduc
National Science Foundation, Award 1710371
August 2017–July 2020
Awarded: $500,000
15. PFI:BIC: Next Generation Real-Time Distributed Manufacturing Service Systems Using
Digital Process Planning and GPU-Accelerated Parallel Computing
PI: Thomas Kurfess (ME)
Co-PIs: Francis Durso (Psychology), Christopher Saldana (ME), Cassandra Telenko (ME), Richard Vuduc
National Science Foundation, Award 1631803
September 2016–August 2019Awarded: $1 million
14. EAGER-DynamicData: Power Aware Data Driven Distributed Simulation on Micro-Cluster
Platforms
PI: Richard Fujimoto
Co-PIs: Richard Vuduc, Michael Hunter (CEE)National Science Foundation, EAGER-DynamicData, Award 1462503
September 2015–August 2017
Awarded: $240,000 over 2 years
13. EAGER: Scaling Up Machine Learning with Virtual Memory
PI: Polo Chau
Co-PIs: Richard Vuduc
National Science Foundation, Award 1551614
September 2015–August 2017
Awarded: $184,904 1 year
12. Collaborative Research: EMBRACE: Evolvable Methods for Benchmarking Realism throughApplication and Community Engagement
PI: David A. Bader & Jack Dongarra (UTK)
Co-PIs: E. J. Riedy, Richard Vuduc, Piotr Luszczek (UTK)
National Science Foundation, Benchmarks of Realistic Scientific Application Performance (BRAP) of Large-scale Computing Systems, Award 1535058
August 2015–July 2018
Awarded: $250,000 over 2 years
11. Vancouver2: Vancouver2: Improving Programmability of Contemporary HeterogeneousArchitectures[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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PI: Jeffrey Vetter (CoC/CSE + ORNL)
Co-PIs: Wen-Mei Hwu (University of Illinois, Urbana-Champaign), Allen Maloney (University of Oregon), Richard Vuduc (CoC/CSE)
Department of Energy, X-Stack
June 2014–May 2017
Funded: $270,000 (to GT)
10. SI2-SSI: Collaborative: The XScala Project: A community repository for model-driven design and tuning of data-intensive applications for extreme-scale accelerator-based systems
PI: David A. Bader (CoC/CSE)
Co-PIs: Viktor Prasanna (University of Southern California), Edward J. Riedy(CoC/CSE), Richard Vuduc (CoC/CSE)
National Science Foundation, Award 1339745
October 2013–September 2018
Funded: $1,188,710
9. XPS:CLCCA: Optimizing heterogeneous platforms for unstructured parallelism
PI: Sudha Yalamanchili (CoE/ECE)
Co-PIs: Hyesoon Kim (CoC/CS), Richard VUduc (CoC/CSE)
National Science Foundation, Award 1337177
September 2013–August 2016
Funded: $735,055
8. CPS: Synergy: Converting multi-axis machine tools into subtractive 3D printers by using
intelligent discrete geometry data structures designed for parallel and distributed computing
PI: Thomas Kurfess (CoE/ME)
Co-PI: Richard Vuduc (CoC/CSE)
National Science Foundation, Award 1329742
September 2013–August 2016
Funded: $969,557
7. CCF: SHF: Small: Localization and Explanation of Faults in Concurrent Software
PI: Mary Jean Harrold (CoC/CS)
Co-PI: Richard Vuduc (CoC/CSE)
National Science Foundation, Award 1116210
July 2011–June 2014
Funded: $490,496
6. MLPACK: Scalable Machine Learning Software for Large-Scale Science
PI: Alexander Gray
Co-PIs: Richard Vuduc, Haesun Park, Yu (Cathy) Jiao, John McDonald, Gordon
Richards, Alexander Shapiro, Jeffrey Vetter
Georgia Institute of Technology, Institute for Data and HPC Type II Seed Grant
Program
AY 2010–2011
Funded: $65,000
5. Vancouver: Designing a Next-Generation Software Infrastructure for Heterogeneous Exascale Computing
PI: Jeffrey Vetter (CoC/CSE + ORNL)
Co-PIs: Wen-Mei Hwu (University of Illinois, Urbana-Champaign), Allen Mal-[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

31
oney (University of Oregon), Richard Vuduc (CoC/CSE)
Department of Energy, X-Stack
September 2010–August 2013
Funded: $339,130 (GT), $3,000,000 (total)
4. Collaborative Research: Establishing a Center for Hybrid Multicore Productivity Research
PI: David A. Bader (CoC/CSE), Thomas Conte (CoC/CS)
Co-PIs: Nathan Clark (CoC/CS), Ada Gavrilovska (CoC/CS), Hyesoon Kim (CoC/CS),
Richard Vuduc (CoC/CSE)
National Science Foundation, Award 0934114
August 2009–July 2010
Funded: $275,000
3. From Algorithms to Hardware: Blueprints to Support Cooperative Hardware/Software
Prefetching in Data-Intensive Applications
Co-PIs: Hyesoon Kim, Richard Vuduc
Raytheon Faculty Fellowship
August 2009–May 2010
Funded: $39,000
2. Qameleon: Hardware/software Co-operative Automated Tuning for Heterogeneous Architectures
PI: Hyesoon Kim (CoC/CS)
Co-PI: Richard Vuduc (CoC/CSE)
National Science Foundation, joint with Semiconductor Research Corporation,Award 0903447
August 2009–July 2012
Funded: $357,810
1. Collaborative Research: Establishing an I/UCRC Center for Multicore Productivity Research (CMPR)
PI: Milt Halem (University of Maryland, Baltimore County)
Co-PIs: David A. Bader (CoC/CSE), Paul Woodward (University of Minnesota),
Paul Sheldon (University of California, San Diego)
Contributors: Richard Vuduc (CoC/CSE)
National Science Foundation, Award 0831110
August 2008
Funded: $10,000
IV-E.3 As Senior Personnel or Contributor

1. Evaluating Memory-centric Architectures for High Performance Data Analysis
PI: E.J. Riedy
Co-PIs: David A. Bader, Thomas M. Conte (CoC/CS)
Senior Personnel: Jeffrey Young (CoC/CS), Richard Vuduc
IARPA
August 2017–August 2018
Awarded: $662,525[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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V Education
In recognition of my teaching service, I have received a Thank-a-Teacher acknowledgement from at least one student in almost every semester since Spring 2009.

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V-A Courses Taught
When

CourseSize

Spring ’20

VIP x602: Team Phoenix

11

Spring ’20

CSE 6040-OMS: Comp for Data Analysis

924

Spring ’20

CSE 6730: Modeling & Simulation

89

Spring ’20

CX 4230: Computer Simulation

31

Fall ’19

CSE 6040-OMS: Comp for Data Analysis

666

Fall ’19

CSE 6040: Comp for Data Analysis

63

Fall ’19

CSE 6001: Intro to CSE

19Spring ’19

CSE 6730: Modeling & Simulation

112

Spring ’19

CX 4230: Computer Simulation

57
≈ 700

Fall ’18

CSE 6040-OMS: Comp for Data Analysis

Fall ’18

CSE 6040: Comp for Data Analysis

72

Fall ’18

CSE 6001: Intro to CSE

23

Spring ’18

CSE 6040-OMS: Comp for Data Analysis

348

Spring ’18

CSE 6220-OMS: Intro to HPC

120

Fall ’17

CSE 6220-OMS: Intro to HPC

64

Fall ’17

CSE 6040-OMS: Comp for Data Analysis

141

Fall ’17

CSE 6040: Comp for Data Analysis

60

Fall ’17
Spring ’16

CSE 6001: Intro to CSE
CSE 6220-OMS: Intro to HPC

836

Spring ’16

CX 4230: Modeling & simulation

29

Fall ’15

CSE 6220-OMS: Intro to HPC

41

Fall ’15

CSE 6040: Comp for Data Analysis

37

Fall ’15

CSE 6001: Intro to CSE

18

Spring ’15

CX 4230: Modeling & simulation

32

Fall ’14

CSE 6001: Intro to CSE

20Fall ’14

CSE 6230: HPC Tools and Applications

41

Fall ’13

CSE 6001: Intro to CSE

9Fall ’13

CSE 6230: HPC Tools and Applications

36

Spring ’13

CSE 6220: Intro to HPC

60

Fall ’12

CSE 6230: HPC Tools and Applications

55

Spring ’12

CS 4225: Intro to HPC

12Fall ’11

CSE 6230: HPC Tools & Apps

49

Spring ’11

CS 4335 / CSE/ECE 6730: Modeling and Simulation

36

Fall ’10

CSE 8803 HPC: Hot Topics in Parallel Computing

12

Spring ’10

CS 4225: Introduction to HPC

Fall ’09

CSE 6230: HPC Tools and Applications

Spring ’09

CS 4643 / MATH 4641: Numerical Analysis II(cancelled)

