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Guojing Cong
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2020 – today
- 2024
- [c66]Shay Snyder
, Victoria Clerico
Transductive Spiking Graph Neural Networks for Loihi. ACM Great Lakes Symposium on VLSI 2024: 608-613 - [c65]Junqi Yin, Avishek Bose, Guojing Cong, Isaac Lyngaas, Quentin Anthony:
Comparative Study of Large Language Model Architectures on Frontier. IPDPS 2024: 556-569 - [c64]Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, J. Austin Ellis, Matthias Maiterth, Guojing Cong, Feiyi Wang, Prasanna Balaprakash:
Optimizing Distributed Training on Frontier for Large Language Models. ISC 2024: 1-11 - [i13]Junqi Yin
Comparative Study of Large Language Model Architectures on Frontier. CoRR abs/2402.00691 (2024) - [i12]Shay Snyder, Victoria Clerico, Guojing Cong, Shruti R. Kulkarni, Catherine D. Schuman, Sumedh R. Risbud, Maryam Parsa:
Transductive Spiking Graph Neural Networks for Loihi. CoRR abs/2404.17048 (2024) - [i11]Derek Gobin, Shay Snyder, Guojing Cong, Shruti R. Kulkarni, Catherine D. Schuman, Maryam Parsa:
Exploration of Novel Neuromorphic Methodologies for Materials Applications. CoRR abs/2405.04478 (2024) - [i10]Avishek Bose, Guojing Cong:
Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction. CoRR abs/2410.09280 (2024) - 2023
- [j12]Yicong Zhu, Changnian Han, Peng Zhang, Guojing Cong, James R. Kozloski, Chih-Chieh Yang, Leili Zhang, Yuefan Deng
AI-aided multiscale modeling of physiologically-significant blood clots. Comput. Phys. Commun. 287: 108718 (2023) - [j11]Guojing Cong
Improving materials property predictions for graph neural networks with minimal feature engineering *. Mach. Learn. Sci. Technol. 4(3): 35030 (2023) - [c63]Jiaji Ma, Guojing Cong, Scott Auerbach:
Clustering and GNN prediction with DrugMatrix. IEEE Big Data 2023: 4442-4449 - [c62]Guojing Cong, Scott Auerbach:
Clustering High-dimensional Toxicogenomics Data with Rare Signals. ICDM (Workshops) 2023: 608-614 - [c61]Guojing Cong, Shruti R. Kulkarni, Seung-Hwan Lim, Prasanna Date, Shay Snyder, Maryam Parsa, Dominic Kennedy, Catherine D. Schuman:
Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation. ICMLA 2023: 1541-1546 - [i9]Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - [i8]Sajal Dash, Isaac Lyngaas, Junqi Yin
Optimizing Distributed Training on Frontier for Large Language Models. CoRR abs/2312.12705 (2023) - 2022
- [j10]Changnian Han, Peng Zhang, Yicong Zhu, Guojing Cong, James R. Kozloski, Chih-Chieh Yang, Leili Zhang, Yuefan Deng
Scalable multiscale modeling of platelets with 100 million particles. J. Supercomput. 78(18): 19707-19724 (2022) - [c60]Guojing Cong, Seung-Hwan Lim, Steven Young:
Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning. ICDM (Workshops) 2022: 23-30 - [c59]Guojing Cong, Talia Ben-Naim, Victor Fung, Anshul Gupta, Rodrigo Neumann, Mathias Steiner:
Extensive Attention Mechanisms in Graph Neural Networks for Materials Discovery. ICDM (Workshops) 2022: 658-665 - [c58]Guojing Cong, Seung-Hwan Lim, Shruti R. Kulkarni, Prasanna Date, Thomas E. Potok, Shay Snyder, Maryam Parsa, Catherine D. Schuman
Semi-Supervised Graph Structure Learning on Neuromorphic Computers. ICONS 2022: 28:1-28:4 - [c57]Ramakrishnan Kannan, Piyush Sao
