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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗
DOE OSTI2017

Improvement in the Accuracy of Flux Measurement of Radio Sources by Exploiting an Arithmetic Pattern in Photon Bunching Noise

A hierarchy of statistics of increasing sophistication and accuracy is proposed to exploit an interesting and fundamental arithmetic structure in the photon bunching noise of incoherent light of large photon occupation number, with the purpose of suppressing the noise and rendering a more reliable and unbiased measurement of the light intensity. The method does not require any new hardware, rather it operates at the software level with the help of high-precision computers to reprocess the intensity time series of the incident light to create a new series with smaller bunching noise coherence length. The ultimate accuracy improvement of this method of flux measurement is limited by the timing resolution of the detector and the photon occupation number of the beam (the higher the photon number the better the performance). The principal application is accuracy improvement in the signal-limited bolometric flux measurement of a radio source.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

CMOS-Based Single-Cycle in-Memory XOR/XNOR

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2× improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

SUNDIALS time integrators for exascale applications with many independent systems of ordinary differential equations

Many complex systems can be accurately modeled as a set of coupled time-dependent partial differential equations (PDEs). However, solving such equations can be prohibitively expensive, easily taxing the world’s largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This operator splitting approach is used ubiquitously across scientific domains, and in many cases leads to a set of ordinary differential equations (ODEs) that need to be solved as part of a larger “outer-loop” time-stepping approach. The SUNDIALS library provides a plethora of robust time integration algorithms for solving ODEs, and the U.S. Department of Energy Exascale Computing Project (ECP) has supported its extension to applications on exascale-capable computing hardware. In this paper, we highlight some SUNDIALS capabilities and its deployment in combustion and cosmology application codes (Pele and Nyx, respectively) where operator splitting gives rise to numerous, small ODE systems that must be solved concurrently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Innovative Natural Gas Technologies For Efficiency Gain In Reliable And Affordable Thermochemical Electricity Generation (INTEGRATE)

Hyper is a cutting-edge system that pairs computational models that simulate the response of different systems with physical components such as sensors and actuators to transfer said calculated response to the HyPer facility. Computational models must operate in real-time in order to resolve the system response when coupled to physical hardware. Real-time is defined by the minimal HyPer system response time of Δt = 0.080 s. The integration of these models serves as a foundation for performance characterization based on real-world SOFC hardware and paves the way for comprehensive fuel flexibility studies. This report underscores the innovative approach to cyber-physical system deployment at NETL’s HyPer Facility, highlighting the critical role of computational models in enhancing the performance and adaptability of energy technologies.

03 NATURAL GAS↗
DOE OSTI2025

Generic Discretization Library

The GenDiL library is a collection of C++ software abstractions designed to discretize and solve partial differential equations (PDEs) for high-performance computing (HPC) applications. Its primary focus is on modern C++ generic programming, which helps ensure portability across various hardware architectures. The central idea behind the library is to provide building blocks for numerical algorithms-such as discretization methods and iteration patterns-so that domain experts can focus on the math, rather than the low-level details of hardware or implementation. By defining abstractions for data types, iteration over computational grids, and scheduling of operations, the library isolates the high-level PDE algorithms from the platform-specific optimizations needed to achieve efficient performance.

Dudouit, Yohann [Lawrence Livermore National Labor↗
DOE OSTI2026

A Benchmark Suite for Evaluating Scientific AI Workloads on GPUs

AI applications have been steadily increasing in the allocation portfolio among leadership computing facilities. These applications depend on deep learning frameworks with hardware acceleration and underlying software systems. With the rapid development of applications, software stacks, and hardware devices, it is essential to evaluate the performance of core operations in AI workloads for direction of optimizations and procurement of next-generation high-performance computing (HPC) infrastructures. Currently, most benchmarks lack scientific AI workloads. So, we present DeepKernelBench and the experimental results of evaluating the benchmark suite for early observations and performance comparisons on datacenter GPUs using representative workloads for scientific AI, including Attentions, General matrix multiplications, Geometrics and Fourier neural operations.