Fall ’08

CSE / CS 6230: HPC Tools and Applications

35

Spring ’08

CSE 8803 PNA: Parallel Numerical Algorithms

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V-B Individual Student GuidanceNote: “WATN?” = “Where are they now?” (as of last communication withthat student)
V-B.1 Ph.D. Students

9. Casey Battaglino (CoC/CSE) — Fall 2010–present
8. Benjamin Cobb (CoC/CSE), co-advising with Haesun Park (CoC/CSE) — Spring
2022–present
7. Max Hawkins (CoC/CSE), co-advising with Spencer Bryngelson (CoC/CSE) —Fall 2023–present
6. Souvadra Hati (CoC/CSE), co-advising with Srinivas Aluru (CoC/CSE) — Fall
2022–present
5. Koby Hayashi (CoC/CSE), co-advising with Haesun Park (CoC/CSE) — Fall 2018–present
4. Michael Isaev (CoC/CS) — Spring 2017–present
3. Sara Karamati (CoC/CSE) — Fall 2014–present
2. Trevor McCrary (CoC/CSE) — Fall 2023–present
1. Vijay Thakkar (CoC/CS) — Spring 2020–present1
Graduated or no longer supervising
16. Daniel Browne (CoC/CSE) — Spring 2012–Spring 2016
15. Aparna Chandramowlishwaran (CoC/CSE) — Fall 2008–Fall 2013
— Dissertation: The fast multipole method at exascale— WATN?: Associate Professor at UC Irvine
14. Xin Chen (CoC/CS) — Fall 2016–Spring 2020
— Dissertation: Five-axis coverage path planning with deep reinforcement learning and fast parallel collision detection— WATN?: Florida International University
13. Jee Whan Choi (CoE/ECE) — Fall 2008–Spring 2015
— Dissertation: Power and performance modeling for high-performance computing algorithms— WATN?: Postdoc at IBM T.J. Watson Research Lab; Assistant Professor at University of Oregon
12. Kenneth Czechowski (CoC/CSE) — Fall 2011–Spring 2019 — Dissertation: Diagnosing performance bottlenecks in HPC applications— WATN?: Main Street Hub
11. Marat Dukhan (CoC/CSE) — Fall 2012–Spring 2021
— Dissertation: Performance primitives for artificial neural networks— WATN?: Google
1 I originally met and began working with Vijay in Spring 2020, when he was an M.S. student at Georgia

Tech.

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10. Srinivas Eswar (CoC/CS), co-advising with Haesun Park (CoC/CSE) — Fall 2016–
Summer 20222— Dissertation: Scalable data mining via constrained low-rank approximation— WATN?: Argonne National Laboratory as a Wilkinson Fellow
9. Raghul Gunasekaran (CoC/CSE) — Fall 2010–Fall 2012
8. Mohammad Hossain (CoC/CS) — Spring 2014–Fall 2016
— Dissertation: Voxel-based offsetting at high resolution with tunable speed andprecision using hybrid dynamic trees— WATN?: Google
7. Cong Hou (CoC/CS) — Summer 2009–Spring 2013
— Dissertation: Automated synthesis for program inversion— WATN?: Waymo
6. Jiajia Li (CoC/CSE) — Spring 2015–Summer 2018
— Dissertation: Scalable tensor decompositions in high performance computing
environments— WATN?: Assistant Professor, College of William and Mary
5. Patrick Lavin (CoC/CSE) — Fall 2017–Summer 2023
— Dissertation: Multifidelity memory system simulation— WATN? Sandia National Laboratories
4. Sangmin Park (CoC/CS; M.J. Harrold) — Fall 2008–Summer 2014
— Dissertation: Effective fault localization techniques for concurrent software— WATN?: MeetElise
3. Piyush Kumar Sao (CoC/CSE) — Fall 2011–Spring 2018
— Dissertation: Scalable and resilient sparse linear solvers— WATN?: Oak Ridge National Laboratories
2. Zhengkai Wu (CoC/ECE) — Spring 2014–Summer 2017
1. Chunxing Yin (CoC/CSE) — Fall 2019–Summer 2023
— Dissertation: Fast and compact neural networks via tensor-train reparameterization— WATN?: Meta
Co-advised
1. George Vulov (CoC/CSE; R. Fujimoto) — Fall 2009–Fall 2011
V-B.2 M.S. Students

1. Trevor McCrary (CoC/CSE) — Spring 2022–present
Graduated and/or no longer supervising
11. Vaibhav Bedia (CoC/CS) — Spring 2014–Fall 2014
— WATN?: Apple
10. Stefan Henneking (CoC/CS) — Spring 2015–Spring 2016
— Thesis: Implementation and analysis of a parallel vertex-centered finite element
2 I originally met and began working with Srinivas in Spring 2015, when he was an M.S. student at Georgia

Tech.

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37segmental refinement multigrid solver— Committee: Richard Vuduc (advisor, CoC/CSE), Edmond Chow (CoC/CSE),
Mark Adams (Lawrence Berkeley National Laboratory)— Now at: UT Austin (Postdoc at ICES)
9. Mitesh Kothari (CoC/CSE) — Spring 2020–Fall 2020 — WATN?: Verizon Media
8. Robert Lee (CoC/CSE) — Spring 2014–Spring 2016
— WATN?: Tower Research Capital (NYC)
7. Wafa Louhichi (CoC/CSE; co-advising with Tom Kurfess in CoE/ME) — Spring
2018–Spring 2019 — WATN?: Wells Fargo
6. Chris McClanahan (CoC/CS) — Fall 2010–Spring 2011
— WATN?: AccelerEyes
5. David S. Noble, Jr. (CoC/CSE) — Fall 2010–Spring 2013
— WATN?: CD-Adapco
4. Shruti Padamati (CoC/CS) — Spring 2014
3. Niveditha Raveendran (CoC/CS) — Spring 2014
— WATN?: Oracle
2. Archana Venkatesh (CoC/CS) — Spring 2014–Fall 2014
— WATN?: Apple
1. Sundaresan Venkatasubramanian (CoC/CS) — Spring 2008
— Thesis: Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU
platforms.
— Committee: Richard Vuduc (advisor, CoC/CSE), Hyesoon Kim (CoC/CS), Jeffrey Vetter (CoC/CSE & ORNL)— WATN?: Amazon.com
V-B.3 Undergraduate Students

Students are from Georgia Tech unless otherwise noted.
23. Marissa Sorkin — Spring 2021–Spring 2023 — WATN?: MS at Georgia Tech
22. Sudhanshu Agarwal — Fall 2019–present
21. Arnab Dey — Fall 2019–Fall 2020 — WATN?: LinkedIn
20. Aman Jain — Fall 2019–present
19. Yuuna Hoshi — Fall 201918. Yotam Kanny — Fall 201917. Judith Brennan — Summer 2018–Fall 201816. Shaowei Zhu — Fall 2017–Spring 2018 — WATN?: Amazon
15. DeVon Ingram — Spring 2016
14. Chaitanya Aluru (UC Berkeley) — Summer 2014
13. Lawrence Moore — Summer 2014
12. Austin Tucker (Morehouse College) — Summer 2013
11. Hyangjin Lee — Summer 2013
10. Lesly Sandoval (Georgia State University) — Summer 2011
9. Gurbinder Gill Singh (IIT-Roorkee) — Summer 2011
8. Ashish Narasimham — Fall 2009–Fall 2011 — WATN? Google Cloud
7. Japnik Singh (IIT-Bombay) — Summer 2010
6. Piyush Kumar Sao (IIT-Madras) — Summer 2010[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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5. Nimit Nigania (IIT-Madras) — Summer 2009
4. Amik Singh (IIT-Roorkee) — Summer 2009
3. Jada Jeffries (Hampton University) — Summer 2009 — WATN?: AT&T
2. Gaurav Chadha (IIT-Guwahati) — Summer 2008
— WATN?: Ph.D. program at U. Michigan, EECS Dept.
1. Lauren Gracia (U. Houston–Downtown) — Summer 2008
— WATN?: Chevron
V-B.4 Service on Thesis or Dissertation Committees

