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2020 – today
- 2024
[j28]François Tessier
, Venkatram Vishwanath, Emmanuel Jeannot:
Adding topology and memory awareness in data aggregation algorithms. Future Gener. Comput. Syst. 159: 188-203 (2024)- [j27]Scott Cheng, Jun-Liang Lin, Murali Emani, Siddhisanket Raskar, Sam Foreman, Zhen Xie, Venkatram Vishwanath, Mahmut Taylan Kandemir:
Thorough Characterization and Analysis of Large Transformer Model Training At-Scale. Proc. ACM Meas. Anal. Comput. Syst. 8(1): 8:1-8:25 (2024) - [c98]Krishna Teja Chitty-Venkata, Varuni Katti Sastry, Murali Emani, Venkatram Vishwanath, Sanjif Shanmugavelu, Sylvia Howland:
WActiGrad: Structured Pruning for Efficient Finetuning and Inference of Large Language Models on AI Accelerators. Euro-Par (2) 2024: 317-331 - [c97]Murali Emani, Sam Foreman, Varuni Sastry, Zhen Xie, Siddhisanket Raskar, William Arnold, Rajeev Thakur, Venkatram Vishwanath, Michael E. Papka, Sanjif Shanmugavelu, Darshan Gandhi, Hengyu Zhao, Dun Ma, Kiran Ranganath, Rick Weisner, Jiunn-yeu Chen, Yuting Yang, Natalia Vassilieva, Bin C. Zhang, Sylvia Howland, Alexander Tsyplikhin:
Toward a Holistic Performance Evaluation of Large Language Models Across Diverse AI Accelerators. IPDPS (Workshops) 2024: 1-10 - [c96]Archit Vasan, Ozan Gökdemir, Alexander Brace, Arvind Ramanathan, Thomas S. Brettin, Rick Stevens, Venkatram Vishwanath:
High Performance Binding Affinity Prediction with a Transformer-Based Surrogate Model. IPDPS (Workshops) 2024: 571-580 - [c95]Scott Cheng, Jun-Liang Lin, Murali Emani, Siddhisanket Raskar, Sam Foreman, Zhen Xie, Venkatram Vishwanath, Mahmut T. Kandemir:
Thorough Characterization and Analysis of Large Transformer Model Training At-Scale. SIGMETRICS/Performance (Abstracts) 2024: 39-40 - [c94]Zhen Xie, Murali Emani, Xiaodong Yu, Dingwen Tao, Xin He, Pengfei Su, Keren Zhou, Venkatram Vishwanath:
Centimani: Enabling Fast AI Accelerator Selection for DNN Training with a Novel Performance Predictor. USENIX ATC 2024: 1203-1221 - 2023
- [j26]Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
Neural Architecture Search Benchmarks: Insights and Survey. IEEE Access 11: 25217-25236 (2023) - [j25]Krishna Teja Chitty-Venkata, Yiming Bian, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
Differentiable Neural Architecture, Mixed Precision and Accelerator Co-Search. IEEE Access 11: 106670-106687 (2023) - [j24]Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, Defne G. Ozgulbas, Natalia Vassilieva, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan:
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. Int. J. High Perform. Comput. Appl. 37(6): 683-705 (2023) - [j23]Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
A survey of techniques for optimizing transformer inference. J. Syst. Archit. 144: 102990 (2023) - [c93]Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash:
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization. e-Science 2023: 1-10 - [c92]Zhen Xie, Siddhisanket Raskar, Murali Emani, Venkatram Vishwanath:
TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators. Euro-Par 2023: 458-473 - [c91]Archit Vasan, Thomas S. Brettin, Rick Stevens, Arvind Ramanathan, Venkatram Vishwanath:
Scalable Lead Prediction with Transformers using HPC resources. SC Workshops 2023: 123 - [c90]Khalid Hossain, Riccardo Balin, Corey Adams, Thomas D. Uram, Kalyan Kumaran, Venkatram Vishwanath, Tanima Dey, Subrata Goswami, Janghaeng Lee, Rebecca Ramer, Koichi Yamada:
Demonstration of Portable Performance of Scientific Machine Learning on High Performance Computing Systems. SC Workshops 2023: 644-647 - [c89]Reet Barik, Siddhisanket Raskar, Murali Emani, Venkatram Vishwanath:
Characterizing the Performance of Triangle Counting on Graphcore's IPU Architecture. SC Workshops 2023: 1949-1957 - [c88]Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Ramakrishnan Sivakumar, Sanjif Shanmugavelu, Zhengyu Chen, Lev Zlotnik, Mingran Wang, Philip Colangelo, Andrew Deng, Philip Lassen, Shukur Pathan:
Exploring the Use of Dataflow Architectures for Graph Neural Network Workloads. ISC Workshops 2023: 648-661 - [i14]Shilpika, Bethany Lusch, Murali Emani, Filippo Simini, Venkatram Vishwanath, Michael E. Papka, Kwan-Liu Ma:
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems. CoRR abs/2306.09457 (2023) - [i13]Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
A Survey of Techniques for Optimizing Transformer Inference. CoRR abs/2307.07982 (2023) - [i12]Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash:
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives. CoRR abs/2309.14936 (2023) - [i11]Murali Emani, Sam Foreman, Varuni Sastry, Zhen Xie, Siddhisanket Raskar, William Arnold, Rajeev Thakur, Venkatram Vishwanath, Michael E. Papka:
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators. CoRR abs/2310.04607 (2023) - [i10]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, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens:
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - 2022
- [j22]Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
Neural Architecture Search for Transformers: A Survey. IEEE Access 10: 108374-108412 (2022) - [j21]Anda Trifan, Defne Gorgun, Michael Salim, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Austin Clyde, David Clark, David J. Hardy, Tom Burnley, Lei Huang, John D. McCalpin, Murali Emani, Hyenseung Yoo, Junqi Yin, Aristeidis Tsaris, Vishal Subbiah, Tanveer Raza, Jessica Liu, Noah Trebesch, Geoffrey Wells, Venkatesh Mysore, Tom Gibbs, James C. Phillips, S. Chakra Chennubhotla, Ian T. Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, John E. Stone, Emad Tajkhorshid, Sarah A. Harris, Arvind Ramanathan:
Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. Int. J. High Perform. Comput. Appl. 36(5-6): 603-623 (2022) - [j20]Romit Maulik, Dimitrios Fytanidis, Bethany Lusch, Venkatram Vishwanath, Saumil Patel:
PythonFOAM: In-situ data analyses with OpenFOAM and Python. J. Comput. Sci. 62: 101750 (2022) - [c87]Huihuo Zheng, Venkatram Vishwanath, Quincey Koziol, Houjun Tang, John Ravi, John Mainzer, Suren Byna:
HDF5 Cache VOL: Efficient and Scalable Parallel I/O through Caching Data on Node-local Storage. CCGRID 2022: 61-70 - [c86]Hariharan Devarajan, Anthony Kougkas, Huihuo Zheng, Venkatram Vishwanath, Xian-He Sun:
Stimulus: Accelerate Data Management for Scientific AI applications in HPC. CCGRID 2022: 109-118 - [c85]Shilpika, Bethany Lusch, Murali Emani, Filippo Simini, Venkatram Vishwanath, Michael E. Papka, Kwan-Liu Ma:
Toward an In-Depth Analysis of Multifidelity High Performance Computing Systems. CCGRID 2022: 716-725 - [c84]Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