Jin, Zheming [Advanced Micro Devices (AMD)]↗
DOE OSTI2025

Designing FAIR Workflows at OLCF: Building Scalable and Reusable Ecosystems for HPC Science

High Performance Computing (HPC) centers, such as the Oak Ridge Leadership Computing Facility (OLCF), provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with unique hardware configurations, specialized software environments, and elevated security re quirements that differ substantially from what most users encounter on their local systems. As a result, users often develop customized digital artifacts that are tightly coupled to the specific configuration of a given HPC center. Although necessary, this practice can lead to significant duplication of effort as multiple users independently create similar solutions to common problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Generating clock signals for a cycle accurate, cycle reproducible FPGA based hardware accelerator

A method, system and computer program product are disclosed for generating clock signals for a cycle accurate FPGA based hardware accelerator used to simulate operations of a device-under-test (DUT). In one embodiment, the DUT includes multiple device clocks generating multiple device clock signals at multiple frequencies and at a defined frequency ratio; and the FPG hardware accelerator includes multiple accelerator clocks generating multiple accelerator clock signals to operate the FPGA hardware accelerator to simulate the operations of the DUT. In one embodiment, operations of the DUT are mapped to the FPGA hardware accelerator, and the accelerator clock signals are generated at multiple frequencies and at the defined frequency ratio of the frequencies of the multiple device clocks, to maintain cycle accuracy between the DUT and the FPGA hardware accelerator. In an embodiment, the FPGA hardware accelerator may be used to control the frequencies of the multiple device clocks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Glovebox Handling of High-Consequence Materials with Super Baxter and Gesture-Based Programming - 18598

The handling of high-consequence materials is a difficult task requiring safe object manipulation while avoiding the risk of contamination or spillage. Specific operations including opening containers, segregating the wastes, and repackaging are required to be safely executed inside contained spaces such as gloveboxes. However, the workers' ability and dexterity to manipulate objects through thick protective gloves are compromised in many aspects. The fixed position of the glovebox arm ports restricts the movements of the workers' arms, which also makes it hard to lift heavy objects. Further, the operational workspace inside the glovebox is restricted by the gloves' reachability. Safety of workers is the paramount concern in glovebox operations and the very reason for their existence. Sharp edges and tools increases the risk of glove punctures, which may expose the operator to chemicals and radiation and risks contamination in the vicinity outside the glovebox. The operators are also affected by ergonomic stressors due to prolonged and repetitive operations. To achieve a high degree of human safety, robotic solutions to handle high-consequence materials inside the glovebox are desirable, as they remove the operators from the hazards listed above. However, robots, in general, lack the degree of adaptability necessary for most high-consequence material handling tasks as these tasks are often unstructured or highly variable. Likewise, most human operators, while highly skilled in the tasks at hand (task expertise), lack the robot programming skills necessary to adapt the robot's motions to the variations (programming expertise). A way to bridge this divide is to make robot programming more intuitive to human operators. Since humans naturally teach one another through demonstration and learning, robotic 'programming by demonstration' paradigms have begun to appear to reduce the burden of robot reprogramming. Gesture-Based Programming attempts to achieve that by enabling a robot to observe the normal actions and affordances of a human performing a task to learn to map those onto the skills of the robot. In the end, this approach permits benefits from both the human and the robot: the human's adaptive decision making and the robot's resilience to high-consequence material. Robotic solutions assuringly provide protection for the operators, however their autonomy brings about other potential risks. Artificial Intelligence is not perfect; it is a black box, which can produce results, but the full extent of those results may not be known precisely. In self-driving cars, a hypothetical AI problem is the 'green man detector'. Since self-driving cars have presumably never seen a green man, how can we know it won't veer toward him the first time it sees a green man? Likewise, even though GbP provides an open method for safe task execution replicating a human, incorrect task inferences can lead to potential collisions or unintended operations. Thus, a hardware safety measure is proposed to provide reliable operation inside the glovebox, and true safe operations are achieved by mechanically limiting the operational range of each arm link. The operational range is computed and validated through exhaustive offline simulations, which can be streamlined by affordable computing power. Super Baxter is one such robot which incorporates these capabilities. It is a human-like, bimanual robot, being developed at the Collaborative Robotics Lab (CRL) in conjunction with the Dept. of Energy/Environmental Management, Rethink Robotics, Barrett Technology, and the NSF Center for Robots and Sensors for the Human Well-Being (RoSe-HUB). Super Baxter is envisioned to represent the next generation of collaborative robots that will be intrinsically human-safe, as well as exceptionally human-intuitive. In summary, Super Baxter will enable safe glovebox operations through low-shot Gesture based Programming incorporated with hardware safety measures. Two aspects of safety will be discussed in the paper. First, the safety of the operator achieved by allowing him to program Super Baxter remotely through Gesture based Programming. Second, the safety of the glovebox, achieved by incorporating hardware safety measures using joint limits. Further, the capability of Super Baxter on Gesture based Programming is demonstrated through typical object manipulation tasks in glovebox operations. (authors)