Ph.D. thesis examining committees (internal to Georgia Tech)
193. Kalsuda Lapborisuth – Advisor: Srinivas Aluru — CoC/CSE, Qualifying Exam,
Fall 2023
192. Souvadra Hati – Co-advisors: Richard Vuduc and Srinivas Aluru — CoC/CSE,
Qualifying Exam, Fall 2023
191. Chunxing Yin – Advisor: Richard Vuduc — CoC/CSE, Thesis Defense, Summer
2023
190. Patrick Lavin – Advisor: Richard Vuduc and Jeffrey S. Young — CoC/CSE, Thesis
Defense, Summer 2023
189. Shruti Shivakumar – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Summer 2023
188. Dennis Oztekin – Advisor: Cyrus Aidun — CoE/ME, Thesis Proposal Defense,
Summer 2023
187. Xiaojing An – Advisor: Ümit Çatalyürek — CoC/CSE, Thesis Defense, Fall 2022
186. Caleb Harris – Advisor: Dmitris Mavris — AE/CSE, Thesis Defense, Fall 2022
185. Johnie Sublett – Advisor: Dmitris Mavris — AE/CSE, Thesis Proposal Defense,
Summer 2022
184. Chunxing Yin – Advisors: Richard Vuduc and Haesun Park — CoC/CS, Thesis
Defense, Summer 2022
183. Raghul Duggal – Advisor: Polo Chau — CoC/CSE, Thesis Defense, Summer 2022
182. Chunxing Yin – Advisor: Richard Vuduc — CoC/CS, Thesis Proposal Defense,
Summer 2022
181. Shruti Shivakumar – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal Defense, Spring 2022
180. Xiaojing An – Advisor: Ümit Çatalyürek — CoC/CSE, Thesis Proposal Defense,
Spring 2022
179. Mikhail Isaev – Advisor: Richard Vuduc — CoC/CS, Thesis Proposal Defense,
Spring 2022
178. Eric Qin – Advisor: Tushar Krishna — CoE/ECE, Thesis Defense, Spring 2022
177. Patrick Lavin – Advisor: Richard Vuduc — CoC/CSE, Thesis Proposal Defense,
Spring 2022
176. Somdut Roy – Advisor: Michael Hunter — CoE/CEE, Thesis Proposal Defense,
Spring 2022
175. Neda Tavakoli – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal Defense,
Fall 2021[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)39174. Abdurrahman (Apo) Yassar – Advisor: Ümit Çatalyürek — CoC/CSE, Thesis Defense, Fall 2021
173. Ankit Srivastava – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Summer
2021
172. Kiran Ravikumar – Advisor: P.-K. Yeung SEP CoE/AE, Thesis Defense, Summer
2021
171. Eric Qin – Advisor: Tushar Krishna — CoE/ECE, Thesis Proposal Defense, Spring
2021
170. Yufei Xu – Advisor: Srinivas Peeta (GT/CEE) — CoE/CSE-CEE, Qualifying Exam,
Spring 2021
169. Prasun Gera – Advisor: Hyesoon Kim — CoC/CS, Thesis Defense, Spring 2021
168. Marat Dukhan – Advisor: Richard Vuduc — CoC/CSE, Thesis Defense, Spring2021
167. Ankit Srivastava – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal, Spring
2021
166. Ben Cobb – Advisor: Ümit Çatalyürek — CoC/CSE, Qualifying Exam, Spring 2021
165. Kaan Sancak – Advisor: Ümit Çatalyürek — CoC/CSE, Qualifying Exam, Fall 2020
164. Benjamin Cobb – Advisor: Ümit Çatalyürek — CoC/CSE, Qualifying Exam, Fall
2020
163. Koby Hayashi – Advisors: Haesun Park and Richard Vuduc — CoC/CSE, Qualifying Exam, Summer 2020
162. Abudurrahman (Apo) Yaşar – Advisor: Ümit Çatalyürek — CoC/CSE, Thesis Proposal, Summer 2020
161. Prithayan Barua – Advisor: Vivek Sarkar — CoC/CSE, Thesis Defense, Spring2020
160. Muyuan Li – Advisor: Ken Brown — CoC/CSE, Thesis Defense, Spring 2020
159. Xin Chen – Advisor: Richard Vuduc — CoC/CSE, Thesis Defense, Spring 2020
158. Philipp Witte – Advisor: Felix Herrmann — CoC/CSE, Thesis Defense, Spring2020
157. Natalie Brown – Advisor: Ken Brown — CoS/Physics, Thesis Defense, Spring2020
156. Hanjun Dai – Advisor: Le Song — CoC/CSE, Thesis Defense, Fall 2019155. Chad Kersey – Advisor: Sudha Yalamanchili and Hyesoon Kim — CoE/ECE, Thesis Defense, Fall 2019
154. Prasun Gera – Advisor: Hyesoon Kim — CoC/CS, Thesis Proposal, Fall 2019153. Aradhya Biswas – Advisor: Richard Fujimoto — CoC/CSE, Thesis Defense, Spring2019
152. Hanjun Dai – Advisor: Le Song — CoC/CSE, Thesis Proposal, Summer 2019
151. Anirban Chatterjee – Advisor: Yichang (James) Tsai — CoE/CSE-CEE, Thesis
Defense, Summer 2019
150. Yuanzheng Zhu – Advisor: Cyrus Aidun — CoE/ME, Thesis Defense, Summer
2019
149. Philipp Witte – Advisor: Felix Herrmann — CoC/CSE, Thesis Proposal, Summer
2019
148. Aradhya Biswas – Advisor: Richard Fujimoto — CoC/CSE, Thesis Proposal, Spring
2019[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)40147. Ioakeim (Kimis) Perros – Advisor: Jimeng Sun — CoC/CSE, Thesis Defense, Spring2019
146. Patrick Flick – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Spring 2019145. Rahul Nihalani – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Spring
2019
144. Xin Chen – Advisor: Richard Vuduc — CoC/CSE, Thesis Proposal, Spring 2019143. Anirban Chatterjee – Advisor: Yichang (James) Tsai — CoE/CSE-CEE, Thesis
Proposal, Spring 2019142. Nagakishore Jammula – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Fall2018
141. Patrick Flick – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal, Spring 2018
140. Kasimir Gabert – Advisor: David Bader — CoC/CSE, Qualifying Exam, Spring
2018
139. SaBra Neal – Advisor: Richard Fujimoto — CoC/CSE, Thesis Defense, Spring2018
138. Tony Pan – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Spring 2018
137. Vipin Sachdeva – Advisor: David Bader — CoC/CSE, Thesis Defense, Spring 2018
136. Qi Ge – Advisor: John Wise — CoS/Physics, Thesis Defense, Spring 2018135. Anirban Chatterjee – Advisor: Yichang (James) Tsai — CoE/CSE-CEE, Qualifying
Exam, Spring 2018134. Patrick Lavin – Co-advisors: Richard Vuduc and Jeff Young — CoC/CSE, Qualifying Exam, Spring 2018133. Srinivas Eswar – Co-advisors: Richard Vuduc and Haesun Park — CoC/CSE, Qualifying Exam, Spring 2018132. Mark Jackson – Advisor: Richard Fujimoto — CoC/CSE, Qualifying Exam, Spring
2018
131. Jiajia Li – Advisor: Richard Vuduc — CoC/CSE, Thesis Proposal, Spring 2018130. Jordi Wolfson-Pou – Advisor: Edmond Chow — CoC/CS, Qualifying Exam, Fall2017
129. Siddharth Biswal – Advisor: Jimeng Sun — CoC/CS, Qualifying Exam, Fall 2017
128. Sungtae An – Advisor: Jimeng Sun — CoC/CS, Qualifying Exam, Fall 2017127. Lluis Munguia – Advisor: David Bader — CoC/CSE, Thesis Defense, Fall 2017126. Chao Chen – Advisor: Greg Eisenhauer — CoC/CS, Qualifying Exam, Fall 2017125. Yuanzheng Zhu – Advisor: Cyrus Aidun — CoE/ME, Thesis Proposal, Fall 2017124. SaBra Neal – Advisor: Richard Fujimoto — CoC/CSE, Thesis Proposal, Fall 2017123. Nagakishore Jammula – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal,
Fall 2017122. Girish Mururu – Advisor: Santosh Pande — CoC/CS, Qualifying Exam, Fall 2017121. Muyuan Li – Advisor: Ken Brown — CoC/CSE/Chem, Qualifying Exam, Fall 2017120. Xin Xing – Advisor: Edmond Chow — CoS/CSE/Math, Qualifying Exam, Spring
2017
119. Chao Shi – Advisor: John Wise — CoS/Physics, Thesis Defense, Spring 2017118. Tony Pan – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal, Spring 2017117. Lifeng Nai – Advisor: Hyesoon Kim — CoC/CS, Thesis Defense, Fall 2016
116. Joo Hwan Lee – Advisor: Hyesoon Kim — CoC/CS, Thesis Defense, Fall 2016
115. Anshuman Goswami – Advisor: Karsten Schwan and Matthew Wolf (GT/ORNL)[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

