CRADL: Proxy Application for Concurrent Relaxation through Accelerated Deep Learning
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Computer science↗
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Essay: Quantum Sensing with Atomic, Molecular, and Optical Platforms for Fundamental Physics
Atomic, molecular, and optical (AMO) physics has been at the forefront of the development of quantum science while laying the foundation for modern technology. With the growing capabilities of quantum control of many atoms for engineered many-body states and quantum entanglement, a key question emerges: what critical impact will the second quantum revolution with ubiquitous applications of entanglement bring to bear on fundamental physics? In this Essay, we argue that a compelling long-term vision for fundamental physics and novel applications is to harness the rapid development of quantum information science to define and advance the frontiers of measurement physics, with strong potential for fundamental discoveries. As quantum technologies, such as fault-tolerant quantum computing and entangled quantum sensor networks, become much more advanced than today’s realization, we wonder what doors of basic science can these tools unlock. We anticipate that some of the most intriguing and challenging problems, such as quantum aspects of gravity, fundamental symmetries, or new physics beyond the minimal standard model, will be tackled at the emerging quantum measurement frontier. . Published by the American Physical Society 2024
Physics↗
Climbing the Summit and Pushing the Frontier of Mixed Precision Benchmarks at Extreme Scale
The rise of machine learning (ML) applications and their use of mixed precision to perform interesting science are driving forces behind AI for science on HPC. The convergence of ML and HPC with mixed precision offers the possibility of transformational changes in computational science. The HPL-AI benchmark is designed to measure the performance of mixed precision arithmetic as opposed to the HPL benchmark which measures double precision performance. Pushing the limits of systems at extreme scale is nontrivial -little public literature explores optimization of mixed precision computations at this scale. In this work, we demonstrate how to scale up the HPL-AI benchmark on the pre-exascale Summit and exascale Frontier systems at the Oak Ridge Leadership Computing Facility (OLCF) with a cross-platform design. We present the implementation, performance results, and a guideline of optimization strategies employed for delivering portable performance on both AMD and NVIDIA GPUs at extreme scale.
Lu, Hao↗
Designing an Optimal Sensor Network via Minimizing Information Loss
Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.
54 ENVIRONMENTAL SCIENCES↗
Parallel Simulation of Beam Dynamics in Particle Accelerators [Slides]
Particle accelerators are among the most versatile and important tools of scientific discovery. The Nation's accelerators are responsible for a wealth of advances in materials science, chemistry, the biosciences, particle physics, and nuclear physics. They also have important applications to national security, the environment, energy, medicine, and on the quality of people's lives. LANL has a long history of making pioneering contributions to Accelerator Science including key contributions to the field of Computational Accelerator Physics. These include the development of early beam dynamics codes with space charge (such as PARMILA and PARMELA), the development of rf cavity codes and magnet codes (including Poisson and Superfish), and the development and distribution of codes to the accelerator community through the Los Alamos Accelerator Code Group. LANL researchers also helped pioneer the development of massively parallel space-charge codes. In project t22_accelsim we have moved beyond electrostatic models of collective effects (i.e., solving the Poisson equation in the bunch frame) to fully electromagnetic models based on the Lienard-Wiechert formalism. This approach enables the large-scale simulation of radiation production and collective effects in high brightness electron beams. This is highly relevant to LANL given its future goal of developing an X-ray Free Electron Laser (XFEL). It also directly impacts a LANL LDRD project to develop an undulator-based non-invasive beam profile monitor for beams created in laser-plasma accelerator systems.
