Estimating Computational Biases for Criticality Safety Applications with Few Neutronically Similar Benchmarks
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Perfetti, Christopher M.↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Toward Improved Control of Neutral Atom Qubits for Applications in Quantum Metrology and Computing.
Abstract not provided.
Bainbridge, Jonathan Edward↗
Uncovering Access, Reuse, and Sharing Characteristics of I/O-Intensive Files on Large-Scale Production HPC Systems
Large-scale high-performance computing (HPC) applications running on supercomputers produce large amounts of data routinely and store it in files on multi-PB shared parallel storage systems. Unfortunately, storage community has a limited understanding of the access and reuse patterns of these files. This paper investigates the access and reuse patterns of I/O-intensive files on a production-scale supercomputer.
Patel, Tirthak↗
BETO 2021 Peer Review - Electrocatalytic CO2 Utilization
The goal of the ChemCatBio DataHub project is to accelerate the catalyst and process development cycle by developing transformational tools for prediction and collaboration in catalyst R&D. The project is currently focused on the development of the Catalyst Property Database (CPD), a free and public resource released in September 2020. The CPD was designed to advance the state of the art for application of computational data. When computational data, such as computed reaction energetics, is used in catalyst design, it is almost always generated by the researchers seeking to use it, even if similar data has been published previously. One barrier to data reuse that results in this duplication of effort is the difficult process of finding and applying published data, which can be slow, error-prone, and manual. The CPD seeks to overcome these challenges by creating a centralized, searchable database of quality catalyst property data. At present, the CPD contains computed adsorption energies for intermediates along catalytic pathways. During FY21 and FY22, development of the CPD continues with a focus on external users and meeting their requirements. A batch upload capability, training and curation procedures, user interviews, and a demonstration of the CPD's utility in accelerating catalyst research are planned. Overall, the Data Hub project and CPD aim to reduce the time and cost of catalyst research by harnessing the power of data in catalyst discovery.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures
Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.
Tian, Jiannan↗
Quantum Computing and Simulations for Energy Applications
Presentation for Clearwater Clean Energy Conference
Duan, Yuhua↗
Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview
A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Qudit Gate Decomposition Dependence for Lattice Gauge Theories
In this work, we investigate the effect of decomposition basis on primitive qudit gates on superconducting radio-frequency cavity-based quantum computers with applications to lattice gauge theory. Three approaches are tested: SNAP & Displacement gates, ECD & single-qubit rotations $R(\theta,\phi)$, and optimal pulse control. For all three decompositions, implementing the necessary sequence of rotations concurrently rather then sequentially can reduce the primitive gate run time. The number of blocks required for the faster ECD &$R_p(\theta)$ is found to scale $\mathcal{O}(d^2)$, while slower SNAP & Displacement set scales at worst $\mathcal{O}(d)$. For qudits with $d<10$, the resulting gate times for the decompositions is similar, but strongly-dependent on experimental design choices. Optimal control can outperforms both decompositions for small $d$ by a factor of 2-12 at the cost of higher classical resources. Lastly, we find that SNAP & Displacement are slightly more robust to a simplified noise model.
Kürkçüoglu, Doga Murat↗
VerifyIO: Ensuring Correctness of Consistency Semantics in Parallel I/O
Abstract—High-performance computing (HPC) applications generate and consume substantial amounts of data, typically managed by parallel file systems. These applications access file systems either through the POSIX interface or by using highlevel I/O libraries. While the POSIX consistency model remains dominant in HPC, emerging file systems and popular I/O libraries increasingly adopt alternative consistency models that relax semantics in various ways, creating significant challenges for correctness and portability. This paper addresses these challenges by proposing a trace-driven I/O consistency verification workflow, implemented in our open-source tool, VerifyIO, which collects execution traces, detects data conflicts, and verifies proper synchronization against specified consistency models. Our extensive evaluation of 91 test case executions across three widely used I/O libraries with four I/O consistency models reveals critical consistency issues at both application and implementation levels.