41— CoC/CS, Thesis Defense, Fall 2016
114. Eric Anger – Advisor: Sudha Yalamanchili — CoE/ECE, Thesis Defense, Fall 2016
113. Jin Wang – Advisor: Sudha Yalamanchili — CoE/ECE, Thesis Defense, Fall 2016112. Marat Dukhan – Advisor: Richard Vuduc — CoC/CS, Thesis Proposal, Fall 2016111. Caleb Robinson – Advisor: Bistra Dilkina — CoC/CSE, Qualifying Exam, Fall 2016110. Jordi Wolfson-Pou – Advisor: Edmond Chow — CoC/CSE, Qualifying Exam, Fall2016
109. Rahul Nihalani – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal, Fall 2016
108. Ankit Srivastava – Advisor: Srinivas Aluru — CoC/CSE, Qualifying Exam, Fall2016
107. Mohammad Hossain – Advisor: Richard Vuduc — CoC/CS, Thesis Defense, Fall2016
106. Piyush Sao – Advisor: Richard Vuduc — CoC/CSE, Thesis Proposal, Fall 2016
105. Philip Pecher – Advisor: Richard Fujimoto — CoE/CSE-ISyE, Thesis Proposal,
Fall 2016
104. Syed Minhaj Hassan – Advisor: Sudha Yalamanchili — CoE/ECE, Thesis Defense,
Fall 2016
103. Chris Forster – Advisor: Marc Smith — CoE/ME, Thesis Defense, Summer 2016
102. Matthew Kinsey – Advisor: Pablo Laguna — CoS/Physics, Thesis Defense, Summer 2016
101. Dipanjan Sengupta – Advisor: Karsten Schwan / Matthew Wolf — CoC/CS, Thesis Defense, Summer 2016
100. James Fairbanks – Advisor: David Bader — CoC/CSE, Thesis Defense, Spring
2016
99. Tushar Kumar – Advisor: Sudha Yalamanchili — CoE/ECE, Thesis Defense, Spring
2016
98. Ramakrishnan (Ramki) Kannan – Advisor: Haesun Park — CoC/CS, Thesis Defense, Spring 2016
97. Haicheng Wu – Advisor: Sudha Yalamanchili — CoE/ECE, Thesis Defense, Spring
2016
96. Jai Dayal – Advisor: Karsten Schwan / Matthew Wolf — CoC/CS, Thesis Proposal, Spring 2016
95. Joo Hwan Lee – Advisor: Hyesoon Kim — CoC/CS, Thesis Proposal, Spring 2016
94. Anshuman Goswami – Advisor: Karsten Schwan / Matthew Wolf — CoC/CS,
Thesis Proposal, Spring 2016
93. Jin Wang – Advisor: Sudha Yalamanchili — CoC/ECE, Thesis Proposal, Spring2016
92. Eric Anger – Advisor: Sudha Yalamanchili — CoC/ECE, Thesis Proposal, Spring2016
91. Parsa Banihashemi – Advisor: Kenneth Will — CoE/CEE, Thesis Defense, Fall
2015
90. Naila Farooqui – Advisor: Karsten Schwan — CoC/CS, Thesis Defense, Fall 201589. Brian Railing – Advisor: Tom Conte — CoC/CS, Thesis Defense, Fall 2015
88. Ryan Curtin – Advisor: Mark Clements — CoE/ECE, Thesis Defense, Summer
2015
87. Aaron Howell – Advisor: Cyrus Aidun — CoE/ME, Thesis Defense, Spring 2015[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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86. Indranil Roy – Advisor: Srinivas Aluru — CoC/CSE, Thesis Defense, Spring 201585. Jee Whan Choi – Advisor: Richard Vuduc — CoE/ECE, Thesis Defense, Spring2015
84. Xing Liu – Advisor: Edmond Chow — CoC/CSE, Thesis Defense, Fall 2014
83. Naila Farooqui – Advisor: Karsten Schwan — CoC/CS, Thesis Proposal, Fall 201482. Parsa Banihashemi – Advisor: Kenneth Will — CoE/CEE, Thesis Proposal, Fall
2014
81. Nagesh B. Lakshminarayana – Advisor: Hyesoon Kim — CoE/ECE, Thesis Defense, Fall 2014
80. Sangmin Park – Advisor: Mary Jean Harrold & Richard Vuduc — CoC/CS, Thesis
Defense, Summer 2014
79. Kaushik Ravichandran – Advisor: Santosh Pande — CoC/CS, Thesis Defense,
Summer 2014
78. Indranil Roy – Advisor: Srinivas Aluru — CoC/CSE, Thesis Proposal, Spring 2014
77. Kartik Iyer – Advisor: P.-K. Yeung — CoC/AE, Thesis Defense, Spring 201476. Seongcheol Baek – Advisor: Jongman Kim — CoE/ECE, Thesis Defense, Spring2014 —
75. Vipin Sachdeva – Advisor: David Bader — CoC/CSE, Thesis Proposal, Spring
2014
74. Zhaoming Yin – Advisor: David Bader — CoC/CSE, Thesis Defense, Spring 2014
73. Xing Liu – Advisor: Edmond Chow — CoC/CSE, Thesis Proposal, Spring 2014
72. Oded Green – Advisor: David Bader — CoC/CSE, Thesis Defense, Spring 2014
71. Fang Zheng – Advisor: Karsten Schwan — CoC/CS, Thesis Defense, Spring 2014
70. Yu Tomita – Advisor: Ken Brown — CoS/CSE-CHEM, Thesis Defense, Spring2014
69. Ryan Curtin – Advisor: Alex Gray — CoE/ECE, Thesis Proposal, Spring 2014
68. Aaron Howell, College of Engineering, School of Mechanical Engineering — Thesis Proposal, Fall 2013 — Advisor: Cyrus Aidun (CoC/ME)
67. Philip Pecher, College of Engineering, School of Industrial and Systems Engineering — Qualifying Exam (CSE-ISyE), Fall 2013 — Advisor: Richard Fujimoto
66. Aftab Patel, College of Computing, School of Computational Science and Engineering — Qualifying Exam, Fall 2013 — Advisor: Edmond Chow
65. Lluis Munguia, College of Computing, School of Computational Science and Engineering — Qualifying Exam, Fall 2013 — Advisor: David A. Bader
64. Jane Kang, College of Engineering, School of Mechanical Engineering — Thesis
Proposal, Fall 2013 — Advisor: Cyrus Aidun
63. Junghee Lee, College of Engineering, School of Electrical and Computer Engineering — Thesis Proposal, Fall 2013 — Advisor: Jongman Kim
62. James Fairbanks, College of Computing, School of Computational Science and Engineering — Qualifying Exam, Fall 2013 — Advisor: David A. Bader
61. Nagesh Lakshminarayana, College of Computing, School of Computer Science —Thesis Proposal, Fall 2013 — Advisor: Hyesoon Kim (CoC/CS)
60. Hana Strakova, University of Vienna — Thesis Defense, Fall 2013 — Advisor: Wilfried Gansterer
59. Fang Zheng, College of Computing, School of Computer Science — Thesis Defense, Summer 2013 — Advisor: Karsten Schwan (CoC/CSE)[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)43
58. William March, CoC, CSE — Thesis Defense, Spring 2013 — Advisor: Alexander
Gray (CoC/CSE)
57. Ryan Riegel, College of Computing, School of Computational Science and Engineering — Thesis Defense, Spring 2013 — Advisor: Alexander Gray (CoC/CSE)
56. Chris Forster, College of Engineering, School of Mechanical Engineering — Thesis
Proposal, Spring 2013 — Advisor: Marc Smith (CoE/ME)
55. Kaushik Ravichandran, College of Computing, School of Computer Science —Thesis Proposal, Spring 2013 — Advisor: Santosh Pande (CoC/CSE)
54. Zhaoming Yin, College of Computing, School of Computational Science and Engineering — Thesis Proposal, Spring 2013 — Advisor: David Bader (CoC/CSE)
53. Da Kuang, College of Computing, School of Computational Science and Engineering — Thesis Proposal, Spring 2013 — Advisor: Haesun Park (CoC/CSE)
52. Aparna Chandramowlishwaran, College of Engineering, School of Electrical andComputer Engineering — Thesis Defense, Spring 2013 — Advisor: Richard Vuduc(CoC/CSE)
51. Cong Hou, College of Engineering, School of Electrical and Computer Engineering — Thesis Defense, Spring 2013 — Advisor: Richard Vuduc (CoC/CSE)
50. Oded Green, College of Computing, School of Computational Science and Engineering — Thesis Proposal, Spring 2013 — Advisor: David Bader (CoC/CSE)
49. David Ediger, College of Engineering, School of Electrical and Computer Engineering — Thesis Defense, Spring 2013 — Advisor: David Bader (CoC/CSE)
48. Sooraj Bhat, College of Computing, School of Computer Science — Thesis Defense, Spring 2013 — Advisor: Alexander G. Gray (CoC/CSE)
47. Nan Du, College of Computing, School of Computational Science and Engineering(CSE) — Qualifying Exam, Spring 2013 — Advisor: Le Song (CoC/CSE)
46. Marat Dukhan, College of Computing, School of Computational Science and Engineering (CSE) — Qualifying Exam, Spring 2013 — Advisor: Richard Vuduc (CoC/CSE)
45. Anita Zakrzewska, CoC, CSE — Qualifying Exam, Spring 2012 — Advisor: David
A. Bader (CoC/CSE)
44. Kartik Iyer, College of Engineering (CoE), Aerospace Engineering (AE) — Thesis
Proposal, Fall 2012 — Advisor: P.-K. Yeung (CoE/AE)
43. Ying Li, College of Computing, Computational Science and Engineering (CSE) —Thesis Defense, Fall 2012 — Advisor: Richard Fujimoto (CoC/CSE)
42. Sunpyo Hong, CoE, Electrical and Computer Engineering (ECE) — Thesis Defense, Fall 2012 — Advisor: Hyesoon Kim (CoC/CS)
41. Dongryeol Lee, CoC/CSE — Thesis Defense, Spring 2012 — Advisor: Alexander
Gray (CoC/CSE)
40. Abtin Rahimian, CoC, CSE — Thesis Defense, Spring 2012 — Advisor: George
Biros (CoC/CSE; UT Austin)
39. Oded Green, CoC, CSE — Qualifying Exam, Spring 2012 — Advisor: David A.
Bader (CoC/CSE)
38. Oguz Kaya, CoC, CSE — Qualifying Exam, Spring 2012 — Advisor: Edmond Chow
(CoC/CSE)
37. Kenneth Czechowski, CoC, CSE — Qualifying Exam, Spring 2012 — Advisor:
Richard Vuduc (CoC/CSE)
36. Raghul Gunasekaran, CoC, CSE — Qualifying Exam, Spring 2012 — Advisor: Richard[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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Vuduc (CoC/CSE)
35. Liangda Li, CoC, CSE — Qualifying Exam, Spring 2012 — Advisor: Haesun Park
(CoC/CSE)
34. Nitin Arora, College of Engineering (CoE), Aerospace Engineering (AE) — Thesis
Defense, Fall 2011 — Advisor: Ryan Russell (CoE/AE)
33. Bo Xiao, CoC, CSE — Qualifying Exam, Fall 2011 — Advisor: George Biros (CoC/CSE+ CoC/Biomedical Engineering (BME))
32. Sangho Lee, CoC, Computer Science (CS) — Qualifying Exam, Fall 2011 — Advisor: Santosh Pande (CoC/CS)
31. Kaushik Ravichandran, CoC, CS — Qualifying Exam, Fall 2011 — Advisor: Santosh Pande (CoC/CS)
30. Jingfang Liu, College of Science, Mathematics — Qualifying Exam, Fall 2011 —Advisor: Haomin Zhou (CoS/Math)
29. Cong Hou, CoC, CS — Qualifying Exam, Summer 2011 — Advisor: Richard Vuduc(CoC/CSE)
28. Asif Salahuddin, CoE, Mechanical Engineering (ME) — Thesis Proposal, Summer
2009 — Thesis Defense, Summer 2011 — Advisor: Cyrus Aidun (CoC/ME)
27. Jaswanth Sreeram, CoC, CS — Thesis Proposal, Spring 2011 — Advisor: Santosh
Pande (CoC/CS)
26. Abtin Rahimian, CoC, CSE — Thesis Proposal, Spring 2011 — Advisor: George
Biros (CoC/CSE + CoE/BME)
25. Pushkar Pande, CoC, CSE — Qualifying Exam, Spring 2011 — Advisor: David
Bader (CoC/CSE)
24. Xing Liu, CoC, CSE — Qualifying Exam, Spring 2011 — Advisor: David Bader(CoC/CSE)
23. George Vulov, CoC, CSE — Qualifying Exam, Spring 2011 — Advisor: Richard
Fujimoto (CoC/CSE), Richard Vuduc (co-advisor, CoC/CSE)
22. Sangmin Park, CoC, CS — Qualifying Exam, Fall 2010 — Advisor: Mary Jean Harrold (CoC/CS), Richard Vuduc (co-advisor, CoC/CSE)
21. Jiang Bian, CoC, CSE — Thesis Proposal, Spring 2010 — Advisor: Hongyuan Zha(CoC/CSE)
20. Dongryeol Lee, CoC, CSE — Thesis Proposal, Spring 2010 — Advisor: Alexander
Gray (CoC/CSE)
19. Sooraj Bhat, CoC, CS — Thesis Proposal, Spring 2010 — Advisor: Alexander Gray
(CoC/CSE)
18. William March, CoC, CSE — Qualifying Exam, Spring 2010 — Advisor: Alexander
Gray (CoC/CSE)
17. Murat Efe Guney, CoE, Civil Engineering (CE) — Thesis Proposal, Summer 2009
— Thesis Defense, Spring 2010 — Advisor: Kenneth Wills (CoE/CE)
16. Aparna Chandramowlishwaran, CoC, CSE — Qualifying Exam, Fall 2009 — Advisor: Richard Vuduc (CoC/CSE)
15. Mingxuan Sun, CoC, CS — Qualifying Exam, Fall 2009 — Advisor: Guy Lebanon
14. Parikshit Ram, CoC, CS — Qualifying Exam, Fall 2009 — Advisor: Alexander Gray
13. Virat Agarwal, CoC, CSE — Thesis Proposal, Summer 2009 — Advisor: David
Bader
12. Rahul Sampath, CoC, CSE — Spring 2009 — Ph.D. Thesis: A parallel geometric[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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multigrid method for finite elements on octrees applied to elastic image registration — Advisor: George Biros (CoC/CSE + CoE/BME)
11. Steven P. Crain, CoC, CSE — Qualifying Exam, Spring 2009 — Advisor: Hongyuan
Zha (CoC/CSE)
10. Haw-Jing “Michael” Lo, CoE, ECE — Spring 2009 — Ph.D. Thesis: Design of areusable distributed arithmetic filter and its application to the affine projection
algorithm — Advisor: David Anderson (CoE/ECE)
9. Jaegul Choo, CoC, CSE — Qualifying Exam, Spring 2009 — Advisor: Haesun Park
(CoC/CSE)
8. Rahul Sampath, CoC, CSE — Thesis Proposal, Spring 2009 — Advisor: George
Biros (CoC/CSE + CoE/BME)
7. Manisha Gajbe, CoC, CS — Qualifying Exam, Spring 2009 — Advisor: David Bader(CoC/CSE)
6. Hasan Abbasi, CoC, CS — Thesis Proposal, Spring 2009 — Advisor: Karsten Schwan(CoC/CS)
5. Dongreol Lee, CoC, CS — Qualifying Exam, Fall 2008 — Advisor: Alexander Gray
(CoC/CSE)
4. Ryan Riegel, CoC, CS — Qualifying Exam, Spring 2008 — Advisor: Alexander
Gray (CoC/CSE)
3. Alfred Park, CoC, CS — Ph.D. Thesis: Master/worker parallel discrete event simulation Advisor: Richard Fujimoto (CoC/CSE)
2. Kamesh Madduri, CoC, CS — Ph.D. Thesis: A high-performance framework foranalyzing massive complex networks Advisor: David Bader (CoC/CSE)
1. Michael P. Holmes, CoC, CS — Ph.D. Thesis: Multi-tree Monte Carlo methods
for fast, scalable machine learning Advisors: Charles Isbell (primary; CoC/IC),
Alexander Gray (CoC/CSE)
M.S. thesis examining committees (internal to Georgia Tech)
6. Wafa Louhichi – Advisor: Tom Kurfess (CoE/ME), Richard Vuduc — CoC/CSE,MS Thesis Defense, Spring 2019
5. Thuy Vy Nguyen – Advisor: Richard Fujimoto — CoC/CSE, MS Thesis Defense,
Summer 2017
4. Daniel R. Browne, CoE, AE — Summer 2011 — Thesis: Enabling collaborative
behaviors among CubeSats — Advisor: Ryan Russell (CoE/AE)
3. Sundaresan Venkatasubramanian, CoC, CS — Spring 2009 — Thesis: Tuned andwildly asynchronous algorithms for hybrid CPU/GPU platforms — Advisor: Richard
Vuduc
2. Rick Quax, CoC, CS — Fall 2008 — Thesis: Modeling and simulating the propagation of infectious diseases using complex networks — Advisor: David Bader
1. Amrita Mathuriya, CoC, CS — Fall 2008 — Thesis: GTfold: A scalable multicore
code for RNA secondary prediction — Advisor: David Bader
Ph.D. thesis or habilitation examining committees (external to Georgia Tech)
12. Philipp Samfraß – Advisor: Michael Bader — The Technical University of Munich,
Thesis Defense, Spring 2022

