Venkatram Vishwanath

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 89

Top Venues

CoRR

CCGRID

ISAV@SC

Publications

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)
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)
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)
François Tessier, Venkatram Vishwanath, Emmanuel Jeannot
Adding topology and memory awareness in data aggregation algorithms. Future Gener. Comput. Syst. 159 (2024)
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)
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) (2024)
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 (2023)
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani
Neural Architecture Search Benchmarks: Insights and Survey. IEEE Access 11 (2023)
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 (2023)
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)
Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization. e-Science (2023)
Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives. CoRR (2023)
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 (2023)
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 Ward, Valerie 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) (2023)
Zhen Xie, Siddhisanket Raskar, Murali Emani, Venkatram Vishwanath
TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators. Euro-Par (2023)
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 (2023)
Reet Barik, Siddhisanket Raskar, Murali Emani, Venkatram Vishwanath
Characterizing the Performance of Triangle Counting on Graphcore's IPU Architecture. SC Workshops (2023)
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)
Archit Vasan, Thomas S. Brettin, Rick Stevens, Arvind Ramanathan, Venkatram Vishwanath
Scalable Lead Prediction with Transformers using HPC resources. SC Workshops (2023)
Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani, Venkatram Vishwanath, Arun K. Somani
A Survey of Techniques for Optimizing Transformer Inference. CoRR (2023)
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 (2023)
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani
Neural Architecture Search for Transformers: A Survey. IEEE Access 10 (2022)
Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash
Asynchronous Distributed Bayesian Optimization at HPC Scale. CoRR (2022)
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) (2022)
Romit Maulik, Dimitrios Fytanidis, Bethany Lusch, Venkatram Vishwanath, Saumil Patel
PythonFOAM: In-situ data analyses with OpenFOAM and Python. J. Comput. Sci. 62 (2022)
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, Adrian Macias, Chetan Tekur, Matthew Boyd
A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads. PMBS@SC (2022)
Hariharan Devarajan, Anthony Kougkas, Huihuo Zheng, Venkatram Vishwanath, Xian-He Sun
Stimulus: Accelerate Data Management for Scientific AI applications in HPC. CCGRID (2022)
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath
Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications. CoRR (2022)
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)
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani
Efficient Design Space Exploration for Sparse Mixed Precision Neural Architectures. HPDC (2022)
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)
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)
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)
Hariharan Devarajan, Huihuo Zheng, Anthony Kougkas, Xian-He Sun, Venkatram Vishwanath
DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications. CCGRID (2021)
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)
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)
Romit Maulik, Dimitrios Fytanidis, Bethany Lusch, Venkatram Vishwanath, Saumil Patel
PythonFOAM: In-situ data analyses with OpenFOAM and Python. CoRR (2021)
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 (2021)
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) (2021)
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) (2020)
Ivana Marincic, Venkatram Vishwanath, Henry Hoffmann
SeeSAw: Optimizing Performance of In-Situ Analytics Applications under Power Constraints. IPDPS (2020)
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 (2020)
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) (2020)
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)
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 (2019)
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)
George K. Thiruvathukal, Cameron Christensen, Xiaoyong Jin, François Tessier, Venkatram Vishwanath
A Benchmarking Study to Evaluate Apache Spark on Large-Scale Supercomputers. CoRR (2019)
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)
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)
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 (2019)
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 (2019)
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)
François Tessier, Paul Gressier, Venkatram Vishwanath
Optimizing Data Aggregation by Leveraging the Deep Memory Hierarchy on Large-scale Systems. ICS (2018)
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)
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)
Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali A. Morozov, Kalyan Kumaran
Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications. PMBS@SC (2018)
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)
Ivana Marincic, Venkatram Vishwanath, Henry Hoffmann
PoLiMEr: An Energy Monitoring and Power Limiting Interface for HPC Applications. E2SC@SC (2017)
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)
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)
Preeti Malakar, Venkatram Vishwanath
Hierarchical Read-Write Optimizations for Scientific Applications with Multi-variable Structured Datasets. Int. J. Parallel Program. 45 (1) (2017)
Francois Tessier, Venkatram Vishwanath, Emmanuel Jeannot
TAPIOCA: An I/O Library for Optimized Topology-Aware Data Aggregation on Large-Scale Supercomputers. CLUSTER (2017)
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)
Preeti Malakar, Christopher Knight, Todd S. Munson, Venkatram Vishwanath, Michael E. Papka
Scalable In situ Analysis of Molecular Dynamics Simulations. ISAV@SC (2017)
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) (2017)
Preeti Malakar, Venkatram Vishwanath
Data movement optimizations for independent MPI I/O on the Blue Gene/Q. Parallel Comput. 61 (2017)
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)
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)
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)
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 (2016)
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)
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
Methods, Infrastructures, and Applications on High Performance Computing Platforms. Comput. Graph. Forum 35 (3) (2016)
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)
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)
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 (2016)
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
infrastructures. SC (2016)
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 (2016)
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)
Preeti Malakar, Venkatram Vishwanath
Route-aware independent MPI I/O on the blue gene/Q. DISCS@SC (2015)
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)
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)
Sean Wallace, Venkatram Vishwanath, Susan Coghlan, Zhiling Lan, Michael E. Papka
Comparison of Vendor Supplied Environmental Data Collection Mechanisms. CLUSTER (2015)
Eun-Sung Jung, Rajkumar Kettimuthu, Venkatram Vishwanath
Cluster-to-cluster data transfer with data compression over wide-area networks. J. Parallel Distributed Comput. (2015)
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)
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)
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)
Prabhat, Surendra Byna, Venkatram Vishwanath, Eli Dart, Michael F. Wehner, William D. Collins
TECA: Petascale Pattern Recognition for Climate Science. CAIP (2) (2015)
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)
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)
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)
Eun-Sung Jung, Venkatram Vishwanath, Rajkumar Kettimuthu
Distributed multipath routing algorithm for data center networks. DISCS@SC (2014)
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)
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)
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)
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)
Ketan Maheshwari, Eun-Sung Jung, Jiayuan Meng, Venkatram Vishwanath, Rajkumar Kettimuthu
Improving Multisite Workflow Performance Using Model-Based Scheduling. ICPP (2014)
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) (2014)
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)
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) (2014)
Sriram Lakshminarasimhan, David A. Boyuka II, Saurabh V. Pendse, Xiaocheng Zou, John Jenkins, Venkatram Vishwanath, Michael E. Papka, Nagiza F. Samatova
Scalable in situ scientific data encoding for analytical query processing. HPDC (2013)

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Venkatram Vishwanath