Consistency Semantics↗
2022 American Conference on Neutron Scattering (ACNS 2022)
The 11th American Conference on Neutron Scattering (ACNS 2022) will be held on June 5-9, 2022, in Boulder, CO. The Conference will provide essential information on the breadth and depth of current neutron-related research worldwide. Hosted by the Neutron Scattering Society of America, this year’s Conference will feature a combination of invited and contributed talks, poster sessions, and tutorials. Topics of the conference are: Advances in Neutron Facilities, Instrumentation and Software: Developments in sources, instrumentation, sample environments and control software. Hard Condensed Matter: Magnetism, correlated metals, quantum/topological materials, superconductors, ferroelectrics, multiferroics, glasses, and disorder phenomena. Submissions outlining examples of neutron scattering in industrial and engineering applications involving hard condensed matter systems are also encouraged. Soft Matter: Neutron studies of soft materials and related fields including in situ and in operando studies. Polymers, surfactants, emulsions, gels, nanoparticles, colloidal suspensions and more. Submissions of computational studies or applications of machine learning beneficial to neutron scattering experiments, as well as examples of neutron scattering in industrial and engineering applications are strongly encouraged. Biology, Biophysics and Biotechnology: Neutron studies of biological and biologically relevant systems. Proteins, bio membranes, biological assemblies, natural materials, nucleic acids, drug-delivery platforms and biomedical systems. Submissions of computational studies or applications of machine learning beneficial to biological neutron scattering experiments, as well as examples of neutron scattering in applied research involving biological systems, are strongly encouraged. Materials Chemistry and Energy: Neutron-based studies of functional materials and materials for energy applications. Examples include porous materials such as metal organic frameworks (MOFs), zeolites; phosphors; novel pigments; electrolytes; catalysts; ionic conductors/cathode materials; photovoltaic materials (hybrid perovskites); thermoelectrics; magnetocalorics/electrocalorics. Structural Materials and Engineering: Neutron scattering studies of materials and engineering processes including structural materials, concrete and metals, as well as engineering processes including combustion, corrosion, additive manufacturing, and others. Neutron Physics: Fundamental physical studies of the neutron and related areas. Emerging Applications in Neutron Scattering: Machine Learning and Data Science: Advances in computing power have contributed to rapidly evolving machine learning and data science fields that can be leveraged to the benefit of the neutron scattering community. The purpose of this session is to highlight recent advances in machine learning and data science and to serve as the foundation of a parallel data and computation track highlighting computation advances and applications in neutron scattering throughout the conference.
36 MATERIALS SCIENCE↗
Enabling Co-Design of Multi-Layer Exascale Storage Architectures
Growing demands for computing power in applications such as energy production, climate analysis, computational chemistry, and bioinformatics have propelled computing systems toward the exascale: systems with 10 18 floating-point operations per second. These systems, to be designed and constructed over the next decade, will create unprecedented challenges in component counts, power consumption, resource limitations, and system complexity. Data storage and access are an increasingly important and complex component in extreme-scale computing systems, and significant design work is needed to develop successful storage hardware and software architectures at exascale. Co-design of these systems will be necessary to find the best possible design points for exascale systems. The goal of this work has been to enable the exploration and co-design of exascale storage systems by providing a detailed, accurate, and highly parallel simulation of exascale storage and the surrounding environment. Specifically, this simulation has (1) portrayed realistic application checkpointing and analysis workloads, (2) captured the complexity, scale, and multilayer nature of exascale storage hardware and software, and (3) executed in a timeframe that enables “what if'” exploration of design concepts. We developed models of the major hardware and software components in an exascale storage system, as well as the application I/O workloads that drive them. We used our simulation system to investigate critical questions in reliability and concurrency at exascale, helping guide the design of future exascale hardware and software architectures. Additionally, we provided this system to interested vendors and researchers so that others can explore the design space. We validated the capabilities of our simulation environment by configuring the simulation to represent the Argonne Leadership Computing Facility Blue Gene/Q system and comparing simulation results for application I/O patterns to the results of executions of these I/O kernels on the actual system.