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11. Jianyu Huang – Advisor: Robert van de Geijn — The University of Texas at Austin,
Thesis Defense, Summer 2018
10. Chenhan Yu – Advisor: George Biros — The University of Texas at Austin, Thesis
Defense, Summer 2018
9. Amir Gholami – Advisor: George Biros — The University of Texas at Austin,
Thesis Defense, Summer 2017
8. Oguz Kaya – Advisors: Yves Robert and Bora Ucar — ENS-Lyon, Thesis Defense,
Fall 2017
7. Richard Veras – Advisor: Franz Franchetti — Carnegie Mellon University / ECE,
Thesis Defense, Summer 2017
6. Amir Gholami – Advisor: George Biros — University of Texas at Austin / ICES,
Thesis Defense, Summer 2017
5. Victoria Caparros Cabezas – Advisor: Markus Püschel — ETH Zürich, Thesis Defense, Fall 2016
4. Bérenger Bramas – Advisor: Olivier Coulard — Inria Bourdeaux - Sud Ouest, Thesis Defense, Spring 2016
3. Richard Veras – Advisor: Franz Franchetti — Carnegie Mellon University / ECE,
Thesis Proposal, Spring 2016
2. Hana Straková – Advisor: Wilfried Gansterer — Universität Wien, Thesis Defense, Spring 2016
1. Dimitar Lukarski — Mathematics, Karlsrühe Institute of Technology (KIT) — Spring2012 — Thesis: Parallel sparse linear algebra for multi-core and many-core platforms — Advisor: Jan-Philipp Weiß (KIT)
V-B.5 Mentorship of postdoctoral fellows or visiting scholars(No data)