Exaflops Biomedical Knowledge Graph Analytics. SC 2022: 6:1-6:11 - [c56]Robert M. Patton, Prasanna Date, Shruti R. Kulkarni, Chathika Gunaratne, Seung-Hwan Lim, Guojing Cong, Steven R. Young, Mark Coletti, Thomas E. Potok, Catherine D. Schuman:
Neuromorphic Computing for Scientific Applications. RSDHA@SC 2022: 22-28 - [i7]Yicong Zhu, Changnian Han, Peng Zhang, Guojing Cong, James R. Kozloski, Chih-Chieh Yang, Leili Zhang, Yuefan Deng:
AI-aided multiscale modeling of physiologically-significant blood clots. CoRR abs/2205.14121 (2022) - [i6]Guojing Cong, Anshul Gupta, Rodrigo Neumann, Maíra de Bayser, Mathias Steiner, Breanndán Ó Conchúir:
Prediction of CO2 Adsorption in Nano-Pores with Graph Neural Networks. CoRR abs/2209.07567 (2022) - 2021
- [j9]Leili Zhang, Giacomo Domeniconi, Chih-Chieh Yang, Seung-gu Kang, Ruhong Zhou, Guojing Cong:
CASTELO: clustered atom subtypes aided lead optimization - a combined machine learning and molecular modeling method. BMC Bioinform. 22(1): 338 (2021) - [j8]Changnian Han
Artificial intelligence for accelerating time integrations in multiscale modeling. J. Comput. Phys. 427: 110053 (2021) - [c55]Seung-Hwan Lim, Junghoon Chae, Guojing Cong, Drahomira Herrmannova, Robert M. Patton, Ramakrishnan Kannan, Thomas E. Potok:
Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis. IEEE BigData 2021: 4381-4386 - [c54]Guojing Cong:
Elastic distributed training with fast convergence and efficient resource utilization. ICMLA 2021: 972-979 - [c53]Guojing Cong, Seung-Hwan Lim:
Versatile feature learning with graph convolutions and graph structures. ICDM (Workshops) 2021: 669-677 - [c52]Yicong Zhu, Peng Zhang, Changnian Han, Guojing Cong, Yuefan Deng:
Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers. ISC 2021: 237-254 - [i5]Guojing Cong, Tianyi Liu:
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum. CoRR abs/2110.00625 (2021) - 2020
- [c51]Chih-Chieh Yang, Giacomo Domeniconi, Leili Zhang, Guojing Cong:
Design of AI-Enhanced Drug Lead Optimization Workflow for HPC and Cloud. IEEE BigData 2020: 5861-5863 - [c50]Guojing Cong, Li Zhang, Chih-Chieh Yang:
Partial data permutation for training deep neural networks. CCGRID 2020: 728-735 - [c49]Guojing Cong, Brian Kingsbury, Chih-Chieh Yang, Tianyi Liu:
Fast Training of Deep Neural Networks for Speech Recognition. ICASSP 2020: 6884-6888 - [c48]Guojing Cong, Tianyi Liu:
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum. MLHPC/AI4S@SC 2020: 29-39 - [i4]Leili Zhang, Giacomo Domeniconi, Chih-Chieh Yang, Seung-gu Kang, Ruhong Zhou, Guojing Cong:
CASTELO: Clustered Atom Subtypes aidEd Lead Optimization - a combined machine learning and molecular modeling method. CoRR abs/2011.13788 (2020)
2010 – 2019
- 2019
- [j7]Guojing Cong, Giacomo Domeniconi, Chih-Chieh Yang, Joshua Shapiro, Fan Zhou, Barry Chen:
Fast neural network training on a cluster of GPUs for action recognition with high accuracy. J. Parallel Distributed Comput. 134: 153-165 (2019) - [c47]Chih-Chieh Yang, Guojing Cong:
Accelerating Data Loading in Deep Neural Network Training. HiPC 2019: 235-245 - [c46]Guojing Cong, Chih-Chieh Yang, Fan Zhou:
Reducing global reductions in large-scale distributed training. ICPP Workshops 2019: 22:1-22:9 - [c45]Ian Karlin, Yoonho Park, Bronis R. de Supinski, Peng Wang, Bert Still, David Beckingsale, Robert Blake, Tong Chen, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher
Preparation and optimization of a diverse workload for a large-scale heterogeneous system. SC 2019: 32:1-32:17 - [c44]Guojing Cong, Giacomo Domeniconi, Joshua Shapiro, Chih-Chieh Yang, Barry Chen:
Video Action Recognition With an Additional End-to-End Trained Temporal Stream. WACV 2019: 51-60 - [i3]Fan Zhou, Guojing Cong:
A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction. CoRR abs/1903.05133 (2019) - [i2]Chih-Chieh Yang, Guojing Cong:
Accelerating Data Loading in Deep Neural Network Training. CoRR abs/1910.01196 (2019) - 2018
- [c43]Fan Zhou, Guojing Cong:
On the Convergence Properties of a K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization. IJCAI 2018: 3219-3227 - [c42]Guojing Cong, Giacomo Domeniconi, Joshua Shapiro, Fan Zhou, Barry Chen:
Accelerating Deep Neural Network Training for Action Recognition on a Cluster of GPUs. SBAC-PAD 2018: 298-305 - 2017
- [j6]Christian Plessl
Foreword to the special issue of the 18th IEEE international conference on computational science and engineering (CSE2015). Concurr. Comput. Pract. Exp. 29(7) (2017) - [c41]Guojing Cong, Onkar Bhardwaj
A Hierarchical, Bulk-Synchronous Stochastic Gradient Descent Algorithm for Deep-Learning Applications on GPU Clusters. ICMLA 2017: 818-821 - [c40]Guojing Cong, Onkar Bhardwaj
An Efficient, Distributed Stochastic Gradient Descent Algorithm for Deep-Learning Applications. ICPP 2017: 11-20 - [c39]Guojing Cong, Brian Kingsbury, Soumyadip Gosh, George Saon
Accelerating deep neural network learning for speech recognition on a cluster of GPUs. MLHPC@SC 2017: 3:1-3:8 - [i1]Fan Zhou, Guojing Cong:
On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimization. CoRR abs/1708.01012 (2017) - 2016
- [c38]Guojing Cong, Ilie Gabriel Tanase:
Composable Locality Optimizations for Accelerating Parallel Forest Computations. HPCC/SmartCity/DSS 2016: 190-197 - [c37]Onkar Bhardwaj, Guojing Cong:
Practical Efficiency of Asynchronous Stochastic Gradient Descent. MLHPC@SC 2016: 56-62 - 2015
- [c36]Guojing Cong, Ilie Gabriel Tanase, Yinglong Xia:
Accelerating Minimum Spanning Forest Computations on Multicore Platforms. Euro-Par Workshops 2015: 541-552 - [c35]Guojing Cong, Carol Meyers, Deepak Rajan, Tiziano Parriani:
Parallel Strategies for Solving Large Unit Commitment Problems in the California ISO Planning Model. IPDPS 2015: 710-719 - [c34]Kattamuri Ekanadham, Guojing Cong:
Memory Centric Computation (Mc2) for Large-Scale Graph Processing. SBAC-PAD 2015: 49-56 - [c33]Guojing Cong, Sophia Wen, James Sedgwick, Louis Ly:
Parallelism-centric optimization and performance study of a finance aggregation engine on modern NUMA systems. WHPCF@SC 2015: 2:1-2:7 - [e1]Christian Plessl, Didier El Baz, Guojing Cong, João M. P. Cardoso, Luís Veiga, Thomas Rauber:
18th IEEE International Conference on Computational Science and Engineering, CSE 2015, Porto, Portugal, October 21-23, 2015. IEEE Computer Society 2015, ISBN 978-1-4673-8297-7 [contents] - 2014
- [c32]Guojing Cong, Paul Muzio:
Fast Parallel Connected Components Algorithms on GPUs. Euro-Par Workshops (1) 2014: 153-164 - [c31]Guojing Cong:
A Synchronous Parallel Max-Flow Algorithm for Real-World Networks. HPCC/CSS/ICESS 2014: 68-75 - 2013
- [c30]Guojing Cong, Hui-Fang Wen:
Mapping applications for high performance on multithreaded, NUMA systems. Conf. Computing Frontiers 2013: 7:1-7:4 - [c29]Guojing Cong, Hui-Fang Wen:
Maximizing the performance of irregular applications on multithreaded, NUMA systems. IA3@SC 2013: 4:1-4:8 - 2012
- [j5]Guojing Cong, I-Hsin Chung, Hui-Fang Wen, David J. Klepacki, Hiroki Murata, Yasushi Negishi, Takao Moriyama:
A Systematic Approach toward Automated Performance Analysis and Tuning. IEEE Trans. Parallel Distributed Syst. 23(3): 426-435 (2012) - [c28]Guojing Cong, Hui-Fang Wen, Hiroki Murata, Yasushi Negishi:
Tool-assisted Optimization of Shared-memory Accesses in UPC Applications. HPCC-ICESS 2012: 104-111 - [c27]Guojing Cong, Konstantin Makarychev:
Optimizing Large-scale Graph Analysis on Multithreaded, Multicore Platforms. IPDPS 2012: 414-425 - [c26]Guojing Cong, Hui-Fang Wen, I-Hsin Chung, David J. Klepacki, Hiroki Murata, Yasushi Negishi:
An Efficient Framework for Multi-dimensional Tuning of High Performance Computing Applications. IPDPS 2012: 1376-1387 - [c25]Yasushi Negishi, Hiroki Murata, Guojing Cong, Hui-Fang Wen, I-Hsin Chung:
A static analysis tool using a three-step approach for data races in HPC programs. PADTAD 2012: 11-17 - [c24]I-Hsin Chung, Changhoan Kim, Hui-Fang Wen, Guojing Cong:
Application data prefetching on the IBM blue gene/Q supercomputer. SC 2012: 88 - 2011
- [c23]Guojing Cong, Konstantin Makarychev:
Optimizing Large-Scale Graph Analysis on a Multi-threaded, Multi-core Platform. IPDPS 2011: 688-697 - [r6]David A. Bader
Graph Algorithms. Encyclopedia of Parallel Computing 2011: 796-805 - [r5]Guojing Cong, David A. Bader
Hybrid Programming With SIMPLE. Encyclopedia of Parallel Computing 2011: 851-860 - [r4]David A. Bader
Spanning Tree, Minimum Weight. Encyclopedia of Parallel Computing 2011: 1870-1877 - [r3]David A. Bader
SWARM: A Parallel Programming Framework for Multicore Processors. Encyclopedia of Parallel Computing 2011: 1966-1971 - 2010
- [j4]Seetharami R. Seelam, I-Hsin Chung, Guojing Cong, Hui-Fang Wen, David J. Klepacki:
Workload performance characterization of DARPA HPCS benchmarks. Concurr. Comput. Pract. Exp. 22(4): 441-461 (2010) - [c22]Judit Giménez
Guided Performance Analysis Combining Profile and Trace Tools. Euro-Par Workshops 2010: 513-521 - [c21]Guojing Cong, I-Hsin Chung, Hui-Fang Wen, David J. Klepacki, Hiroki Murata, Yasushi Negishi, Takao Moriyama:
Application tuning through bottleneck-driven refactoring. IPDPS Workshops 2010: 1-7 - [c20]Guojing Cong, George Almási, Vijay A. Saraswat:
Fast PGAS Implementation of Distributed Graph Algorithms. SC 2010: 1-11
2000 – 2009
- 2009
- [c19]Guojing Cong, Konstantin Makarychev:
Improving Memory Access Locality for Large-Scale Graph Analysis Applications. PDCCS 2009: 121-127 - [c18]Guojing Cong, I-Hsin Chung, Hui-Fang Wen, David J. Klepacki, Hiroki Murata, Yasushi Negishi, Takao Moriyama:
A Holistic Approach towards Automated Performance Analysis and Tuning. Euro-Par 2009: 33-44 - [c17]Guojing Cong, Seetharami R. Seelam, I-Hsin Chung, Sophia Wen, David J. Klepacki:
Towards a framework for automated performance tuning. IPDPS 2009: 1-8 - 2008
- [c16]Seetharami R. Seelam, I-Hsin Chung, Guojing Cong, Hui-Fang Wen, David J. Klepacki:
Workload Performance Characterization of DARPA HPCS Benchmarks. HPCC 2008: 342-351 - [c15]Guojing Cong, Sreedhar B. Kodali, Sriram Krishnamoorthy, Doug Lea, Vijay A. Saraswat, Tong Wen:
Solving Large, Irregular Graph Problems Using Adaptive Work-Stealing. ICPP 2008: 536-545 - [c14]I-Hsin Chung, Guojing Cong, David J. Klepacki, Simone Sbaraglia, Seetharami R. Seelam, Hui-Fang Wen:
A framework for automated performance bottleneck detection. IPDPS 2008: 1-7 - [c13]Guojing Cong, Hanhong Xue:
A scalable, asynchronous spanning tree algorithm on a cluster of SMPs. IPDPS 2008: 1-6 - 2007
- [c12]Abhinav Bhatele, Guojing Cong:
A Selective Pro ling Tool: Towards Automatic Performance Tuning. IPDPS 2007: 1-6 - [c11]Guojing Cong, David A. Bader
Techniques for Designing Efficient Parallel Graph Algorithms for SMPs and Multicore Processors. ISPA 2007: 137-147 - [c10]Hui-Fang Wen, Simone Sbaraglia, Seetharami R. Seelam, I-Hsin Chung, Guojing Cong, David J. Klepacki:
A Productivity Centered Tools Framework for Application Performance Tuning. QEST 2007: 273-274 - [c9]Simone Sbaraglia, Hui-Fang Wen, Seetharami R. Seelam, I-Hsin Chung, Guojing Cong, Kattamuri Ekanadham, David J. Klepacki:
A productivity centered application performance tuning framework. VALUETOOLS 2007: 49 - [r2]Guojing Cong, David A. Bader:
Efficient Parallel Graph Algorithms for Multicore and Multiprocessors. Handbook of Parallel Computing 2007 - [r1]Kamesh Madduri, John Feo, Guojing Cong, David A. Bader:
Design of Multithreaded Algorithms for Combinatorial Problems. Handbook of Parallel Computing 2007 - 2006
- [j3]Guojing Cong, David A. Bader
Designing irregular parallel algorithms with mutual exclusion and lock-free protocols. J. Parallel Distributed Comput. 66(6): 854-866 (2006) - [j2]David A. Bader
Fast shared-memory algorithms for computing the minimum spanning forest of sparse graphs. J. Parallel Distributed Comput. 66(11): 1366-1378 (2006) - [c8]Guojing Cong, Simone Sbaraglia:
A Study on the Locality Behavior of Minimum Spanning Tree Algorithms. HiPC 2006: 583-594 - 2005
- [j1]David A. Bader
A fast, parallel spanning tree algorithm for symmetric multiprocessors (SMPs). J. Parallel Distributed Comput. 65(9): 994-1006 (2005) - [c7]Guojing Cong, David A. Bader:
An Empirical Analysis of Parallel Random Permutation Algorithms ON SMPs. PDCS 2005: 27-34 - [c6]David A. Bader
On the Architectural Requirements for Efficient Execution of Graph Algorithms. ICPP 2005: 547-556 - [c5]Guojing Cong
An Experimental Study of Parallel Biconnected Components Algorithms on Symmetric Multiprocessors (SMPs). IPDPS 2005 - 2004
- [c4]Guojing Cong, David A. Bader
Lock-Free Parallel Algorithms: An Experimental Study. HiPC 2004: 516-528 - [c3]Guojing Cong, David A. Bader
The Euler Tour Technique and Parallel Rooted Spanning Tree. ICPP 2004: 448-457 - [c2]David A. Bader
A Fast, Parallel Spanning Tree Algorithm for Symmetric Multiprocessors. IPDPS 2004 - [c1]David A. Bader
Fast Shared-Memory Algorithms for Computing the Minimum Spanning Forest of Sparse Graphs. IPDPS 2004
Coauthor Index
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