43 PARTICLE ACCELERATORS↗
Using naturally occurring polysaccharides to align molecules with nonlinear optical activity
The Biophysics and Advanced Materials Branch of the Microgravity Science and Applications Division at Marshall Space Flight Center has been investigating polymers with the potential for nonlinear optical (NLO) applications for a number of years. Some of the potential applications for NLO materials include optical communications, computing, and switching. To this point the branch's research has involved polydiacetylenes, phthalocyanins, and other synthetic polymers which have inherent NLO properties. The aim of the present research is to investigate the possibility of using naturally occurring polymers such as polysaccharides or proteins to trap and align small organic molecules with useful NLO properties. Ordering molecules with NLO properties enhances 3rd order nonlinear effects and is required for 2nd order nonlinear effects. Potential advantages of such a system are the flexibility to use different small molecules with varying chemical and optical properties, the stability and cost of the polymers, and the ability to form thin, optically transparent films. Since the quality of any polymer films depends on optimizing ordering and minimizing defects, this work is particularly well suited for microgravity experiments. Polysaccharide and protein polymers form microscopic crystallites which must align to form ordered arrays. The ordered association of crystallites is disrupted by gravity effects and NASA research on protein crystal growth has demonstrated that low gravity conditions can improve crystal quality.
Prasthofer, Thomas↗
Unmanned Spacecraft of the United States
In 1957 the first earth satellite ushered in the age of space flight. Since that historic event, space exploration has become a major national objective of both the United States and the Soviet Union. These two nations have attempted a total of well over 200 space flight missions. Other nations are also participating in various degrees in what will continue to grow as a cooperative world effort. In the years since 1957, man has successfully flown in earth orbit. He has initiated programs to land on the moon and return. He has made dramatic applications of earth satellites in meteorology, communications, navigation, and geodesy. A host of scientific satellites.continue to advance understanding of the earth's environment, the sun, and the stars. Automated spacecraft are being flown to the moon, deep into interplanetary space, and to the near planets, Mars and Venus. One of the most exciting technological aspects of space exploration has been the development of automated spacecraft. Most of the scientific exploration of space and the useful applications of space flight thus far have been made possible by automated spacecraft. Development of these spacecraft and their many complex subsystems is setting the pace today for many branches of science and technology. Guidance, computer, attitude control, power, telecommunication, instrumentation, and structural subsystems are being subjected to new standards of light weight, high efficiency, extreme accuracy, and unsurpassed reliability and quality. This publication reviews the automated spacecraft which have been developed and flown, or which are under active development in the United States by the National Aeronautics and Space Administration. From the facts and statistics contained herein, certain observations can be made and certain conclusions drawn.
Cortright, Edgar M.↗
Guided Design of Alloys, Strengthened via Precipitation
Using theoretical and computational guidance, we are designing stronger middle and high-entropy alloys and superalloys for the airspace applications. We focus on improving mechanical properties of materials at cryogenic and elevated temperatures. Here we discuss basic science and fundamental theory that are being used for guided design of next-generation alloys. As examples, we consider the medium-entropy NiCoCr alloy and precipitated superalloys with a local phase transformation strengthening. This research is funded by NASA’s Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.
Alloys↗
Physics guided machine learning using simplified theories
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.
42 ENGINEERING↗
MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning
Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.
97 MATHEMATICS AND COMPUTING↗
Sierra Center of Excellence: Lessons Learned
The introduction of heterogeneous computing via GPUs from the Sierra architecture represented a significant shift in direction for computational science at Lawrence Livermore National Laboratory (LLNL), and therefore required significant preparation. Over the last five years, the Sierra Center of Excellence (CoE) has brought employees with specific expertise from IBM and NVIDIA together with LLNL in a concentrated effort to prepare applications, system software, and tools for the Sierra supercomputer. This article shares the process we applied for the CoE and documents lessons learned during the collaboration, with the hope that others will be able to learn from both our success and intermediate setbacks. We describe what we have found to work for the management of such a collaboration and best practices for algorithms and source code, system configuration and software stack, tools, and application performance.
