Search Results - "Micikevicius, Paulius"

MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance by Mattson, Peter, Reddi, Vijay Janapa, Cheng, Christine, Coleman, Cody, Diamos, Greg, Kanter, David, Micikevicius, Paulius, Patterson, David, Schmuelling, Guenther, Tang, Hanlin, Wei, Gu-Yeon, Wu, Carole-Jean

“…In this article, we describe the design choices behind MLPerf, a machine learning performance benchmark that has become an industry standard. The first two…”
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MLPerf Inference Benchmark by Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, Zhou, Yuchen

“…Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded…”
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Visibility-Based Forest Walk-Through Using Inertial Level of Detail Models by Micikevicius, Paulius, Hughes, Charles E.

“…Modern simulation and training applications often require visualization of large scenes with many complex objects. However, this places prohibitive…”
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Applying mixed reality to entertainment by Stapleton, C., Hughes, C., Moshell, M., Micikevicius, P., Altman, M.

“…The lack of compelling content has relegated many promising entertainment technologies to laboratory curiosities. Although mixed-reality techniques show great…”
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Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation by Wu, Hao, Judd, Patrick, Zhang, Xiaojie, Isaev, Mikhail, Micikevicius, Paulius

“…Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer…”
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Scaling the power wall: a path to exascale by Villa, Oreste, Johnson, Daniel R., O'Connor, Mike, Bolotin, Evgeny, Nellans, David, Luitjens, Justin, Sakharnykh, Nikolai, Wang, Peng, Micikevicius, Paulius, Scudiero, Anthony, Keckler, Stephen W., Dally, William J.

“…Modern scientific discovery is driven by an insatiable demand for computing performance. The HPC community is targeting development of supercomputers able to…”
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Cluster computing for determining three-dimensional protein structure by MICIKEVICIUS, Paulius, DEO, Narsingh

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Accelerating Sparse Deep Neural Networks by Mishra, Asit, Latorre, Jorge Albericio, Pool, Jeff, Stosic, Darko, Stosic, Dusan, Venkatesh, Ganesh, Yu, Chong, Micikevicius, Paulius

“…As neural network model sizes have dramatically increased, so has the interest in various techniques to reduce their parameter counts and accelerate their…”
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FP8 Formats for Deep Learning by Micikevicius, Paulius, Stosic, Dusan, Burgess, Neil, Cornea, Marius, Dubey, Pradeep, Grisenthwaite, Richard, Ha, Sangwon, Heinecke, Alexander, Judd, Patrick, Kamalu, John, Mellempudi, Naveen, Oberman, Stuart, Shoeybi, Mohammad, Siu, Michael, Wu, Hao

“…FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose…”
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Parallel graph algorithms for molecular conformation and tree codes by Micikevicius, Paulius

“…Parallel graph algorithms for molecular conformation and tree encoding problems are proposed in this dissertation. A coarse-grained parallel algorithm for…”
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Microscaling Data Formats for Deep Learning by Rouhani, Bita Darvish, Zhao, Ritchie, More, Ankit, Hall, Mathew, Khodamoradi, Alireza, Deng, Summer, Choudhary, Dhruv, Cornea, Marius, Dellinger, Eric, Denolf, Kristof, Dusan, Stosic, Elango, Venmugil, Golub, Maximilian, Heinecke, Alexander, James-Roxby, Phil, Jani, Dharmesh, Kolhe, Gaurav, Langhammer, Martin, Li, Ada, Melnick, Levi, Mesmakhosroshahi, Maral, Rodriguez, Andres, Schulte, Michael, Shafipour, Rasoul, Shao, Lei, Siu, Michael, Dubey, Pradeep, Micikevicius, Paulius, Naumov, Maxim, Verrilli, Colin, Wittig, Ralph, Burger, Doug, Chung, Eric

“…Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling…”
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Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq by Kuchaiev, Oleksii, Ginsburg, Boris, Gitman, Igor, Lavrukhin, Vitaly, Li, Jason, Nguyen, Huyen, Case, Carl, Micikevicius, Paulius

“…We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training. Benchmarks…”
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Parallel graph algorithms for molecular conformation and tree codes by Micikevicius, Paulius

“…Parallel graph algorithms for molecular conformation and tree encoding problems are proposed in this dissertation. A coarse-grained parallel algorithm for…”
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Mixed Precision Training by Micikevicius, Paulius, Narang, Sharan, Alben, Jonah, Diamos, Gregory, Elsen, Erich, Garcia, David, Ginsburg, Boris, Houston, Michael, Kuchaiev, Oleksii, Venkatesh, Ganesh, Wu, Hao

“…Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As…”
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MLPerf Training Benchmark by Mattson, Peter, Cheng, Christine, Coleman, Cody, Diamos, Greg, Micikevicius, Paulius, Patterson, David, Tang, Hanlin, Wei, Gu-Yeon, Bailis, Peter, Bittorf, Victor, Brooks, David, Chen, Dehao, Dutta, Debojyoti, Gupta, Udit, Hazelwood, Kim, Hock, Andrew, Huang, Xinyuan, Ike, Atsushi, Jia, Bill, Kang, Daniel, Kanter, David, Kumar, Naveen, Liao, Jeffery, Ma, Guokai, Narayanan, Deepak, Oguntebi, Tayo, Pekhimenko, Gennady, Pentecost, Lillian, Reddi, Vijay Janapa, Robie, Taylor, John, Tom St, Tabaru, Tsuguchika, Wu, Carole-Jean, Xu, Lingjie, Yamazaki, Masafumi, Young, Cliff, Zaharia, Matei

“…Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware…”
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MLPerf Inference Benchmark by Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B, Jiao, Jeff, John, Tom St, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, Zhou, Yuchen

“…Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded…”
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Exploring Topological Properties of NMR Graphs by Micikevicius, P., Deo, N.

“…In this paper we explore the topological properties of graphs derived from nuclear magnetic resonance (NMR) restraint data. Only the distance bound data is…”
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