97 MATHEMATICS AND COMPUTING↗
Computational methods for frictional contact with applications to the Space Shuttle orbiter nose-gear tire: Comparisons of experimental measurements and analytical predictions
A computational procedure is presented for the solution of frictional contact problems for aircraft tires. A Space Shuttle nose-gear tire is modeled using a two-dimensional laminated anisotropic shell theory which includes the effects of variations in material and geometric parameters, transverse-shear deformation, and geometric nonlinearities. Contact conditions are incorporated into the formulation by using a perturbed Lagrangian approach with the fundamental unknowns consisting of the stress resultants, the generalized displacements, and the Lagrange multipliers associated with both contact and friction conditions. The contact-friction algorithm is based on a modified Coulomb friction law. A modified two-field, mixed-variational principle is used to obtain elemental arrays. This modification consists of augmenting the functional of that principle by two terms: the Lagrange multiplier vector associated with normal and tangential node contact-load intensities and a regularization term that is quadratic in the Lagrange multiplier vector. These capabilities and computational features are incorporated into an in-house computer code. Experimental measurements were taken to define the response of the Space Shuttle nose-gear tire to inflation-pressure loads and to inflation-pressure loads combined with normal static loads against a rigid flat plate. These experimental results describe the meridional growth of the tire cross section caused by inflation loading, the static load-deflection characteristics of the tire, the geometry of the tire footprint under static loading conditions, and the normal and tangential load-intensity distributions in the tire footprint for the various static vertical-loading conditions. Numerical results were obtained for the Space Shuttle nose-gear tire subjected to inflation pressure loads and combined inflation pressure and contact loads against a rigid flat plate. The experimental measurements and the numerical results are compared.
Tanner, John A.↗
A Compact VLSI Neural Computer Integrated with Active Pixel Sensor for Real-time Machine Vision Applications
A compact VLSI neural computer integrated with an active pixel sensor has been under development to mimic what is inherent in biological visio systems.
Active↗
A characterization of workflow management systems for extreme-scale applications
We present that the automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. Finally, the paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.
97 MATHEMATICS AND COMPUTING↗
Scientific Computing Paradigm
The usage model of supercomputers for scientific applications, such as computational fluid dynamics (CFD), has changed over the years. Scientific visualization has moved scientists away from looking at numbers to looking at three-dimensional images, which capture the meaning of the data. This change has impacted the system models for computing. This report details the model which is used by scientists at NASA's research centers.
VanZandt, John↗
Interfacing Computer Aided Parallelization and Performance Analysis
When porting sequential applications to parallel computer architectures, the program developer will typically go through several cycles of source code optimization and performance analysis. We have started a project to develop an environment where the user can jointly navigate through program structure and performance data information in order to make efficient optimization decisions. In a prototype implementation we have interfaced the CAPO computer aided parallelization tool with the Paraver performance analysis tool. We describe both tools and their interface and give an example for how the interface helps within the program development cycle of a benchmark code.
Jost, Gabriele↗
Benchmarking numerical methods for impact and cratering applications
Large scale computational models are important for studying impact cratering events that are prevalent both on Earth and, more broadly, in this solar system. To address these problems, models must reliably account for both large length scales (e.g., kilometers) and relatively long time scales (hundreds of seconds). This work benchmarks two such approaches, a more traditional hydrodynamics approach and a finite-discrete element method (FDEM), for impact cratering applications. Both 2D and 3D results are discussed for two different impact velocities, 5 km/s and 20 km/s, striking normal to the target and, for 3D simulations, 45° from vertical. In addition, comparisons to previously published data are presented. Finally, differences in how these methods model damage are discussed. Ultimately, both approaches show successful modeling of several different impact scenarios.
58 GEOSCIENCES↗
Educational NASA Computational and Scientific Studies (enCOMPASS)
Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.
Memarsadeghi, Nargess↗