Efficient Design Space Exploration for Sparse Mixed Precision Neural Architectures. HPDC 2022: 265-276 - [c83]Murali Emani, Zhen Xie, Siddhisanket Raskar, Varuni Sastry, William Arnold, Bruce Wilson, Rajeev Thakur, Venkatram Vishwanath, Zhengchun Liu, Michael E. Papka, Cindy Orozco Bohorquez, Rick Weisner, Karen Li, Yongning Sheng, Yun Du, Jian Zhang, Alexander Tsyplikhin, Gurdaman Khaira, Jeremy Fowers, Ramakrishnan Sivakumar, Victoria Godsoe, Adrián Macías, Chetan Tekur, Matthew Boyd:
A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads. PMBS@SC 2022: 13-25 - [c82]Jeyan Thiyagalingam, Gregor von Laszewski, Junqi Yin, Murali Emani, Juri Papay, Gregg Barrett, Piotr Luszczek, Aristeidis Tsaris, Christine R. Kirkpatrick, Feiyi Wang, Tom Gibbs, Venkatram Vishwanath, Mallikarjun Shankar, Geoffrey C. Fox, Tony Hey:
AI Benchmarking for Science: Efforts from the MLCommons Science Working Group. ISC Workshops 2022: 47-64 - [i9]Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash:
Asynchronous Distributed Bayesian Optimization at HPC Scale. CoRR abs/2207.00479 (2022) - [i8]Ryien Hosseini, Filippo Simini, Venkatram Vishwanath:
Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications. CoRR abs/2207.09955 (2022) - 2021
- [j19]Murali Emani, Venkatram Vishwanath, Corey Adams, Michael E. Papka, Rick Stevens, Laura Florescu, Sumti Jairath, William Liu, Tejas Nama, Arvind Sujeeth, Volodymyr V. Kindratenko, Anne C. Elster:
Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture. Comput. Sci. Eng. 23(2): 114-119 (2021) - [c81]Hariharan Devarajan, Huihuo Zheng, Anthony Kougkas, Xian-He Sun, Venkatram Vishwanath:
DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications. CCGRID 2021: 81-91 - [c80]Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey C. Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin:
MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. MLHPC@SC 2021: 33-45 - [c79]Alexander Brace, Michael Salim, Vishal Subbiah, Heng Ma, Murali Emani, Anda Trifan, Austin R. Clyde, Corey Adams, Thomas D. Uram, Hyun Seung Yoo, Andew Hock, Jessica Liu, Venkatram Vishwanath, Arvind Ramanathan:
Stream-AI-MD: streaming AI-driven adaptive molecular simulations for heterogeneous computing platforms. PASC 2021: 6:1-6:13 - [c78]Romain Égelé, Prasanna Balaprakash, Isabelle Guyon, Venkatram Vishwanath, Fangfang Xia, Rick Stevens, Zhengying Liu:
AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data. SC 2021: 30 - [i7]Romit Maulik, Dimitrios Fytanidis, Bethany Lusch, Venkatram Vishwanath, Saumil Patel:
PythonFOAM: In-situ data analyses with OpenFOAM and Python. CoRR abs/2103.09389 (2021) - [i6]Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey C. Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin:
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. CoRR abs/2110.11466 (2021) - 2020
- [j18]Hank Childs, Sean Ahern, James P. Ahrens, Andrew C. Bauer, Janine Bennett, E. Wes Bethel, Peer-Timo Bremer, Eric Brugger, Joseph Cottam, Matthieu Dorier, Soumya Dutta, Jean M. Favre, Thomas Fogal, Steffen Frey, Christoph Garth, Berk Geveci, William F. Godoy, Charles D. Hansen, Cyrus Harrison, Bernd Hentschel, Joseph A. Insley, Christopher R. Johnson, Scott Klasky, Aaron Knoll, James Kress, Matthew Larsen, Jay F. Lofstead, Kwan-Liu Ma, Preeti Malakar, Jeremy S. Meredith, Kenneth Moreland, Paul A. Navrátil, Patrick O'Leary, Manish Parashar, Valerio Pascucci, John Patchett, Tom Peterka, Steve Petruzza, Norbert Podhorszki, David Pugmire, Michel E. Rasquin, Silvio Rizzi, David H. Rogers, Sudhanshu Sane, Franz Sauer, Robert Sisneros, Han-Wei Shen, Will Usher, Rhonda Vickery, Venkatram Vishwanath, Ingo Wald, Ruonan Wang, Gunther H. Weber, Brad Whitlock, Matthew Wolf, Hongfeng Yu, Sean B. Ziegeler:
A terminology for in situ visualization and analysis systems. Int. J. High Perform. Comput. Appl. 34(6) (2020) - [j17]Suren Byna, M. Scot Breitenfeld, Bin Dong, Quincey Koziol, Elena Pourmal, Dana Robinson, Jérome Soumagne, Houjun Tang, Venkatram Vishwanath, Richard Warren:
ExaHDF5: Delivering Efficient Parallel I/O on Exascale Computing Systems. J. Comput. Sci. Technol. 35(1): 145-160 (2020) - [j16]Ganesh Sivaraman, Nicholas E. Jackson, Benjamín Sánchez-Lengeling, Álvaro Vázquez-Mayagoitia, Alán Aspuru-Guzik, Venkatram Vishwanath, Juan J. de Pablo:
A machine learning workflow for molecular analysis: application to melting points. Mach. Learn. Sci. Technol. 1(2): 25015 (2020) - [c77]Ivana Marincic, Venkatram Vishwanath, Henry Hoffmann:
SeeSAw: Optimizing Performance of In-Situ Analytics Applications under Power Constraints. IPDPS 2020: 789-798 - [i5]Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu:
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data. CoRR abs/2010.16358 (2020)
2010 – 2019
- 2019
- [c76]Shilpika, Bethany Lusch, Murali Emani, Venkatram Vishwanath, Michael E. Papka, Kwan-Liu Ma:
MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs. DAAC@SC 2019: 13-18 - [c75]Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Venkatram Vishwanath, Michael E. Papka:
Balsam: Near Real-Time Experimental Data Analysis on Supercomputers. XLOOP@SC 2019: 26-31 - [c74]Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens:
Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. SC 2019: 37:1-37:33 - [c73]Wushi Dong, Nicola J. Ferrier, Narayanan Kasthuri, Peter Littlewood, Murat Keçeli, Rafael Vescovi, Hanyu Li, Corey Adams, Elise Jennings, Samuel Flender, Thomas D. Uram, Venkatram Vishwanath:
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping. DLS@SC 2019: 52-61 - [i4]George K. Thiruvathukal, Cameron Christensen, Xiaoyong Jin, François Tessier, Venkatram Vishwanath:
A Benchmarking Study to Evaluate Apache Spark on Large-Scale Supercomputers. CoRR abs/1904.11812 (2019) - [i3]Wushi Dong, Murat Keçeli, Rafael Vescovi, Hanyu Li, Corey Adams, Thomas D. Uram, Venkatram Vishwanath, Bobby Kasthuri, Nicola J. Ferrier, Peter Littlewood:
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping. CoRR abs/1905.06236 (2019) - [i2]Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens:
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. CoRR abs/1909.00311 (2019) - [i1]Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, Michael E. Papka:
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows. CoRR abs/1909.08704 (2019) - 2018
- [c72]Houjun Tang, Suren Byna, Francois Tessier, Teng Wang, Bin Dong, Jingqing Mu, Quincey Koziol, Jérome Soumagne, Venkatram Vishwanath, Jialin Liu, Richard Warren:
Toward Scalable and Asynchronous Object-Centric Data Management for HPC. CCGrid 2018: 113-122 - [c71]François Tessier, Paul Gressier, Venkatram Vishwanath:
Optimizing Data Aggregation by Leveraging the Deep Memory Hierarchy on Large-scale Systems. ICS 2018: 229-239 - [c70]Preeti Malakar, Todd S. Munson, Christopher Knight, Venkatram Vishwanath, Michael E. Papka:
Topology-aware space-shared co-analysis of large-scale molecular dynamics simulations. SC 2018: 24:1-24:15 - [c69]Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali A. Morozov, Kalyan Kumaran:
Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications. PMBS@SC 2018: 33-44 - [c68]Will Usher, Silvio Rizzi, Ingo Wald, Jefferson Amstutz, Joseph A. Insley, Venkatram Vishwanath, Nicola J. Ferrier, Michael E. Papka, Valerio Pascucci:
libIS: a lightweight library for flexible in transit visualization. ISAV@SC 2018: 33-38 - 2017
- [j15]Salman Habib, Vitali A. Morozov, Nicholas Frontiere, Hal Finkel, Adrian Pope, Katrin Heitmann, Kalyan Kumaran, Venkatram Vishwanath, Tom Peterka, Joseph A. Insley, David Daniel, Patricia K. Fasel, Zarija Lukic:
HACC: extreme scaling and performance across diverse architectures. Commun. ACM 60(1): 97-104 (2017) - [j14]Preeti Malakar, Venkatram Vishwanath:
Hierarchical Read-Write Optimizations for Scientific Applications with Multi-variable Structured Datasets. Int. J. Parallel Program. 45(1): 94-108 (2017) - [j13]Preeti Malakar, Venkatram Vishwanath:
Data movement optimizations for independent MPI I/O on the Blue Gene/Q. Parallel Comput. 61: 35-51 (2017) - [c67]Sudheer Chunduri, Prasanna Balaprakash, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran:
Analytical Performance Modeling and Validation of Intel's Xeon Phi Architecture. Conf. Computing Frontiers 2017: 247-250 - [c66]Francois Tessier, Venkatram Vishwanath, Emmanuel Jeannot:
TAPIOCA: An I/O Library for Optimized Topology-Aware Data Aggregation on Large-Scale Supercomputers. CLUSTER 2017: 70-80 - [c65]Takanori Fujiwara, Preeti Malakar, Khairi Reda, Venkatram Vishwanath, Michael E. Papka, Kwan-Liu Ma:
A Visual Analytics System for Optimizing Communications in Massively Parallel Applications. VAST 2017: 59-70 - [c64]Preeti Malakar, Christopher Knight, Todd S. Munson, Venkatram Vishwanath, Michael E. Papka:
Scalable In situ Analysis of Molecular Dynamics Simulations. ISAV@SC 2017: 1-6 - [c63]Ivana Marincic, Venkatram Vishwanath, Henry Hoffmann:
PoLiMEr: An Energy Monitoring and Power Limiting Interface for HPC Applications. E2SC@SC 2017: 7:1-7:8 - [c62]Michael J. Lewis, George K. Thiruvathukal, Venkatram Vishwanath, Michael E. Papka, Andrew E. Johnson:
A distributed graph approach for pre-processing linked RDF data using supercomputers. SBD@SIGMOD 2017: 6:1-6:6 - 2016
- [j12]Andrew C. Bauer, Hasan Abbasi, James P. Ahrens, Hank Childs, Berk Geveci, Scott Klasky, Kenneth Moreland, Patrick O'Leary, Venkatram Vishwanath, Brad Whitlock, E. Wes Bethel:
In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms. Comput. Graph. Forum 35(3): 577-597 (2016) - [j11]Ketan Maheshwari, Eun-Sung Jung, Jiayuan Meng, Vitali A. Morozov, Venkatram Vishwanath, Rajkumar Kettimuthu:
Workflow performance improvement using model-based scheduling over multiple clusters and clouds. Future Gener. Comput. Syst. 54: 206-218 (2016) - [j10]Huy Bui, Eun-Sung Jung, Venkatram Vishwanath, Andrew E. Johnson, Jason Leigh, Michael E. Papka:
Improving sparse data movement performance using multiple paths on the Blue Gene/Q supercomputer. Parallel Comput. 51: 3-16 (2016) - [j9]Sean Wallace, Zhou Zhou, Venkatram Vishwanath, Susan Coghlan, John R. Tramm, Zhiling Lan, Michael E. Papka:
Application power profiling on IBM Blue Gene/Q. Parallel Comput. 57: 73-86 (2016) - [c61]Silvio Rizzi, Mark Hereld, Joseph A. Insley, Preeti Malakar, Michael E. Papka, Thomas D. Uram, Venkatram Vishwanath:
Coupling LAMMPS and the vl3 Framework for Co-Visualization of Atomistic Simulations. IPDPS Workshops 2016: 1038-1042 - [c60]Min Shih, Silvio Rizzi, Joseph A. Insley, Thomas D. Uram, Venkatram Vishwanath, Mark Hereld, Michael E. Papka, Kwan-Liu Ma:
Parallel distributed, GPU-accelerated, advanced lighting calculations for large-scale volume visualization. LDAV 2016: 47-55 - [c59]Dawid Zawislak, Brian R. Toonen, William E. Allcock, Silvio Rizzi, Joseph A. Insley, Venkatram Vishwanath, Michael E. Papka:
Early Investigations into Using a Remote RAM Pool with the vl3 Visualization Framework. ISAV@SC 2016: 23-28 - [c58]Francois Tessier, Preeti Malakar, Venkatram Vishwanath, Emmanuel Jeannot, Florin Isaila:
Topology-Aware Data Aggregation for Intensive I/O on Large-Scale Supercomputers. COMHPC@SC 2016: 73-81 - [c57]Sean Wallace, Xu Yang, Venkatram Vishwanath, William E. Allcock, Susan Coghlan, Michael E. Papka, Zhiling Lan:
A data driven scheduling approach for power management on HPC systems. SC 2016: 656-666 - [c56]Preeti Malakar, Venkatram Vishwanath, Christopher Knight, Todd S. Munson, Michael E. Papka:
Optimal execution of co-analysis for large-scale molecular dynamics simulations. SC 2016: 702-715 - [c55]Utkarsh Ayachit, Andrew C. Bauer, Earl P. N. Duque, Greg Eisenhauer, Nicola J. Ferrier, Junmin Gu, Kenneth E. Jansen, Burlen Loring, Zarija Lukic, Suresh Menon, Dmitriy Morozov, Patrick O'Leary, Reetesh Ranjan, Michel E. Rasquin, Christopher P. Stone, Venkatram Vishwanath, Gunther H. Weber, Brad Whitlock, Matthew Wolf, K. John Wu, E. Wes Bethel:
Performance analysis, design considerations, and applications of extreme-scale in situ infrastructures. SC 2016: 921-932 - 2015
- [j8]Eun-Sung Jung, Rajkumar Kettimuthu, Venkatram Vishwanath:
Cluster-to-cluster data transfer with data compression over wide-area networks. J. Parallel Distributed Comput. 79-80: 90-103 (2015) - [c54]Prabhat, Surendra Byna, Venkatram Vishwanath, Eli Dart, Michael F. Wehner, William D. Collins:
TECA: Petascale Pattern Recognition for Climate Science. CAIP (2) 2015: 426-436 - [c53]Sean Wallace, Venkatram Vishwanath, Susan Coghlan, Zhiling Lan, Michael E. Papka:
Comparison of Vendor Supplied Environmental Data Collection Mechanisms. CLUSTER 2015: 690-697 - [c52]Huy Bui, Robert L. Jacob, Preeti Malakar, Venkatram Vishwanath, Andrew E. Johnson, Michael E. Papka, Jason Leigh:
Multipath Load Balancing for M × N Communication Patterns on the Blue Gene/Q Supercomputer Interconnection Network. CLUSTER 2015: 833-840 - [c51]Silvio Rizzi, Mark Hereld, Joseph A. Insley, Michael E. Papka, Thomas D. Uram, Venkatram Vishwanath:
Large-Scale Parallel Visualization of Particle-Based Simulations using Point Sprites and Level-Of-Detail. EGPGV@EuroVis 2015: 1-10 - [c50]Huy Bui, Preeti Malakar, Venkatram Vishwanath, Todd S. Munson, Eun-Sung Jung, Andrew E. Johnson, Michael E. Papka, Jason Leigh:
Improving Communication Throughput by Multipath Load Balancing on Blue Gene/Q. HiPC 2015: 115-124 - [c49]Daqing Yun, Chase Qishi Wu, Nageswara S. V. Rao, Bradley W. Settlemyer, Josh Lothian, Rajkumar Kettimuthu, Venkatram Vishwanath:
Profiling transport performance for big data transfer over dedicated channels. ICNC 2015: 858-862 - [c48]Jiayuan Meng, Thomas D. Uram, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran:
Modeling Cooperative Threads to Project GPU Performance for Adaptive Parallelism. IPDPS Workshops 2015: 998-1007 - [c47]Jie Jiang, Mark Hereld, Joseph A. Insley, Michael E. Papka, Silvio Rizzi, Thomas D. Uram, Venkatram Vishwanath:
Streaming ultra high resolution images to large tiled display at nearly interactive frame rate with vl3. LDAV 2015: 133-134 - [c46]Silvio Rizzi, Mark Hereld, Joseph A. Insley, Michael E. Papka, Thomas D. Uram, Venkatram Vishwanath:
Large-scale co-visualization for LAMMPS using vl3. LDAV 2015: 141-142 - [c45]Preeti Malakar, Venkatram Vishwanath:
Route-aware independent MPI I/O on the blue gene/Q. DISCS@SC 2015: 5:1-5:8 - [c44]Preeti Malakar, Venkatram Vishwanath, Todd S. Munson, Christopher Knight, Mark Hereld, Sven Leyffer, Michael E. Papka:
Optimal scheduling of in-situ analysis for large-scale scientific simulations. SC 2015: 52:1-52:11 - 2014
- [j7]Sriram Lakshminarasimhan, Xiaocheng Zou, David A. Boyuka II, Saurabh V. Pendse, John Jenkins, Venkatram Vishwanath, Michael E. Papka, Scott Klasky, Nagiza F. Samatova:
DIRAQ: scalable in situ data- and resource-aware indexing for optimized query performance. Clust. Comput. 17(4): 1101-1119 (2014) - [j6]Katrin Heitmann, Salman Habib, Hal Finkel, Nicholas Frontiere, Adrian Pope, Vitali A. Morozov, Steve Rangel, Eve Kovacs, Juliana Kwan, Nan Li, Silvio Rizzi, Joseph A. Insley, Venkatram Vishwanath, Tom Peterka, David Daniel, Patricia K. Fasel, George Zagaris:
Large-Scale Simulations of Sky Surveys. Comput. Sci. Eng. 16(5): 14-23 (2014) - [c43]Jiayuan Meng, Xingfu Wu, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran, Valerie E. Taylor:
SKOPE: a framework for modeling and exploring workload behavior. Conf. Computing Frontiers 2014: 6:1-6:10 - [c42]Silvio Rizzi, Mark Hereld, Joseph A. Insley, Michael E. Papka, Thomas D. Uram, Venkatram Vishwanath:
Performance Modeling of vl3 Volume Rendering on GPU-Based Clusters. EGPGV@EuroVis 2014: 65-72 - [c41]Ketan Maheshwari, Eun-Sung Jung, Jiayuan Meng, Venkatram Vishwanath, Rajkumar Kettimuthu:
Improving Multisite Workflow Performance Using Model-Based Scheduling. ICPP 2014: 131-140 - [c40]Huy Bui, Jason Leigh, Eun-Sung Jung, Venkatram Vishwanath, Michael E. Papka:
Improving Data Movement Performance for Sparse Data Patterns on the Blue Gene/Q Supercomputer. ICPP Workshops 2014: 302-311 - [c39]Huy Bui, Hal Finkel, Venkatram Vishwanath, Salman Habib, Katrin Heitmann, Jason Leigh, Michael E. Papka, Kevin Harms:
Scalable Parallel I/O on a Blue Gene/Q Supercomputer Using Compression, Topology-Aware Data Aggregation, and Subfiling. PDP 2014: 107-111 - [c38]Eun-Sung Jung, Venkatram Vishwanath, Rajkumar Kettimuthu:
Distributed multipath routing algorithm for data center networks. DISCS@SC 2014: 49-56 - [c37]Sidharth Kumar, John Edwards, Peer-Timo Bremer, Aaron Knoll, Cameron Christensen, Venkatram Vishwanath, Philip H. Carns, John A. Schmidt, Valerio Pascucci:
Efficient I/O and Storage of Adaptive-Resolution Data. SC 2014: 413-423 - [c36]Sidharth Kumar, Cameron Christensen, John A. Schmidt, Peer-Timo Bremer, Eric Brugger, Venkatram Vishwanath, Philip H. Carns, Hemanth Kolla, Ray W. Grout, Jacqueline Chen, Martin Berzins, Giorgio Scorzelli, Valerio Pascucci:
Fast Multiresolution Reads of Massive Simulation Datasets. ISC 2014: 314-330 - [e3]Hank Childs, Renato Pajarola, Venkatram Vishwanath:
4th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2014, Paris, France, November 9-10, 2014. IEEE Computer Society 2014, ISBN 978-1-4799-5215-1 [contents] - 2013
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