42 ENGINEERING↗
DOE OSTI2022

Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer

Abstract Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Cycle accurate and cycle reproducible memory for an FPGA based hardware accelerator

A method, system and computer program product are disclosed for using a Field Programmable Gate Array (FPGA) to simulate operations of a device under test (DUT). The DUT includes a device memory having a number of input ports, and the FPGA is associated with a target memory having a second number of input ports, the second number being less than the first number. In one embodiment, a given set of inputs is applied to the device memory at a frequency Fd and in a defined cycle of time, and the given set of inputs is applied to the target memory at a frequency Ft. Ft is greater than Fd and cycle accuracy is maintained between the device memory and the target memory. In an embodiment, a cycle accurate model of the DUT memory is created by separating the DUT memory interface protocol from the target memory storage array.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Cycle accurate and cycle reproducible memory for an FPGA based hardware accelerator

A method, system and computer program product are disclosed for using a Field Programmable Gate Array (FPGA) to simulate operations of a device under test (DUT). The DUT includes a device memory having a number of input ports, and the FPGA is associated with a target memory having a second number of input ports, the second number being less than the first number. In one embodiment, a given set of inputs is applied to the device memory at a frequency Fd and in a defined cycle of time, and the given set of inputs is applied to the target memory at a frequency Ft. Ft is greater than Fd and cycle accuracy is maintained between the device memory and the target memory. In an embodiment, a cycle accurate model of the DUT memory is created by separating the DUT memory interface protocol from the target memory storage array.

42 ENGINEERING↗
DOE OSTI2021

Cycle accurate and cycle reproducible memory for an FPGA based hardware accelerator

A method, system and computer program product are disclosed for using a Field Programmable Gate Array (FPGA) to simulate operations of a device under test (DUT). The DUT includes a device memory having a number of input ports, and the FPGA is associated with a target memory having a second number of input ports, the second number being less than the first number. In one embodiment, a given set of inputs is applied to the device memory at a frequency Fd and in a defined cycle of time, and the given set of inputs is applied to the target memory at a frequency Ft. Ft is greater than Fd and cycle accuracy is maintained between the device memory and the target memory. In an embodiment, a cycle accurate model of the DUT memory is created by separating the DUT memory interface protocol from the target memory storage array.

Asaad, Sameh W.↗
DOE OSTI2016

Investigation of Portable Event-Based Monte Carlo Transport Using the NVIDIA Thrust Library