97 MATHEMATICS AND COMPUTING↗
Low Precision for Lower Energy Consumption: Preprint
Low-precision numeric types offer significant efficiency and energy benefits for computing applications. Mixed-precision algorithms, combining low and high precision types, maintain accuracy while improving performance. Despite advantages, there exist challenges on adapting existing mixed-precision algorithms to new technologies, such as new hardware architectures and new low-precision data types. This paper presents current challenges and opportunities to advance science in this domain targetting more energy efficient solutions.
energy efficiency↗
Science-based Acceleration of the Full Value Stream for Metal Additive Manufacturing: Expedited Powder Development in the Area of Aluminum Powder Alloys and their End Use (Final Report)
The overall Science-based Acceleration of the Full Value Stream for Metal Additive Manufacturing (AM): Expedited Powder Development (“X-P4AM”) project objective is to drastically reduce the time-to-market barriers for new additive alloys of interest in automotive and aerospace applications, through computational alloy design with rapid screening and down- selection via synthesis of candidate alloys with rapid solidification. The project will also refine the technology in high pressure gas atomization to improve the production of commercial quantities of selected powders with high powder yields and enhanced powder quality. A new aluminum (Al) alloy based on high entropy composition, enhanced powder production, and optimized AM build parameterization provided a critical step in widespread adoption of AM technology for automotive applications, in this case. The individual backgrounds and capabilities of the Parties are ideally suited to the successful execution of this work. The included work enhanced the Contractors’ AM capabilities, a core competency of the Contractors, and develop a close working relationship with the Participant in the area of aluminum powder alloys and their end use.
36 MATERIALS SCIENCE↗
Science-based Acceleration of the Full Value Stream for Metal Additive Manufacturing (AM): Expedited Powder Development and Additive Manufacturing Deployment in the Area of Aluminum Powder Alloys
The overall Science-based Acceleration of the Full Value Stream for Metal Additive Manufacturing (AM): Expedited Powder Development (“X-P4AM”) project objective is to drastically reduce the time-to-market barriers for new additive alloys of interest in automotive and aerospace applications, through computational alloy design with rapid screening and down- selection via synthesis of candidate alloys with rapid solidification. The project will also refine the technology in high pressure gas atomization to improve the production of commercial quantities of selected powders with high powder yields and enhanced powder quality. A new aluminum (Al) alloy based on high entropy composition, enhanced powder production, and optimized AM build parameterization provided a critical step in widespread adoption of AM technology for automotive applications, in this case. The individual backgrounds and capabilities of the Parties are ideally suited to the successful execution of this work. The included work enhanced the Contractors’ AM capabilities, a core competency of the Contractors, and develop a close working relationship with the Participant in the area of aluminum powder alloys and their end use.
36 MATERIALS SCIENCE↗
The Stability of Powered Flight around Asteroids with Application to Vesta
The reliability of low-thrust trajectories between science orbits around large asteroids must be evaluated subject to the unavoidable uncertainties of orbit determination, asteroid physical parameters, momentum de-saturation maneuvers, and transfer maneuver execution error. This paper presents a computationally inexpensive way to extend the concept or orbital stability to trajectories undergoing continuously powered low-thrust flight. Trajectories that are stable using this measure are shown to be stable under the combined uncertainties expected during operations. The measure is general and relatively simple to implement. The method was applied to maneuvers planed around the asteroid Vesta in support of NASA's Dawn Discovery mission.
Dawn↗
Image Segmentation Analysis for NASA Earth Science Applications
NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.
Tilton, James C.↗
Software Deployment Process at NERSC: Deploying the Extreme-scale Scientific Software Stack (E4S) Using Spack at the National Energy Research Scientific Computing Center (NERSC)
One of the many benefits of using a high-performance computing (HPC) system at a Department of Energy (DOE) Office of Science (SC) HPC facility is the large number of software products, built and optimized for the system. The HPC center staff and HPC vendors provide optimized software such as libraries and even full scientific applications, ready to be used by users as building blocks to accelerate scientific discovery. Behind each provided packaged software module are a large number of decisions - which compiler, optimizations, variants/options - to build the software on the target system. And, even before the software gets deployed, the software must be developed, tested, and maintained, including deprecating old versions and ensuring compatibility across versions. The software lifecycle is complex and is further convoluted by a web of interdependencies on other software.
97 MATHEMATICS AND COMPUTING↗
Flightspeed Integral Image Analysis Toolkit
The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles
Thompson, David R.↗