Search Results - "Jacobs, Sam"

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications by Liu, Shusen, Gaffney, Jim, Peterson, Luc, Robinson, Peter B., Bhatia, Harsh, Pascucci, Valerio, Spears, Brian K., Bremer, Peer-Timo, Wang, Di, Maljovec, Dan, Anirudh, Rushil, Thiagarajan, Jayaraman J., Jacobs, Sam Ade, Van Essen, Brian C., Hysom, David, Yeom, Jae-Seung

“…With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in…”
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Mental Health among Firefighters: Understanding the Mental Health Risks, Treatment Barriers, and Coping Strategies by DeMoulin, Doug, Jacobs, Sam, Nam, Yoon-Sung, Harding, Alyson B., Moskowitz, Adam F., Shi, Yihe, Kim, Hyun

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Enabling machine learning-ready HPC ensembles with Merlin by Peterson, J. Luc, Bay, Ben, Koning, Joe, Robinson, Peter, Semler, Jessica, White, Jeremy, Anirudh, Rushil, Athey, Kevin, Bremer, Peer-Timo, Di Natale, Francesco, Fox, David, Gaffney, Jim A., Jacobs, Sam A., Kailkhura, Bhavya, Kustowski, Bogdan, Langer, Steven, Spears, Brian, Thiagarajan, Jayaraman, Van Essen, Brian, Yeom, Jae-Seung

“…With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to…”
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Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models by Jacobs, Sam Ade, Moon, Tim, McLoughlin, Kevin, Jones, Derek, Hysom, David, Ahn, Dong H, Gyllenhaal, John, Watson, Pythagoras, Lightstone, Felice C, Allen, Jonathan E, Karlin, Ian, Van Essen, Brian

“…We improved the quality and reduced the time to produce machine learned models for use in small molecule antiviral design. Our globally asynchronous…”
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Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications by Liu, Shusen, Gaffney, Jim, Peterson, Luc, Robinson, Peter B., Bhatia, Harsh, Pascucci, Valerio, Spears, Brian K., Bremer, Peer-Timo, Wang, Di, Maljovec, Dan, Anirudh, Rushil, Thiagarajan, Jayaraman J., Jacobs, Sam Ade, Van Essen, Brian C., Hysom, David, Yeom, Jae-Seung

“…With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in…”
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Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism by Ekenna, Chinwe, Jacobs, Sam Ade, Thomas, Shawna, Amato, Nancy M.

“…Probabilistic Roadmap Methods (PRMs) are widely used motion planning methods that sample robot configurations (nodes) and connect them to form a graph…”
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The anatomy of a distributed motion planning roadmap by Jacobs, Sam Ade, Amato, Nancy M.

“…In this paper, we evaluate and compare the quality and structure of roadmaps constructed from parallelizing sampling-based motion planning algorithms against…”
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Parallelizing Training of Deep Generative Models on Massive Scientific Datasets by Jacobs, Sam Ade, Gaffney, Jim, Benson, Tom, Robinson, Peter, Peterson, Luc, Spears, Brian, Van Essen, Brian, Hysom, David, Yeom, Jae-Seung, Moon, Tim, Anirudh, Rushil, Thiagaranjan, Jayaraman J., Liu, Shusen, Bremer, Peer-Timo

“…Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to…”
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On the weakly C-H···π hydrogen bonded complexes of sevoflurane and benzene by Dom, Johan J J, van der Veken, Benjamin J, Michielsen, Bart, Jacobs, Sam, Xue, Zhifeng, Hesse, Susanne, Loritz, Hans-Martin, Suhm, Martin A, Herrebout, Wouter A

“…A vibrational assignment of the anaesthetic sevoflurane, (CF(3))(2)CHOCH(2)F, is proposed and its interaction with the aromatic model compound benzene is…”
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Blind RRT: A probabilistically complete distributed RRT by Rodriguez, Cesar, Denny, Jory, Jacobs, Sam Ade, Thomas, Shawna, Amato, Nancy M.

“…Rapidly-Exploring Random Trees (RRTs) have been successful at finding feasible solutions for many types of problems. With motion planning becoming more…”
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Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Carmichael, Zachariah, Moon, Tim, Jacobs, Sam Ade

“…Monumental advances in deep learning have led to unprecedented achievements across various domains. While the performance of deep neural networks is…”
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Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer by Yao, Jinghan, Jacobs, Sam Ade, Tanaka, Masahiro, Ruwase, Olatunji, Shafi, Aamir, Subramoni, Hari, Panda, Dhabaleswar K

“…Large Language Models (LLMs) with long context capabilities are integral to complex tasks in natural language processing and computational biology, such as…”
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Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training by Lian, Xinyu, Jacobs, Sam Ade, Kurilenko, Lev, Tanaka, Masahiro, Bekman, Stas, Ruwase, Olatunji, Zhang, Minjia

“…Existing checkpointing approaches seem ill-suited for distributed training even though hardware limitations make model parallelism, i.e., sharding model state…”
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Large-Scale Industrial Alarm Reduction and Critical Events Mining Using Graph Analytics on Spark by Jacobs, Sam Ade, Dagnino, Aldo

“…In current industrial practice, thousands of industrial alarms generating millions of alarm events, are built into digital control systems typically found in…”
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DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models by Jacobs, Sam Ade, Tanaka, Masahiro, Zhang, Chengming, Zhang, Minjia, Song, Shuaiwen Leon, Rajbhandari, Samyam, He, Yuxiong

“…Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence…”
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System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models by Jacobs, Sam Ade, Tanaka, Masahiro, Zhang, Chengming, Zhang, Minjia, Aminabadi, Reza Yazdani, Song, Shuaiwen Leon, Rajbhandari, Samyam, He, Yuxiong

“…Long sequences are ubiquitous in NLP tasks such as document summarization, machine translation, and dialogue modeling [1]-[9]. Traditional approaches to…”
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Local randomization in neighbor selection improves PRM roadmap quality by McMahon, T., Jacobs, S., Boyd, B., Tapia, L., Amato, N. M.

“…Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations…”
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ZeRO++: Extremely Efficient Collective Communication for Giant Model Training by Wang, Guanhua, Qin, Heyang, Jacobs, Sam Ade, Holmes, Connor, Rajbhandari, Samyam, Ruwase, Olatunji, Yan, Feng, Yang, Lei, He, Yuxiong

“…Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and…”
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Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks by Zaman, Shehtab, Moon, Tim, Benson, Tom, Jacobs, Sam Ade, Chiu, Kenneth, Van Essen, Brian

“…Graph neural networks (GNNs) are a powerful approach for machine learning on graph datasets. Such datasets often consist of millions of modestly-sized graphs,…”
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SUPER: SUb-Graph Parallelism for TransformERs by Jain, Arpan, Moon, Tim, Benson, Tom, Subramoni, Hari, Jacobs, Sam Ade, Panda, Dhabaleswar K, Essen, Brian Van

“…Transformer models have revolutionized the field of Natural Language Processing (NLP) and they achieve state-of-the-art performance in applications like…”
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