Richard Vuduc Explained
| Richard Vuduc |
| Nationality: | American |
|
| Awards: | NSF career award, Gordon Bell Prize |
| Alma Mater: | Cornell University, University of California-Berkeley |
| Discipline: | High-performance computing, Scientific computing, Parallel algorithms, and Performance analysis, modeling, and engineering. |
| Workplaces: | Georgia Institute of Technology School of Computational Science & Engineering |
Richard Vuduc is a tenured professor of computer science at the Georgia Institute of Technology. His research lab, The HPC Garage, studies high-performance computing, scientific computing, parallel algorithms, modeling, and engineering.[1] He is a member of the Association for Computing Machinery (ACM). As of 2022, Vuduc serves as Vice President of the SIAM Activity Group on Supercomputing. He has co-authored over 200 articles in peer-reviewed journals and conferences.[2]
Education
Dr. Vuduc received his Ph.D. in computer science from the University of California, Berkeley, in 2004. He received his B.S in computer science at Cornell University in 1997. He is also an alumnus of the Thomas Jefferson High School for Science and Technology in Alexandria, Virginia.
Academic career
Vuduc was a Postdoctoral Scholar in the Center for Advanced Scientific Computing at the Lawrence Livermore National Laboratory.[3] He has served as an associate editor of both the International Journal of High-Performance Computing Applications and IEEE Transactions on Parallel and Distributed Systems. He co-chaired the Technical Papers Program of the “Supercomputing” (SC) Conference in 2016 and was later elected to be Vice President of the SIAM Activity Group on Supercomputing from 2016 to 2018. He also served as department’s Associate Chair and Director of its graduate (MS & Ph.D.) programs from 2013-2016.
Major honors and awards
- Member of the DARPA Computer Science Study Group
- Recipient NSF CAREER award
- Collaborative Gordon Bell Prize 2010
- Lockheed-Martin Aeronautics Company Dean’s Award for Teaching Excellence 2013
- Best Paper Awards, including the SIAM Conference on Data Mining (SDM, 2012) and IEEE Parallel and Distributed Processing Symposium (IPDPS, 2015)
Major publications
- Book: 10.1145/1362622.1362674. 9781595937643. Optimization of sparse matrix-vector multiplication on emerging multicore platforms. Proceedings of the 2007 ACM/IEEE conference on Supercomputing - SC '07. 1. 2007. Williams. Samuel. Oliker. Leonid. Vuduc. Richard. Shalf. John. Yelick. Katherine. Demmel. James. 1845814. https://escholarship.org/uc/item/50s2p3md.
- Vuduc. Richard. Demmel. James W.. Yelick. Katherine A.. 2005. OSKI: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series. en. 16. 1. 521. 10.1088/1742-6596/16/1/071. 1742-6596. 2005JPhCS..16..521V. free.
- Vuduc. Richard (Rich). Model-driven autotuning of sparse matrix-vector multiply on GPUs. ACM SIGPLAN Notices. en.
- Im. Eun-Jin. Yelick. Katherine. Vuduc. Richard. February 2004. Sparsity: Optimization Framework for Sparse Matrix Kernels. Int. J. High Perform. Comput. Appl.. 18. 1. 135–158. 10.1177/1094342004041296. 1094-3420. 10.1.1.137.5844. 2447843.
- Vuduc. Richard Wilson. Automatic Performance Tuning of Sparse Matrix Kernels. 2003. University of California, Berkeley.
- Demmel. J.. Dongarra. J.. Eijkhout. V.. Fuentes. E.. Petitet. A.. Vuduc. R.. Whaley. R. C.. Yelick. K.. February 2005. Self-Adapting Linear Algebra Algorithms and Software. Proceedings of the IEEE. 93. 2. 293–312. 10.1109/JPROC.2004.840848. 0018-9219. 10.1.1.108.7568. 3065125.