V-C Educational Innovations and Other Contributions
2. Development of CSE 6040-OMS, the Online MS in Analytics version of Computing for Data Analysis
1. Development of CSE 6220-OMS, the Online MS in CS version of Intro to HPC

VI Service
VI-A Professional Contributions
Professional recognition
1. Invited participant, US National Academy of Engineering Frontiers of Engineering (US NAE-FOE) Symposium, September 2015
Professional societies (memberships, leadership, elected positions)
10. 2023, Officer Nominating Committee, Society for Industrial and Applied Mathematics, Activity Group on Supercomputing (SIAM/SC)[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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9. 2022–2025, Member, Gene Golub SIAM Summer School (G2S3) Committee
8. 2018–2020, Chair (elected), Society for Industrial and Applied Mathematics, Activity Group on Supercomputing (SIAM/SC)
7. 2016–2018, Vice Chair (elected), Society for Industrial and Applied Mathematics,
Activity Group on Supercomputing (SIAM/SC)
6. 2015–present, Executive Committee, Newletter Editor, Association of Computing Machinery (ACM) Special Interest Group on High-Performance Computing(ACM SIGHPC)
5. 2010–2012, Secretary (elected), Society for Industrial and Applied Mathematics,
Activity Group on Supercomputing (SIAM/SC)
4. Member, ACM
3. Member, SIAM
2. Member, Institute of Electrical and Electronics Engineers (IEEE)
1. Member, USENIX Association
Editorial boards for scientific journals
4. Associate Editor, ACM Transactions on Parallel Computing (TOPC), 2018–present
3. Associate Editor, IEEE Transactions on Parallel and Distributed Systems (TPDS),
2015–2017
2. Associate Editor, International Journal of High-Performance Computing Applications (IJHPCA), 2012–2018
1. Guest Co-editor, International Journal of High-Performace Computing Applications (IJHPCA), special issue on Autotuning,doi:10.1177/1094342013495303, 2013
Conference committee activities
67. Test of Time Award Committee, ACM/IEEE Conf. Supercomputing (SC), 2024
66. Program Co-Chair, IEEE Int’l. Parallel and Distributed Processing Symp. (IPDPS),
2024
65. Program Committee, SIAM Conference on Applied and Computational Discrete
Algorithms (ACDA), 2023
64. Program Committee, Experiments track, IEEE Int’l. Parallel and Distributed Processing Symp. (IPDPS), 2023
63. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Post-Moore computing track, 2023
62. Technical Committee, IEEE High-Performance Extreme Computing (HPEC) Conference, 2022
61. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Post-Moore computing track, 2022
60. Program Committee and Engagement Committee, SIAM Conference on Applied
and Computational Discrete Algorithms (ACDA), 2021
59. Technical Committee, IEEE High-Performance Extreme Computing (HPEC) Conference, 2021
58. Area Co-Chair, Algorithms Track, IEEE Int’l. Parallel and Distributed Processing
Symp. (IPDPS), 2021

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57. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Performance track,
2021
56. Technical Committee, IEEE High-Performance Extreme Computing (HPEC) Conference, 2020
55. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Applications track,2019
54. Technical Committee, IEEE High-Performance Extreme Computing (HPEC) Conference, 201953. Publicity Co-chair (Americas), ACM SIGPLAN Principles and Practice of Parallel
Programming (PPoPP), 201952. Technical Committee, IEEE High-Performance Extreme Computing (HPEC) Conference, 2018
51. Research Posters Committee, Int’l. Supercomputing Conference (ISC), 2018
50. Program Committee, Platform for Advanced Scientific Computing (PASC) Conference, 201849. Program Committee, SIAM Workshop on Combinatorial Scientific Computing,2018
48. Panels Vice-Chair, ACM/IEEE Conf. Supercomputing (SC), 2018
47. Program Committee, Post-Moore’s Era Supercomputing (PMES) Workshop, 201746. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Programming systems track, 201745. Program Committee, IEEE Int’l. Parallel and Distributed Processing Symp. (IPDPS),
Applications track, 201744. Ph.D. Forum Committee, Int’l. Supercomputing Conference (ISC), 2017
43. Research Posters Committee, Int’l. Supercomputing Conference (ISC), 201742. Program Co-Chair, IEEE CLUSTER, 201741. Technical Papers Vice-Chair,3 ACM/IEEE Conf. Supercomputing (SC), 2016
40. Co-chair, SIAM Conf. Parallel Processing (SIAM PP), 201639. Program Committee, International Conference on Parallel Processing (ICPP), 2016
38. Tutorials Co-chair,4 ACM/IEEE Conf. Supercomputing (SC), 2015
37. General co-chair, IEEE Int’l. Symposium on Workload Characterization (IISWC),
2015
36. Program Committee, ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC), 201535. Program Committee Track Chair, IEEE Cluster, “Applications, Algorithms, andPerformance” track, 201534. Program Committee, IEEE/ACM Symp. on Cluster, Cloud, and Grid Computing(CCGrid), Programming Models and Runtime Systems area, 201533. Program Committee, NSF eXtreme Science and Engineering Discovery Environment Conference (XSEDE), Technology track, 201432. Program Committee, IEEE/ACM Symp. on Cluster, Cloud, and Grid Computing(CCGrid), Doctoral symposium, 2014
3 Together with Lois McInnes Curfman (Argonne National Laboratory), we implemented double-blind

review for SC’16.
4 Together with CJ Newburn (then at Intel), we implemented online distribution of tutorial notes for
SC’15.