Power consumption considerations are driving future high performance computing platforms toward many-core computing architectures. Los Alamos National Laboratory's Trinity machine, available in 2016, will use both Intel Xeon Haswell processors and Intel Xeon Phi Knights Landing many integrated core (MIC) architecture coprocessors. Lawrence Livermore National Laboratory's Sierra machine, available in 2018, will use an IBM PowerPC architecture along with Nvidia graphics processing unit (GPU) architecture accelerators. These different advanced architectures make the computing landscape in upcoming years complex. Traditional approaches to Monte Carlo transport do not work efficiently on these new computing platforms. MIC architectures require vectorization to operate efficiently, and vectorization is difficult to achieve in Monte Carlo transport. GPU architectures require additional code to explicitly use the hardware, requiring significant code changes or hardware specific branches in the source code. A significant challenge for Monte Carlo transport projects is to simultaneously support within a single source code base efficient simulations for both the current generation of architectures and the different advanced computing architectures. In order to address these challenges, two important changes are typically required: a new algorithmic approach for solving Monte Carlo transport, and explicit use of hardware specific software. In this paper, we describe initial research investigations of an event-based Monte Carlo transport algorithm implemented using the Nvidia Thrust library on a GPU for a Monte Carlo test code. The event-based algorithm targets many-core architectures by increasing SIMD (single instruction multiple data) parallelism, while Thrust potentially provides portable performance by allowing one source code base to compile code targeted for both CPUs and GPUs. We described preliminary investigations of portable event-based Monte Carlo algorithms implemented using the Nvidia Thrust library in a research Monte Carlo test code. We found that an explicit CUDA implementation of an event-based Monte Carlo algorithm performed significantly more efficiently than a Thrust implementation on GPU platforms, most likely as a result of additional flexibility in access to different memory spaces on the GPU. Additionally, we showed that on GPU platforms and at large enough problem sizes the event-based implementations perform more efficiently than the serial history-based implementation running on the host CPU. While investigating this problem, we also discovered that the performance of the event-based algorithm is affected by what tallies are being used. A zonal scalar flux tally requires atomic operations that significantly impacted the performance of the code, in some cases producing slowdowns instead of speedups. We decided to remove the tally in order to focus on the effectiveness of the event-based algorithm. Future work will be required to research more effective ways of handling such tallies. Additionally, we would like to consider new ways for optimizing both the Thrust and CUDA versions, in order to see how much performance we have yet to achieve. The potential trade-off between portability and performance was demonstrated in this investigation. Thrust provides both CPU and GPU versions of the code in one code base, but it does so at a cost. We discovered approximately a factor of 2-5 performance difference between Thrust and CUDA on each GPU platform we tested. Thrust provides a tool to access the GPUs with less effort and specialization, but it does so by giving up fine grained control where extra performance can be found for this application. Future work will be required to determine whether this performance differential between Thrust and CUDA can be reduced for event-based Monte Carlo transport. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Apparatus for skidding sensitivity testing of energetic materials

A remote-operated apparatus for testing the detonation sensitivity of energetic materials is detailed. Using an air ram and rotating disk, the normal force and transverse velocity of the impact plane are controlled independently, enabling the exploration of varying impact conditions over a wide parameter space. A microcontroller local to the apparatus is used to automate apparatus operation and ensure temporal alignment of the impacting ram head with the rotating disk. Calculation of the firing parameters and issuing of operational commands are handled by a remote computer and relayed to the local microcontoller for execution at the hardware level. Finally, impact forces are taken from fast strain measurements obtained from gauges incorporated into the ram head. Infrared imaging of explosive samples provides insight to the peak thermal temperatures experienced at the sample surface during the impact event.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Ginkgo - A math library designed to accelerate Exascale Computing Project science applications

Large-scale simulations require efficient computation across the entire computing hierarchy. A challenge of the Exascale Computing Project (ECP) was to reconcile highly heterogeneous hardware with the myriad of applications that were required to run on these supercomputers. Mathematical software forms the backbone of almost all scientific applications, providing efficient abstractions and operations that are crucial to harness the performance of computing systems. Ginkgo is one such mathematical software library, nurtured by ECP, providing high-performance, user-friendly, and performance portable interfaces for applications in ECP and beyond. In this paper, we elaborate on Ginkgo’s philosophy of high-performance software that is sustainable, reproducible, and easy to use. We showcase the wide feature set of solvers and preconditioners available in Ginkgo and the central concepts involved in their design. We elaborate on four different ECP software integrations: MFEM, PeleLM + SUNDIALS, XGC, and ExaSGD that use Ginkgo to accelerate their science runs. Performance studies of different problems from these applications highlight the effectiveness of Ginkgo and the benefits incurred by these ECP applications.

Cojean, Terry↗