- Book: Vuduc. Richard. Demmel. James W.. Yelick. Katherine A.. Kamil. Shoaib. Nishtala. Rajesh. Lee. Benjamin. 2002. Performance Optimizations and Bounds for Sparse Matrix-vector Multiply. http://dl.acm.org/citation.cfm?id=762761.762822. Proceedings of the 2002 ACM/IEEE Conference on Supercomputing. SC '02. Los Alamitos, CA, USA. IEEE Computer Society Press. 1–35.
- Lashuk. Ilya. Chandramowlishwaran. Aparna. Langston. Harper. Nguyen. Tuan-Anh. Sampath. Rahul. Shringarpure. Aashay. Vuduc. Richard. Ying. Lexing. Zorin. Denis. May 2012. A Massively Parallel Adaptive Fast Multipole Method on Heterogeneous Architectures. Communications of the ACM. 55. 5. 101–109. 10.1145/2160718.2160740. 2272736. 0001-0782.
- Book: 10.1109/SC.2010.42. 9781424475599. Petascale Direct Numerical Simulation of Blood Flow on 200K Cores and Heterogeneous Architectures. 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis. 1–11. 2010. Rahimian. Abtin. Lashuk. Ilya. Veerapaneni. Shravan. Chandramowlishwaran. Aparna. Malhotra. Dhairya. Moon. Logan. Sampath. Rahul. Shringarpure. Aashay. Vetter. Jeffrey. Vuduc. Richard. Zorin. Denis. Biros. George. 5490197.
- Book: 10.1145/2145816.2145819. 9781450311601. 10.1.1.226.3542. A performance analysis framework for identifying potential benefits in GPGPU applications. Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming - PPoPP '12. 11. 2012. Sim. Jaewoong. Dasgupta. Aniruddha. Kim. Hyesoon. Hyesoon Kim. Vuduc. Richard. 6817445.
- Book: Vuduc. Richard. Chandramowlishwaran. Aparna. Choi. Jee. Guney. Murat. Shringarpure. Aashay. 2010. On the Limits of GPU Acceleration. http://dl.acm.org/citation.cfm?id=1863086.1863099. Proceedings of the 2nd USENIX Conference on Hot Topics in Parallelism. HotPar'10. Berkeley, CA, USA. USENIX Association. 13.
- Book: Vuduc. Richard W.. Moon. Hyun-Jin. 2005 . Berlin, Heidelberg. Springer-Verlag. 807–816. 10.1007/11557654_91. 978-3540290315. https://digital.library.unt.edu/ark:/67531/metadc875745/. Fast Sparse Matrix-Vector Multiplication by Exploiting Variable Block Structure. High Performance Computing and Communications. 3726. Lecture Notes in Computer Science.
- Book: 10.1145/1806799.1806838. 9781605587196. Falcon. Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - ICSE '10. 1. 245. 2010. Park. Sangmin. Vuduc. Richard W.. Harrold. Mary Jean. 8744239.
- Vuduc. Richard. Demmel. James W.. Bilmes. Jeff A.. February 2004. Statistical Models for Empirical Search-Based Performance Tuning. The International Journal of High Performance Computing Applications. 18. 1. 65–94. 10.1177/1094342004041293. 1094-3420. 10.1.1.64.5699. 2563412.
- Book: Qing. Yi. Keith. Seymour. Haihang. You. Richard. Vuduc. Dan. Quinlan. POET: Parameterized Optimizations for Empirical Tuning. https://www.academia.edu/2670691. 2007 IEEE International Parallel and Distributed Processing Symposium. en.
- Book: Chandramowlishwaran. A.. Knobe. K.. Vuduc. R.. April 2010 . 1–12. 10.1109/IPDPS.2010.5470404. 978-1-4244-6442-5. 10.1.1.169.5643. Performance evaluation of concurrent collections on high-performance multicore computing systems. 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS). 1133093.
Notes and References
- Web site: Richard Vuduc College of Computing. www.cc.gatech.edu. en. 2017-12-17.
- Web site: Richard Vuduc - Google Scholar Citations. scholar.google.co.in. 2017-12-17.
- News: Why the father of the self-driving car left Google. Poletti. Therese. MarketWatch. 2017-12-18. en-US.