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31. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Programming systems track, 201430. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Workshops track,2014
29. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Doctoral Showcase, 2014
28. Program Committee, Int’l. Mtg. High-Performance Computing for Computational Sci. (VECPAR), 2014
27. Workshop and Tutorials Committee Co-Chair, ACM Int’l. Conf. Supercomputing(ICS), 2013
26. Program Committee Track Co-Chair, ACM/IEEE Conf. Supercomputing (SC),2013
25. Program Committee Co-Chair, ACM SIGPLAN Symp. Principles and Practice ofParallel Programming (PPoPP), 2013
24. Program Committee, Int’l. Green Computing Conference (IGCC), 2013
23. Program Committee, ACM/IEEE Conf. Supercomputing (SC), Performance track,2012
22. Program Committee, Int’l. Wkshp. Modern Accelerator Technologies for GIScience (MAT4GIScience), 2012
21. Program Committee, Int’l. Conf. Parallel Processing (ICPP), Performance track,2012
20. Program Committee, IEEE Int’l. Parallel and Distributed Processing Symp. (IPDPS),
Algorithms track, 2012
19. PhD Forum Committee, IPDPS, 2012
18. Program Committee, SIAM Conf. Parallel Processing (SIAM PP), 2012
17. Program Committee, Int’l. Conf. High-Performance Computing (HiPC), Software track, 201116. Program Committee, Workshop on GPU Computing, at Int’l. Conf. Parallel Processing and Applied Mathematics (PPAM), 201115. Program Committee, ACM Student Research Competition and Poster Session at
the ACM Int’l. Conf. Supercomputing (ICS), 2011
14. Program Committee, SC, Performance track, 201113. Program Committee, IPDPS, Software track, 201112. PhD Forum Committee, IPDPS, 2011
11. Program Committee, IEEE/ACM Int’l. Symp. Code Generation and Optimization(CGO), 2011
10. Program Committee, HiPC, 2010
9. Program Committee, SC, Applications track, 2010
8. Program Committee, Conf. Partitioned Global Address Space (PGAS) Programming Models, 2010
7. Program Committee, Int’l. Mtg. High-Performance Computing for Computational Sci. (VECPAR), 2010
6. Program Committee, ACM Int’l. Conf. Computing Frontiers (CF), 2010
5. Program Committee, PPoPP, 2010
4. Program Committee, SC, Applications track, 2009
3. Program Committee, Int’l. Conf. High-Performance Computing (HiPC), 2008[git] • Branch: master @ bbc28ca • Release: (2023‐11‐28)

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2. Program Committee, IEEE Int’l. Conf. Computational Sci. Eng. (CSE), 2008
1. Poster Committee, SC, 2005
Workshop committee service
30. Program Committee, Chapel Implementers and Users Workshop (CHIUW), 2022
29. Program Co-chair, Chapel Implementers and Users Workshop (CHIUW), 2021
28. Program Committee, Chapel Implementers and Users Workshop (CHIUW), 2020
27. Program Committee, Chapel Implementers and Users Workshop (CHIUW), 201926. Program Co-Chair, The 13th Workshop on High-Performance, Power-Aware
Computing (HPPAC), 2017
25. Co-organizer, The 1st Workshop on Evolvable Methods for Benchmarking Realism and Community Engagement (EMBRACE), 201724. Program Committee, Int’l. Wkshp. Automatic Performance Tuning (iWAPT),
2013–201923. Program Committee, Post-Moore’s Era Supercomputing (PMES) Workshop, 2016
22. Co-organizer, Workshop on Power and Energy Aspects of Computing (PEAC),
2013, 201521. Program Chair, Int’l. Wkshp. Automatic Performance Tuning (iWAPT), 2012
20. Program Chair, Concurrent Collections (CnC) Workshop, 201119. Program Committee, High-Performance Scientific Software at Euro-Par, 201118. Program Committee, USENIX Wkshp. Hot Topics in Parallelism (HotPar), 2011
17. Vice Program Chair, iWAPT, 201116. Program Committee, International Workshop on Multicore Software Engineering at ACM/IEEE Int’l. Conf. Software Eng., 201115. Program Committee, Workshop on Application/Architecture Co-design for Extremescale Computing at IEEE Cluster, 2010
14. Organizing Committee, Concurrent Collections Workshop at LCPC, 2010
13. Program Committee, iWAPT, 201012. Program Committee, IEEE Int’l. Symp. Workload Characterization (IISWC),
2010
11. Local Organizing Chair, IISWC, 2010
10. Steering Committee, Wkshp. Statistical Machine Learning applied to ARchitecture and compilaTion (SMART), 2010
9. Program Committee, HotPar, 2010
8. Program Committee, iWAPT, 2009
7. Program Committee, SMART, 2008
6. Program Committee, iWAPT, 2008
5. Program Committee, IEEE Int’l. Wkshp. Parallel and Distributed Scientific andEng. Computing (PDSEC), 2008
4. Program Committee, iWAPT, 2007
3. Program Committee, Wkshp. Performance Optimization of High-level Languages
and Libraries (POHLL), 2007
2. Program Committee, SMART, 2007
1. Program Committee, POHLL, 2006

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Workshop and external course organization
11. Co-organizer, Minisymposium on Domain-Specific Programming Systems (“dopsies”), SIAM Conference on Computational Science and Engineering (CSE), FebMar. 2023
10. Co-organizer, Minisymposium on Energy-aware High-Performance Computing,
SIAM Parallel Processing (PP), Feb. 2012
9. Co-organizer, Wkshp. Libraries and Autotuning for Extreme Scale Systems, for
the US Dept. of Energy Center for Scalable Application Development Software(DOE CScADS), Aug. 2011
8. Co-organizer, Tutorial on Performance analysis and tuning for GPGPUs, at theIEEE Int’l. Symp. High Performance Computer Architecture (HPCA), Feb. 2011
7. Co-organizer, Tutorial on Performance analysis and tuning for GPGPUs, at the
ACM/IEEE Int’l. Symp. Microarchitecture (MICRO), Dec. 2010
6. Co-organizer, Wkshp. Libraries and Autotuning for Petascale Applications, for
US DOE CScADS, Aug. 2010
5. Co-organizer, Tutorial on Performance Analysis and Optimization for GPUs, at
NSF TeraGrid Conf., Aug. 2010
4. Co-organizer, Wkshp. Libraries and Autotuning for Petascale Applications, for
US DOE CScADS, Jul. 2009
3. Co-organizer, Wkshp. Automatic Tuning for Petascale Systems, for US DOE
CScADS, Aug. 2008
2. Co-organizer, Minisymposium on Adaptive Tools and Frameworks for High-Performance
Numerical Computations, SIAM PP, Mar. 2006
1. Co-organizer, Tutorial on “ROSE C/C++ Source-to-source Translator,” Int’l. Conf.
Parallel Architectures and Compilation Techniques (PACT), Sep. 2005
Professional panel service (not otherwise listed in this CV)
12. Moderator, Early Career Panel, SIAM Conference on Computational Science andEngineering (CSE), Feb-Mar 2023
11. Panelist, DARPA ISAT: Data-oblivious Interdisciplinary Representations (DOPLR), 2020
10. Panel on Supporting Students While We Teach, a Georgia Tech Center for Teaching and Learning (CTL) Workshop, Jan 2019
9. Panel on Career Networking for the SC16 Early Career Program, 2016
8. Panel on Career Networking for the SC15 Early Career Program, 2015
7. Panel on High-Performance Power-Aware Computing (HPPAC) at IPDPS, Phoenix,
AZ, USA, May 2014
6. Birds-of-a-Feather Session on Community MOOCs for Computing Technologies
and Applications, at SC, Denver, CO, USA, November 2013
5. Panel on Algorithms at IPDPS, Shanghai, China, May 2012
4. Panel on Communication and Synchronization Primitives, US Dept. of Energy(DOE), Advanced Scientific Computing Research (ASCR), Workshop on Exascale
Programming Challenges, July 2011.
3. Panel on AMD Fusion-like Processors in HPC, AMD Fusion Developers’ Summit,
June 2011.

