Brian Van Essen
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View article: BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models Open
Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules requires accurate out-of-distribution (OOD) predictions, b…
View article: Lion Cub: Minimizing Communication Overhead in Distributed Lion
Lion Cub: Minimizing Communication Overhead in Distributed Lion Open
Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet interconnects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techni…
View article: HPC Center of the Future: R&D Acquisition Intent
HPC Center of the Future: R&D Acquisition Intent Open
This document contains intended technical requirements for an anticipated future procurement for Lawrence Livermore National Laboratory (LLNL), hereafter referred to as “LLNL” or “the Laboratory”, which seeks to fund a few new and innovati…
View article: Lion Cub: Minimizing Communication Overhead in Distributed Lion
Lion Cub: Minimizing Communication Overhead in Distributed Lion Open
Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet intercon nects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techn…
View article: Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets
Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets Open
We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid…
View article: Advanced Research Directions on AI for Energy
Advanced Research Directions on AI for Energy Open
This AI for Energy report further details grand challenges that provide significant opportunities for energy applications across nuclear energy, the power grid, carbon management, energy storage, and energy materials over the next decade. …
View article: 2022 Review of Data-Driven Plasma Science
2022 Review of Data-Driven Plasma Science Open
Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma scie…
View article: Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops
Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops Open
This is a report about a series of workshops sponsored by the Department of Energy (DOE) to gather input on new and rapidly emerging opportunities and challenges of scientific AI. The members of the workshops believes that AI can have a fo…
View article: Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure
Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure Open
Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS pr…
View article: A flexible proton beam imaging energy spectrometer (PROBIES) for high repetition rate or single-shot high energy density (HED) experiments (invited)
A flexible proton beam imaging energy spectrometer (PROBIES) for high repetition rate or single-shot high energy density (HED) experiments (invited) Open
The PROBIES diagnostic is a new, highly flexible, imaging and energy spectrometer designed for laser-accelerated protons. The diagnostic can detect low-mode spatial variations in the proton beam profile while resolving multiple energies on…
View article: Scalable Composition and Analysis Techniques for Massive Scientific Workflows
Scalable Composition and Analysis Techniques for Massive Scientific Workflows Open
Composite science workflows are gaining traction to manage the combined effects of (1) extreme hardware heterogeneity in new High Performance Computing (HPC) systems and (2) growing software complexity – effects necessitated by the converg…
View article: 2022 Review of Data-Driven Plasma Science
2022 Review of Data-Driven Plasma Science Open
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and m…
View article: ExaLearn: Co-Design Center for Exascale Machine Learning Technologies.
ExaLearn: Co-Design Center for Exascale Machine Learning Technologies. Open
appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide ex…
View article: Is Disaggregation possible for HPC Cognitive Simulation?
Is Disaggregation possible for HPC Cognitive Simulation? Open
Cognitive simulation (CogSim) is an important and emerging workflow for HPC scientific exploration and scientific machine learning (SciML). One challenging workload for CogSim is the replacement of one component in a complex physical simul…
View article: Co-design Center for Exascale Machine Learning Technologies (ExaLearn)
Co-design Center for Exascale Machine Learning Technologies (ExaLearn) Open
Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition t…
View article: Accelerating the rate of discovery: toward high-repetition-rate HED science
Accelerating the rate of discovery: toward high-repetition-rate HED science Open
As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 Hz) come online around the world, the high energy density (HED) science they enable will experience a radical paradigm shift. The >10 3 increase in sh…
View article: Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models
Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models Open
We improved the quality and reduced the time to produce machine learned models for use in small molecule antiviral design. Our globally asynchronous multi-level parallel training approach strong scales to all of Sierra with up to 97.7% eff…
View article: ExaLearn: Co-Design Center for Exascale Machine Learning Technologies.
ExaLearn: Co-Design Center for Exascale Machine Learning Technologies. Open
appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide ex…
View article: ExaLearn: Co-Design Center for Exascale Machine Learning Technologies.
ExaLearn: Co-Design Center for Exascale Machine Learning Technologies. Open
appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide ex…
View article: The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism Open
Here, we present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can …
View article: Machine Learning-driven Multiscale Modeling Reveals Lipid-Dependent Dynamics of RAS Signaling Proteins
Machine Learning-driven Multiscale Modeling Reveals Lipid-Dependent Dynamics of RAS Signaling Proteins Open
RAS is a signaling protein associated with the cell membrane that is mutated in 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires ne…
View article: The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism Open
We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make t…
View article: DiHydrogen
DiHydrogen Open
DiHydrogen is the second version of the Hydrogen fork of the well-known distributed linear algebra library, Elemental. DiHydrogen is a GPU-accelerated distributed multilinear algebra interface with a particular emphasis on the needs of the…
View article: Merlin: Enabling Machine Learning-Ready HPC Ensembles
Merlin: Enabling Machine Learning-Ready HPC Ensembles Open
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows…
View article: Preparation and optimization of a diverse workload for a large-scale heterogeneous system
Preparation and optimization of a diverse workload for a large-scale heterogeneous system Open
Productivity from day one on supercomputers that leverage new technologies requires significant preparation. An institution that procures a novel system architecture often lacks sufficient institutional knowledge and skills to prepare for …
View article: Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets Open
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 initialize the process. We present a novel tournament method to train tradition…
View article: Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism Open
Scaling CNN training is necessary to keep up with growing datasets and reduce training time. We also see an emerging need to handle datasets with very large samples, where memory requirements for training are large. Existing training frame…