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ORCIDRichard W. Vuduc
Rich Vuduc
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- affiliation: Georgia Institute of Technology, Atlanta GA, USA
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2020 – today
- 2024
[c100]Jana Hozzová
, Jacob O. Tørring, Ben van Werkhoven, David Strelák, Richard W. Vuduc:
FAIR Sharing of Data in Autotuning Research (Vision Paper). ICPE (Companion) 2024: 21-27- 2023
- [c99]Srinivas Eswar, Benjamin Cobb, Koby Hayashi, Ramakrishnan Kannan, Grey Ballard, Richard W. Vuduc, Haesun Park:
Distributed-Memory Parallel JointNMF. ICS 2023: 301-312 - [c98]Patrick Lavin, Jeffrey Young, Richard W. Vuduc:
Multifidelity Memory System Simulation in SST. MEMSYS 2023: 8:1-8:15 - [c97]Mikhail Isaev, Nic McDonald, Larry Dennison, Richard W. Vuduc:
Calculon: a methodology and tool for high-level co-design of systems and large language models. SC 2023: 71:1-71:14 - [d2]Srinivas Eswar, Benjamin Cobb, Koby Hayashi, Ramakrishnan Kannan, Grey Ballard, Rich Vuduc, Haesun Park:
AminerMag S Dataset. Zenodo, 2023
- [d1]Srinivas Eswar, Benjamin Cobb, Koby Hayashi, Ramakrishnan Kannan, Grey Ballard, Rich Vuduc, Haesun Park:
AminerMag X Dataset. Zenodo, 2023 - 2022
- [j25]Richard W. Vuduc:
Jack, The Autotuner. Comput. Sci. Eng. 24(4): 24-27 (2022) - [j24]Nicole Prindle, Ali Kazmi, Aman Jain, Albert Chen, Marissa Sorkin, Sudhanshu Agarwal, Richard W. Vuduc, Vijay Thakkar:
Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From Georgia Tech. IEEE Trans. Parallel Distributed Syst. 33(9): 2035-2038 (2022) - [c96]Mikhail Isaev, Nic McDonald, Jeffrey Young, Richard W. Vuduc:
ParaGraph: An application-simulator interface and toolkit for hardware-software co-design. ICPP 2022: 63:1-63:13 - [c95]Sara Karamati, Clayton Hughes, K. Scott Hemmert, Ryan E. Grant, Whit Schonbein, Scott Levy, Thomas M. Conte, Jeffrey Young, Richard W. Vuduc:
"Smarter" NICs for faster molecular dynamics: a case study. IPDPS 2022: 583-594 - [c94]Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc:
Nimble GNN Embedding with Tensor-Train Decomposition. KDD 2022: 2327-2335 - [c93]Ramakrishnan Kannan, Piyush Sao, Hao Lu, Jakub Kurzak, Gundolf Schenk, Yongmei Shi, Seung-Hwan Lim, Sharat Israni, Vijay Thakkar, Guojing Cong, Robert M. Patton, Sergio E. Baranzini, Richard W. Vuduc, Thomas E. Potok:
Exaflops Biomedical Knowledge Graph Analytics. SC 2022: 6:1-6:11 - [i16]Sara Karamati, Clayton Hughes, K. Scott Hemmert, Ryan E. Grant, Whit Schonbein, Scott Levy, Thomas M. Conte, Jeffrey Young, Richard W. Vuduc:
"Smarter" NICs for faster molecular dynamics: a case study. CoRR abs/2204.05959 (2022) - [i15]Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc:
Nimble GNN Embedding with Tensor-Train Decomposition. CoRR abs/2206.10581 (2022) - 2021
- [j23]Yang You, Jingyue Huang, Cho-Jui Hsieh, Richard W. Vuduc, James Demmel:
Communication-avoiding kernel ridge regression on parallel and distributed systems. CCF Trans. High Perform. Comput. 3(3): 252-270 (2021) - [j22]Srinivas Eswar, Ramakrishnan Kannan, Richard W. Vuduc, Haesun Park:
ORCA: Outlier detection and Robust Clustering for Attributed graphs. J. Glob. Optim. 81(4): 967-989 (2021) - [c92]Rahul Duggal, Cao Xiao, Richard W. Vuduc, Duen Horng Chau, Jimeng Sun:
CUP: Cluster Pruning for Compressing Deep Neural Networks. IEEE BigData 2021: 5102-5106 - [c91]Patrick Lavin, Jeffrey Young, Richard W. Vuduc, Jonathan Beard:
Online model swapping for architectural simulation. CF 2021: 102-112 - [c90]Piyush Sao, Hao Lu, Ramakrishnan Kannan, Vijay Thakkar, Richard W. Vuduc, Thomas E. Potok:
Scalable All-pairs Shortest Paths for Huge Graphs on Multi-GPU Clusters. HPDC 2021: 121-131 - [c89]Mitesh Kothari, Richard W. Vuduc:
An interface for multidimensional arrays in Arkouda. HPEC 2021: 1-2 - [c88]Sudhanshu Agarwal, Richard W. Vuduc:
Is it Nemo or Dory? Fast and accurate object detection for IoT and edge devices. IOT 2021: 94-101 - 2020
- [j21]Eric R. Hein, Srinivas Eswar, Abdurrahman Yasar, Jiajia Li, Jeffrey S. Young, Thomas M. Conte, Ümit V. Çatalyürek, Richard W. Vuduc, E. Jason Riedy, Bora Uçar:
Programming Strategies for Irregular Algorithms on the Emu Chick. ACM Trans. Parallel Comput. 7(4): 25:1-25:25 (2020) - [j20]Zhihao Li, Haipeng Jia, Yunquan Zhang, Tun Chen, Liang Yuan, Richard W. Vuduc:
Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs. IEEE Trans. Parallel Distributed Syst. 31(8): 1925-1941 (2020) - [c87]Xin Chen, Thomas M. Tucker, Thomas R. Kurfess, Richard W. Vuduc, Liting Hu:
Max orientation coverage: efficient path planning to avoid collisions in the CNC milling of 3D objects. IROS 2020: 6862-6869 - [c86]Patrick Lavin, Jeffrey Young, Richard W. Vuduc, E. Jason Riedy, Aaron Vose, Daniel Ernst:
Evaluating Gather and Scatter Performance on CPUs and GPUs. MEMSYS 2020: 209-222 - [c85]Tong Zhou, Jun Shirako, Anirudh Jain, Sriseshan Srikanth, Thomas M. Conte, Richard W. Vuduc, Vivek Sarkar:
Intrepydd: performance, productivity, and portability for data science application kernels. Onward! 2020: 65-83 - [c84]Piyush Sao, Ramakrishnan Kannan, Prasun Gera, Richard W. Vuduc:
A supernodal all-pairs shortest path algorithm. PPoPP 2020: 250-261 - [c83]Ramakrishnan Kannan, Piyush Sao, Hao Lu, Drahomira Herrmannova, Vijay Thakkar, Robert M. Patton, Richard W. Vuduc, Thomas E. Potok:
Scalable knowledge graph analytics at 136 petaflop/s. SC 2020: 6 - [c82]Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Richard W. Vuduc, Haesun Park:
Distributed-memory parallel symmetric nonnegative matrix factorization. SC 2020: 74 - [i14]Patrick Lavin, Jeffrey Young, Rich Vuduc, Jonathan Beard:
Online Model Swapping in Architectural Simulation. CoRR abs/2012.01571 (2020)
2010 – 2019
- 2019
- [j19]Ioakeim Perros, Evangelos E. Papalexakis, Richard W. Vuduc, Elizabeth Searles, Jimeng Sun:
Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization. J. Biomed. Informatics 93 (2019) - [j18]Yuchen Ma, Jiajia Li, Xiaolong Wu, Chenggang Yan, Jimeng Sun, Richard W. Vuduc:
Optimizing sparse tensor times matrix on GPUs. J. Parallel Distributed Comput. 129: 99-109 (2019) - [j17]Piyush Sao, Xiaoye S. Li, Richard W. Vuduc:
A communication-avoiding 3D algorithm for sparse LU factorization on heterogeneous systems. J. Parallel Distributed Comput. 131: 218-234 (2019) - [j16]Jeffrey S. Young, Eric R. Hein, Srinivas Eswar, Patrick Lavin, Jiajia Li, E. Jason Riedy, Richard W. Vuduc, Tom Conte:
A microbenchmark characterization of the Emu chick. Parallel Comput. 87: 60-69 (2019) - [c81]Xin Chen, Dmytro Konobrytskyi, Thomas M. Tucker, Thomas R. Kurfess, Richard W. Vuduc:
Faster parallel collision detection at high resolution for CNC milling applications. ICPP 2019: 97:1-97:10 - [c80]Piyush Sao, Ramakrishnan Kannan, Xiaoye Sherry Li, Richard W. Vuduc:
A communication-avoiding 3D sparse triangular solver. ICS 2019: 127-137 - [c79]Jiajia Li, Bora Uçar, Ümit V. Çatalyürek, Jimeng Sun, Kevin J. Barker, Richard W. Vuduc:
Efficient and effective sparse tensor reordering. ICS 2019: 227-237 - [c78]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan:
Load-Balanced Sparse MTTKRP on GPUs. IPDPS 2019: 123-133 - [c77]Xin Chen, Thomas M. Tucker, Thomas R. Kurfess, Richard W. Vuduc:
Adaptive Deep Path: Efficient Coverage of a Known Environment under Various Configurations. IROS 2019: 3549-3556 - [c76]Piyush Sao, Christian Engelmann, Srinivas Eswar, Oded Green, Richard W. Vuduc:
Self-stabilizing Connected Components. FTXS@SC 2019: 50-59 - [i13]Eric R. Hein, Srinivas Eswar, Abdurrahman Yasar, Jiajia Li, Jeffrey S. Young, Thomas M. Conte, Ümit V. Çatalyürek, Rich Vuduc, E. Jason Riedy, Bora Uçar:
Programming Strategies for Irregular Algorithms on the Emu Chick. CoRR abs/1901.02775 (2019) - [i12]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan:
Load-Balanced Sparse MTTKRP on GPUs. CoRR abs/1904.03329 (2019) - [i11]Rahul Duggal, Cao Xiao, Richard W. Vuduc, Jimeng Sun:
CUP: Cluster Pruning for Compressing Deep Neural Networks. CoRR abs/1911.08630 (2019) - 2018
- [j15]Prasanna Balaprakash, Jack J. Dongarra, Todd Gamblin, Mary W. Hall, Jeffrey K. Hollingsworth, Boyana Norris, Richard W. Vuduc:
Autotuning in High-Performance Computing Applications. Proc. IEEE 106(11): 2068-2083 (2018) - [c75]Yang You, James Demmel, Cho-Jui Hsieh, Richard W. Vuduc:
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems. ICS 2018: 307-317 - [c74]Eric R. Hein, Tom Conte, Jeffrey Young, Srinivas Eswar, Jiajia Li, Patrick Lavin, Richard W. Vuduc, E. Jason Riedy:
An Initial Characterization of the Emu Chick. IPDPS Workshops 2018: 579-588 - [c73]Piyush Sao, Xiaoye Sherry Li, Richard W. Vuduc:
A Communication-Avoiding 3D LU Factorization Algorithm for Sparse Matrices. IPDPS 2018: 908-919 - [c72]Sara Karamati, Jeffrey S. Young, Richard W. Vuduc:
An Energy-Efficient Single-Source Shortest Path Algorithm. IPDPS 2018: 1080-1089 - [c71]Ioakeim Perros, Evangelos E. Papalexakis, Haesun Park, Richard W. Vuduc, Xiaowei Yan, Christopher deFilippi, Walter F. Stewart, Jimeng Sun:
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping. KDD 2018: 2080-2089 - [c70]Jiajia Li, Jimeng Sun, Richard W. Vuduc:
HiCOO: hierarchical storage of sparse tensors. SC 2018: 19:1-19:15 - [i10]Ioakeim Perros, Evangelos E. Papalexakis, Haesun Park, Richard W. Vuduc, Xiaowei Yan, Christopher deFilippi, Walter F. Stewart, Jimeng Sun:
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping. CoRR abs/1803.05473 (2018) - [i9]Yang You, James Demmel, Cho-Jui Hsieh, Richard W. Vuduc:
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems. CoRR abs/1805.00569 (2018) - [i8]Devangi N. Parikh, Margaret E. Myers, Richard W. Vuduc, Robert A. van de Geijn:
A Simple Methodology for Computing Families of Algorithms. CoRR abs/1808.07832 (2018) - [i7]Jeffrey Young, Eric R. Hein, Srinivas Eswar, Patrick Lavin, Jiajia Li, E. Jason Riedy, Richard W. Vuduc, Tom Conte:
A Microbenchmark Characterization of the Emu Chick. CoRR abs/1809.07696 (2018) - [i6]Patrick Lavin, E. Jason Riedy, Rich Vuduc, Jeffrey S. Young:
Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns. CoRR abs/1811.03743 (2018) - 2017
- [j14]Zhihui Du, Rong Ge, Victor W. Lee, Richard W. Vuduc, David A. Bader, Ligang He:
Modeling the Power Variability of Core Speed Scaling on Homogeneous Multicore Systems. Sci. Program. 2017: 8686971:1-8686971:13 (2017) - [j13]Yang You, James Demmel, Kent Czechowski, Le Song, Rich Vuduc:
Design and Implementation of a Communication-Optimal Classifier for Distributed Kernel Support Vector Machines. IEEE Trans. Parallel Distributed Syst. 28(4): 974-988 (2017) - [c69]Shuaiwen Leon Song, Richard W. Vuduc:
HPPAC Workshop Introduction. IPDPS Workshops 2017: 952 - [c68]Jiajia Li, Jee Choi, Ioakeim Perros, Jimeng Sun, Richard W. Vuduc:
Model-Driven Sparse CP Decomposition for Higher-Order Tensors. IPDPS 2017: 1048-1057 - [c67]Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang, Richard W. Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun:
SPARTan: Scalable PARAFAC2 for Large & Sparse Data. KDD 2017: 375-384 - [c66]Yiyang Zhao, Linnan Wang, Wei Wu, George Bosilca, Richard W. Vuduc, Jinmian Ye, Wenqi Tang, Zenglin Xu:
Efficient Communications in Training Large Scale Neural Networks. ACM Multimedia (Thematic Workshops) 2017: 110-116 - [c65]Ioakeim Perros, Fei Wang, Ping Zhang, Peter B. Walker, Richard W. Vuduc, Jyotishman Pathak, Jimeng Sun:
Polyadic Regression and its Application to Chemogenomics. SDM 2017: 72-80 - [i5]Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang, Richard W. Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun:
SPARTan: Scalable PARAFAC2 for Large & Sparse Data. CoRR abs/1703.04219 (2017) - 2016
- [c64]Piyush Sao, Oded Green, Chirag Jain, Richard W. Vuduc:
A Self-Correcting Connected Components Algorithm. FTXS@HPDC 2016: 9-16 - [c63]JeeWhan Choi, Richard W. Vuduc:
Analyzing the Energy Efficiency of the Fast Multipole Method Using a DVFS-Aware Energy Model. IPDPS Workshops 2016: 79-88 - [c62]Mohammad M. Hossain, Thomas M. Tucker, Thomas R. Kurfess, Richard W. Vuduc:
Hybrid Dynamic Trees for Extreme-Resolution 3D Sparse Data Modeling. IPDPS 2016: 132-141 - [c61]Jiajia Li, Yuchen Ma, Chenggang Yan, Richard W. Vuduc:
Optimizing Sparse Tensor Times Matrix on Multi-core and Many-Core Architectures. IA3@SC 2016: 26-33 - [i4]Marat Dukhan, Richard W. Vuduc, E. Jason Riedy:
Wanted: Floating-Point Add Round-off Error instruction. CoRR abs/1603.00491 (2016) - [i3]Ioakeim Perros, Robert Chen, Richard W. Vuduc, Jimeng Sun:
Sparse Hierarchical Tucker Factorization and its Application to Healthcare. CoRR abs/1610.07722 (2016) - 2015
- [j12]Sangmin Park, Richard W. Vuduc, Mary Jean Harrold:
UNICORN: a unified approach for localizing non-deadlock concurrency bugs. Softw. Test. Verification Reliab. 25(3): 167-190 (2015) - [c60]Ioakeim Perros, Robert Chen, Richard W. Vuduc, Jimeng Sun:
Sparse Hierarchical Tucker Factorization and Its Application to Healthcare. ICDM 2015: 943-948 - [c59]Piyush Sao, Xing Liu, Richard W. Vuduc, Xiaoye S. Li:
A Sparse Direct Solver for Distributed Memory Xeon Phi-Accelerated Systems. IPDPS 2015: 71-81 - [c58]Yang You, James Demmel, Kenneth Czechowski, Le Song, Richard W. Vuduc:
CA-SVM: Communication-Avoiding Support Vector Machines on Distributed Systems. IPDPS 2015: 847-859 - [c57]Mohammad M. Hossain, Thomas M. Tucker, Thomas R. Kurfess, Richard W. Vuduc:
A GPU-parallel construction of volumetric tree. IA3@SC 2015: 10:1-10:4 - [c56]Jiajia Li, Casey Battaglino, Ioakeim Perros, Jimeng Sun, Richard W. Vuduc:
An input-adaptive and in-place approach to dense tensor-times-matrix multiply. SC 2015: 76:1-76:12 - [c55]Oded Green, Marat Dukhan, Richard W. Vuduc:
Branch-Avoiding Graph Algorithms. SPAA 2015: 212-223 - 2014
- [j11]Dongryeol Lee, Piyush Sao, Richard W. Vuduc, Alexander G. Gray:
A distributed kernel summation framework for general-dimension machine learning. Stat. Anal. Data Min. 7(1): 1-13 (2014) - [c54]JeeWhan Choi, Aparna Chandramowlishwaran, Kamesh Madduri, Richard W. Vuduc:
A CPU: GPU Hybrid Implementation and Model-Driven Scheduling of the Fast Multipole Method. GPGPU@ASPLOS 2014: 64 - [c53]Piyush Sao, Richard W. Vuduc, Xiaoye Sherry Li:
A Distributed CPU-GPU Sparse Direct Solver. Euro-Par 2014: 487-498 - [c52]Jee W. Choi, Marat Dukhan, Xing Liu, Richard W. Vuduc:
Algorithmic Time, Energy, and Power on Candidate HPC Compute Building Blocks. IPDPS 2014: 447-457 - [c51]Kenneth Czechowski, Victor W. Lee, Ed Grochowski, Ronny Ronen, Ronak Singhal, Richard W. Vuduc, Pradeep Dubey:
Improving the energy efficiency of Big Cores. ISCA 2014: 493-504 - [i2]Oded Green, Marat Dukhan, Richard W. Vuduc:
Branch-Avoiding Graph Algorithms. CoRR abs/1411.1460 (2014) - 2013
- [j10]JeeWhan Choi, Richard W. Vuduc:
How much (execution) time and energy does my algorithm cost? XRDS 19(3): 49-51 (2013) - [j9]Leonid Oliker, Richard W. Vuduc:
Introduction for Special Issue on Autotuning. Int. J. High Perform. Comput. Appl. 27(4): 377-378 (2013) - [c50]JeeWhan Choi, Daniel Bedard, Robert J. Fowler, Richard W. Vuduc:
A Roofline Model of Energy. IPDPS 2013: 661-672 - [c49]Kenneth Czechowski, Richard W. Vuduc:
A Theoretical Framework for Algorithm-Architecture Co-design. IPDPS 2013: 791-802 - [c48]Sangmin Park, Mary Jean Harrold, Richard W. Vuduc:
Griffin: grouping suspicious memory-access patterns to improve understanding of concurrency bugs. ISSTA 2013: 134-144 - [c47]Marat Dukhan, Richard W. Vuduc:
Methods for High-Throughput Computation of Elementary Functions. PPAM (1) 2013: 86-95 - [c46]Piyush Sao, Richard W. Vuduc:
Self-stabilizing iterative solvers. ScalA@SC 2013: 4:1-4:8 - [e1]Alex Nicolau, Xiaowei Shen, Saman P. Amarasinghe, Richard W. Vuduc:
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP '13, Shenzhen, China, February 23-27, 2013. ACM 2013, ISBN 978-1-4503-1922-5 [contents] - [i1]Shel Swenson, Yogesh Simmhan, Viktor K. Prasanna, Manish Parashar, E. Jason Riedy, David A. Bader, Richard W. Vuduc:
Sustainable Software Development for Next-Gen Sequencing (NGS) Bioinformatics on Emerging Platforms. CoRR abs/1309.1828 (2013) - 2012
- [b1]Hyesoon Kim, Richard W. Vuduc, Sara S. Baghsorkhi, JeeWhan Choi, Wen-mei W. Hwu:
Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU). Synthesis Lectures on Computer Architecture, Morgan & Claypool Publishers 2012, ISBN 978-3-031-00609-8 - [j8]Ilya Lashuk, Aparna Chandramowlishwaran, Harper Langston, Tuan-Anh Nguyen, Rahul S. Sampath, Aashay Shringarpure, Richard W. Vuduc, Lexing Ying, Denis Zorin, George Biros:
A massively parallel adaptive fast multipole method on heterogeneous architectures. Commun. ACM 55(5): 101-109 (2012) - [j7]Jaekyu Lee, Hyesoon Kim, Richard W. Vuduc:
When Prefetching Works, When It Doesn't, and Why. ACM Trans. Archit. Code Optim. 9(1): 2:1-2:29 (2012) - [c45]Cong Hou, George Vulov, Daniel J. Quinlan, David R. Jefferson, Richard Fujimoto, Richard W. Vuduc:
A New Method for Program Inversion. CC 2012: 81-100 - [c44]Kenneth Czechowski, Casey Battaglino, Chris McClanahan, Kartik Iyer, P.-K. Yeung, Richard W. Vuduc:
On the communication complexity of 3D FFTs and its implications for Exascale. ICS 2012: 205-214 - [c43]Sangmin Park, Richard W. Vuduc, Mary Jean Harrold:
A Unified Approach for Localizing Non-deadlock Concurrency Bugs. ICST 2012: 51-60 - [c42]Richard W. Vuduc, Kenneth Czechowski, Aparna Chandramowlishwaran, JeeWhan Choi:
Courses in High-performance Computing for Scientists and Engineers. IPDPS Workshops 2012: 1335-1340 - [c41]Aparna Chandramowlishwaran, Richard W. Vuduc:
Communication-Optimal Parallel N-body Solvers. IPDPS Workshops 2012: 2462-2465 - [c40]JeeWhan Choi, Richard W. Vuduc:
Modeling and Analysis for Performance and Power. IPDPS Workshops 2012: 2466-2469 - [c39]Sooraj Bhat, Ashish Agarwal, Richard W. Vuduc, Alexander G. Gray:
A type theory for probability density functions. POPL 2012: 545-556 - [c38]Jaewoong Sim, Aniruddha Dasgupta, Hyesoon Kim, Richard W. Vuduc:
A performance analysis framework for identifying potential benefits in GPGPU applications. PPoPP 2012: 11-22 - [c37]Cong Hou, Daniel J. Quinlan, David R. Jefferson, Richard Fujimoto, Richard W. Vuduc:
Synthesizing Loops for Program Inversion. RC 2012: 72-84 - [c36]William B. March, Kenneth Czechowski, Marat Dukhan, Thomas Benson, Dongryeol Lee, Andrew J. Connolly, Richard W. Vuduc, Edmond Chow, Alexander G. Gray:
Optimizing the computation of n-point correlations on large-scale astronomical data. SC 2012: 74 - [c35]Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray:
A Distributed Kernel Summation Framework for General-Dimension Machine Learning. SDM 2012: 391-402 - [c34]Aparna Chandramowlishwaran, JeeWhan Choi, Kamesh Madduri, Richard W. Vuduc:
Brief announcement: towards a communication optimal fast multipole method and its implications at exascale. SPAA 2012: 182-184 - [c33]Richard W. Vuduc, Kenneth Czechowski:
Toward a Theory of Algorithm-Architecture Co-design. VECPAR 2012: 4-8 - 2011
- [j6]Richard W. Vuduc, Kent Czechowski:
What GPU Computing Means for High-End Systems. IEEE Micro 31(4): 74-78 (2011) - [c32]Kent Czechowski, Casey Battaglino, Chris McClanahan, Aparna Chandramowlishwaran, Richard W. Vuduc:
Balance Principles for Algorithm-Architecture Co-Design. HotPar 2011 - [c31]George Vulov, Cong Hou, Richard W. Vuduc, Richard Fujimoto, Daniel J. Quinlan, David R. Jefferson:
The Backstroke framework for source level reverse computation applied to parallel discrete event simulation. WSC 2011: 2965-2979 - [c30]Takahiro Katagiri, Richard W. Vuduc:
The Sixth International Workshop on Automatic Performance Tuning (iWAPT2011). ICCS 2011: 2124-2125 - [r1]Richard W. Vuduc:
Autotuning. Encyclopedia of Parallel Computing 2011: 102-105 - 2010
- [c29]Sangmin Park, Richard W. Vuduc, Mary Jean Harrold:
Falcon: fault localization in concurrent programs. ICSE (1) 2010: 245-254 - [c28]Aparna Chandramowlishwaran, Kathleen Knobe, Richard W. Vuduc:
Performance evaluation of concurrent collections on high-performance multicore computing systems. IPDPS 2010: 1-12 - [c27]Aparna Chandramowlishwaran, Samuel Williams, Leonid Oliker, Ilya Lashuk, George Biros, Richard W. Vuduc:
Optimizing and tuning the fast multipole method for state-of-the-art multicore architectures. IPDPS 2010: 1-12 - [c26]Richard W. Vuduc:
Unconventional wisdom in multicore computing. IPDPS 2010: 1 - [c25]Jaekyu Lee, Nagesh B. Lakshminarayana, Hyesoon Kim, Richard W. Vuduc:
Many-Thread Aware Prefetching Mechanisms for GPGPU Applications. MICRO 2010: 213-224 - [c24]JeeWhan Choi, Amik Singh, Richard W. Vuduc:
Model-driven autotuning of sparse matrix-vector multiply on GPUs. PPoPP 2010: 115-126 - [c23]Aparna Chandramowlishwaran, Kathleen Knobe, Richard W. Vuduc:
Applying the concurrent collections programming model to asynchronous parallel dense linear algebra. PPoPP 2010: 345-346 - [c22]Aparna Chandramowlishwaran, Kamesh Madduri, Richard W. Vuduc:
Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method. SC 2010: 1-12 - [c21]Abtin Rahimian, Ilya Lashuk, Shravan K. Veerapaneni, Aparna Chandramowlishwaran, Dhairya Malhotra, Logan Moon, Rahul S. Sampath, Aashay Shringarpure, Jeffrey S. Vetter, Richard W. Vuduc, Denis Zorin, George Biros:
Petascale Direct Numerical Simulation of Blood Flow on 200K Cores and Heterogeneous Architectures. SC 2010: 1-11 - [c20]Sooraj Bhat, Ashish Agarwal, Alexander G. Gray, Richard W. Vuduc:
Toward interactive statistical modeling. ICCS 2010: 1835-1844
2000 – 2009
- 2009
- [j5]Samuel Williams, Leonid Oliker, Richard W. Vuduc, John Shalf, Katherine A. Yelick, James Demmel:
Optimization of sparse matrix-vector multiplication on emerging multicore platforms. Parallel Comput. 35(3): 178-194 (2009) - [c19]Nitin Arora, Aashay Shringarpure, Richard W. Vuduc:
Direct N-body Kernels for Multicore Platforms. ICPP 2009: 379-387 - [c18]Sundaresan Venkatasubramanian, Richard W. Vuduc:
Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems. ICS 2009: 244-255 - [c17]Seunghwa Kang, David A. Bader, Richard W. Vuduc:
Understanding the design trade-offs among current multicore systems for numerical computations. IPDPS 2009: 1-12 - [c16]Chunhua Liao, Daniel J. Quinlan, Richard W. Vuduc, Thomas Panas:
Effective Source-to-Source Outlining to Support Whole Program Empirical Optimization. LCPC 2009: 308-322 - [c15]Ilya Lashuk, Aparna Chandramowlishwaran, Harper Langston, Tuan-Anh Nguyen, Rahul S. Sampath, Aashay Shringarpure, Richard W. Vuduc, Lexing Ying, Denis Zorin, George Biros:
A massively parallel adaptive fast-multipole method on heterogeneous architectures. SC 2009 - 2007
- [j4]Rajesh Nishtala, Richard W. Vuduc, James Demmel, Katherine A. Yelick:
When cache blocking of sparse matrix vector multiply works and why. Appl. Algebra Eng. Commun. Comput. 18(3): 297-311 (2007) - [c14]Thomas Panas, Thomas Epperly, Daniel J. Quinlan, Andreas Sæbjørnsen, Richard W. Vuduc:
Communicating Software Architecture using a Unified Single-View Visualization. ICECCS 2007: 217-228 - [c13]Qing Yi, Keith Seymour, Haihang You, Richard W. Vuduc, Daniel J. Quinlan:
POET: Parameterized Optimizations for Empirical Tuning. IPDPS 2007: 1-8 - [c12]Daniel J. Quinlan, Richard W. Vuduc, Ghassan Misherghi:
Techniques for specifying bug patterns. PADTAD 2007: 27-35 - [c11]Samuel Williams, Leonid Oliker, Richard W. Vuduc, John Shalf, Katherine A. Yelick, James Demmel:
Optimization of sparse matrix-vector multiplication on emerging multicore platforms. SC 2007: 38 - 2006
- [c10]Daniel J. Quinlan, Markus Schordan, Richard W. Vuduc, Qing Yi:
Annotating user-defined abstractions for optimization. IPDPS 2006 - [c9]Richard W. Vuduc, Martin Schulz, Daniel J. Quinlan, Bronis R. de Supinski, Andreas Sæbjørnsen:
Improving distributed memory applications testing by message perturbation. PADTAD 2006: 27-36 - 2005
- [j3]Richard Carl Demmel, Jack J. Dongarra, Victor Eijkhout, Erika Fuentes, Antoine Petitet, Richard W. Vuduc, R. Clint Whaley, Katherine A. Yelick:
Self-Adapting Linear Algebra Algorithms and Software. Proc. IEEE 93(2): 293-312 (2005) - [c8]Richard W. Vuduc, Hyun Jin Moon:
Fast Sparse Matrix-Vector Multiplication by Exploiting Variable Block Structure. HPCC 2005: 807-816 - [c7]Daniel J. Quinlan, Shmuel Ur, Richard W. Vuduc:
An Extensible Open-Source Compiler Infrastructure for Testing. Haifa Verification Conference 2005: 116-133 - 2004
- [j2]Richard W. Vuduc, James Demmel, Jeff A. Bilmes:
Statistical Models for Empirical Search-Based Performance Tuning. Int. J. High Perform. Comput. Appl. 18(1): 65-94 (2004) - [j1]Eun-Jin Im, Katherine A. Yelick, Richard W. Vuduc:
Sparsity: Optimization Framework for Sparse Matrix Kernels. Int. J. High Perform. Comput. Appl. 18(1): 135-158 (2004) - [c6]Benjamin C. Lee, Richard W. Vuduc, James Demmel, Katherine A. Yelick:
Performance Models for Evaluation and Automatic Tuning of Symmetric Sparse Matrix-Vector Multiply. ICPP 2004: 169-176 - 2003
- [c5]Rich Vuduc, Attila Gyulassy, James Demmel, Katherine A. Yelick:
Memory Hierarchy Optimizations and Performance ounds for Sparse A. International Conference on Computational Science 2003: 705-714 - 2002
- [c4]Rich Vuduc, James Demmel, Katherine A. Yelick, Shoaib Kamil, Rajesh Nishtala, Benjamin C. Lee:
Performance optimizations and bounds for sparse matrix-vector multiply. SC 2002: 35:1-35:35 - 2001
- [c3]Rich Vuduc, James Demmel, Jeff A. Bilmes:
Statistical Models for Automatic Performance Tuning. International Conference on Computational Science (1) 2001: 117-126 - 2000
- [c2]Rich Vuduc, James Demmel:
Code Generators for Automatic Tuning of Numerical Kernels: Experiences with FFTW. SAIG 2000: 190-211 - [c1]Danyel Fisher, Kris Hildrum, Jason I. Hong, Mark W. Newman, Megan Thomas, Rich Vuduc:
SWAMI: a framework for collaborative filtering algorithm development and evaluation. SIGIR 2000: 366-368
Coauthor Index
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