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2. Panel on MPI+Manycore Issues and Plans, Sandia National Laboratories Computer Science Research Institute (CSRI) Workshop on Next-generation Scalable
Application: When MPI-only is Not Enough, May 2008.
1. Panel on New HPC-Challenge Benchmark Candidates, Georgia Tech, November
2008.
Student awards and fellowships reviewing
1. Reviewer for the ACM SIGHPC / Intel Fellowships in Computational and Data
Science, 2016–2022
Research project reviewing
20. Department of Energy, Exascale Computing Project, 2021–2022
19. National Science Foundation, 2019–2022
18. Natural Sciences and Engineering Research Council (NSERC), Canada, 201817. Department of Energy, Exascale Computing Project, 2018
16. The Parternship for Advanced Computing in Europe (PRACE), 2017
15. The Netherlands Organisation for Scientific Research (NWO), Technology Foundation STW, 2016
14. Inria (French National Institute for Computer Science and Applied Mathematics),
2016
13. Department of Energy, 2016
12. National Science Foundation, 2016
11. Department of Energy, 2015, 2016
10. National Science Foundation, 2015
9. Department of Energy, 2014
8. National Science Foundation, 2014
7. National Science Foundation, 2013
6. Department of Energy, 2012
5. National Science Foundation, 2012
4. National Science Foundation, 2011
3. Department of Energy, 2010
2. Department of Energy, 2009
1. National Science Foundation, 2008
Peer reviewing of technical books
3. MIT Press, 2015.
2. CRC Press, 2008, 2010–2014, 2016–2017.
1. John Wiley & Sons, 2013.
Peer reviewing for scientific journals
13. ACM Transactions on Parallel Computing (TOPC), 2013, 2014.
12. International Journal on High Performance Computing Applications (IJHPCA),
2002, 2004, 2009, 2011, 2013, 2014.
11. SIAM J. Scientific Computing (SISC), 2008, 2009, 2011, 2012, 2014, 2015, 2016.
10. IEEE Trans. Par. Distrib. Sys. (TPDS), 2009, 2010, 2011, 2012.

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9. ACM Trans. Architecture and Code Optimization (ACM TACO), 2009, 2012.
8. J. Parallel Computing (ParCo), 2008, 2009.
7. ACM Trans. Mathematical Software (ACM-TOMS), 2008.
6. J. Parallel and Distributed Computing (JPDC), 2008, 2010.
5. Software: Practice and Experience, 2008.
4. Concurrency and Computation: Practice and Experience (CC:PE), 2007.
3. Parallel Processing Letters, 2004.
2. Proc. IEEE, 2004.
1. J. Functional Programming (JFP), 2001.
Ad hoc peer reviewing for technical conferences and workshops
18. Innovative Parallel Computing Conference (InPar), 2012
17. ACM SIGPLAN Symp. Principles and Practice of Parallel Programming (PPoPP),
2011, 2012
16. IEEE/ACM Int’l. Symp. Microarchitecture (MICRO), 2011
15. IEEE Int’l. Par. Distrib. Processing Symp. (IPDPS), 2010.
14. Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU),
2009, 2010.
13. Winter Simulation Conference (WSC), 2009.
12. Int’l. Symp. Computer Architecture (ISCA), 2008.
11. Computing Frontiers (CF), 2008.
10. Int’l Conf. High-Performance Computing and Communications (HPCC), 2007.
9. ACM/IEEE Conf. Supercomputing (SC), 2007.
8. ACM Int’l. Conf. Supercomputing (ICS), 2007.
7. Network and Parallel Computing (NPC), 2006.
6. Euro-Par, 2006.
5. Combinatorial Scientific Computing, 2005.
4. ACM Symposium on Parallel Algorithms and Architectures (SPAA), 2003, 2004.
3. ACM Conf. Programming Language Design and Implementation (PLDI), 2003.
2. Int’l. Conf. Computational Science (ICCS), 2001.
1. ACM Conf. Principles of Programming Languages (POPL), 2000.

VI-B Public and Community Service
7. Judge, Georgia Science and Engineering Fair, 2013
6. Judge, Georgia Science and Engineering Fair, 2011
5. Judge, Georgia Science and Engineering Fair, 2010
4. Judge, Georgia Science and Engineering Fair, 2009
3. Speaker, Advice on Preparing a Research Statement, CoC Job Hunting Seminar,
2009
2. Reader, Georgia Junior Humanities and Science Fair, 2008
1. Speaker, Advice on Preparing a Research Statement, CoC Job Hunting Seminar,
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VI-C Institute Contributions
43. CSE, Awards Committee, CoC, 2020–present AY.
42. IC, Chair of the School Chair Search Committee, 2021–2022.
41. Biomedical Engineering (BME), Periodic Peer Review Subcommittee, 2021 AY.
40. CSE, College of Computing Retention, Promotion, and Tenure Committee, 2019–present.
39. CSE, College of Computing Awards Committee, 2020–2023 AY.
38. CSE, Faculty Recruiting Committee, CoC, 2008–2021 AY.
37. CSE, School Chair Search Committee, 2019–2021 AY.
36. CSE, Strategic Visioning and Planning Committee, 2020 AY.
35. CoC Undergraduate Curriculum Committee, 2015–2020 AY.
34. CSE, School Chair Advisory Committee, 2018–2019 AY.
33. CSE, Chair Review Committee, 2018 AY.
32. CSE, Qualifying Exam Committee, CoC, 2008–present.
31. CoC CS Graduate Curriculum Committee, 2015–2018 AY.
30. College of Computing Dean Review Committee, 2015.
29. Data Science and Engineering Faculty Council, 2014–2015 AY.
28. CoC Dean’s Advisory Committee, 2014–2015 AY.
27. CoC Undergraduate Curriculum Committee, 2014–2015 AY.
26. CETL TA Awards Selection Committee, 2014.
25. Associate Chair for Academic Affairs, CSE, 2013–2015 AY.
24. Director of Graduate Programs, CSE, 2013–2016 AY.
23. Brooks Byers Professorships Committee, 2013–2014 AY.
22. CSE Awards Committee Chair, CoC, 2012–2013 AY.
21. CoC Awards Committee Member, 2012–2013 AY.
20. CoC MOOMS Committee Member, 2012–2013 AY.
19. CSE Chair Search Committee Member, 2012–2013 AY.
18. CSE Seminar Co-organizer, CoC, 2012–2013 AY.
17. CSE School Advisory Committee (SAC), CoC, 2011–2012 AY. Committee, (SAC),
CoC, 2011–2012 Academic Year (AY).
16. CSE, Space Planning Committee, CoC, 2011–2012 AY.
15. CSE, Graduate Admissions and Recruiting Committee, CoC, 2010–2011 Academic
Year (AY).
14. Undergraduate Curriculum Advisory Committee on Parallel Computing, Collegeof Computing (CoC), 2009–2010 AY.
13. CSE Chair Advisory Committee, CoC, 2009–2010 AY.
12. Computer Science (CS) PhD Recruiting Weekend Committee, CoC, 2008–2009
AY.
11. CSE Review of the Chair Committee, CoC, 2008–2009 AY.
10. Faculty Computer Committee (FCC), CoC, 2008–2009 AY.
9. CSE PhD Admissions Committee, CoC, 2008–2009 AY.
8. CS PhD Admissions Committee, CoC, 2008–2009 AY.
7. CSE Chair Advisory Committee, CoC, 2008–2009 AY.
6. CSE FRC, CoC, 2008–2009 AY.
5. CSE PhD Admissions Committee, 2007–2008 AY.

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4. CS PhD Admissions Committee, 2007–2008 AY.
3. Teaching Assistanceship Requirement Grandfathering Committee, CoC. 2007–2008 AY.
2. FCC, CoC, 2007–2008 AY.
1. Awards Committee, CoC, 2007–2008 AY.

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