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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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Tensor algebra is a powerful tool for computing, but writing optimized codes that operate on sparse tensors can be very complex. This project enables a Tensor Algebra Compiler (TACO) that simplifies this task from man-years to man-days and extends TACO to support complex and large distributed systems. This report details the hypotheses, approaches used, and findings in this project.
This project developed and implemented a system for conducting cybersecurity vulnerability detection of smart grid components and systems by performing static analysis of compiled software (“firmware”). The resulting system for automated vulnerability detection (AVUD) was implemented as part of Oak Ridge National Laboratory’s existing test bed for smart meters, the Sustainable Campus Initiative. The work consisted of two phases: the first phase implemented the necessary software and computational models to perform the analysis, and the second phase demonstrated the system on example firmware in partnership with smart meter manufacturer Sensus USA, Inc. The resulting system won an R&D 100 award and has been successfully commercialized, winning a National Laboratory Consortium Commercialization Award.
This document is the compilation of the sampling activities associated with the FY2021 NNSA funded characterization of the 03-0016, Ion Beam facility at Los Alamos National Laboratory. Included within this document are the radiological and non-radiological sampling plans, results from the executed sampling plans, areas not sampled, summary of findings for each area sampled, and anticipated waste streams.
This report summarizes the IAEA Workshop on Compilation of Experimental Nuclear Reaction Data held at the IAEA Headquarters in Vienna, Austria from 13 to 16 December 2022. The meeting was attended by 23 participants representing 12 cooperative Centres from seven Member States (China, Hungary, Japan, Korea, Russia, Ukraine and USA) and two International Organisations (NEA, IAEA) as well as a participant from Mongolia and Spain. A summary of the workshop is given in this report along with the conclusions and actions.
This report summarizes Unitary Fund’s contributions to the Department of Energy’s TEAM project (DE-SC0020266) under Thrust 2: Quantum Programming and Compilation. The central outcomes of this work have been the development of Mitiq, an open-source Python toolkit for applying quantum error mitigation (QEM) techniques to noisy quantum programs, and the invention, benchmarking and theoretical investigation of novel QEM techniques. Additional outcomes include the development of other open source software packages for the usage, simulation and control of quantum computers.
Due to the growing use of large language models (LLMs) by developers and researchers, it has become essential to reliably evaluate their ability to generate code that uses specialized libraries. We explore the use of compile-time and run-time evaluation of LLM-generated Kokkos code through extending the methods used by OpenAI with the HumanEval dataset. Our evaluation framework is based on the first 40 prompts from the Kokkos138 dataset. We start by discussing two different forms of LLM prompting, using entirely plain English or providing pseudocode for added context. These two methods are used to generate Kokkos code with the Llama-3.1-8B-Instruct and CodeQwen1.5-7B-Chat models. We found that both forms of prompting led to high failure rates and difficulties with reliably parsing LLM-generated code, while prompts with pseudocode for context generally led to improved results on more complicated tests.
This document is a compilation of the workshop agenda and presentation materials from the Alabama CSS Workshop in Tuscaloosa, Alabama.
This project developed the LAPIS compiler framework, built on the Multilevel Intermediate Representation (MLIR), to optimize sparse linear algebra operations and support performance portability across diverse architectures. The main innovation of LAPIS is the Kokkos dialect, which allows for lowering codes from a high productivity language to different architectures in an elegant way. The dialect also allows the conversion of lower-level MLIR code to C++ Kokkos code, facilitating the integration of scientific machine learning (SciML) models into applications. To extend LAPIS for distributed memory architectures, a new partition dialect was created to manage the distribution of sparse tensors and express communication patterns for sparse linear algebra operations. This dialect also supports the distributed execution of operators and includes algorithmic optimizations to minimize communication to improve performance. The project also demonstrates that MLIR can enable effective linear algebra-level optimizations, improving performance on different GPUs for both sparse and dense linear algebra kernels. Key applications of LAPIS include sparse linear algebra and graph kernels, TenSQL, a relational database management solution built on GraphBLAS, and the development of subgraph isomorphism and monomorphism kernels, showcasing performance portability. In summary, the LAPIS framework supports productivity, performance, portability, and distributed memory execution, while also enabling linear algebra-level optimizations that are challenging in traditional programming languages, with successful applications ranging from simple sparse linear algebra to complex graph kernels.
This report assembles comprehensive data from the Sirius-1 experiment conducted by Idaho National Laboratory in collaboration with the National Aeronautics and Space Administration. The primary goal is to provide a robust data set that external users can utilize for the validation of computational methods for transient multiphysics simulations. By compiling all relevant data, including experiment design calculations, detailed engineering drawings for the experiment and data from reactor and fuel specimen measurement, this report is intended to serves as a reference for researchers and engineers working on the development and validation of computational models for transient nuclear behavior.
The reliable determination of rotation–activity correlations (RACs) depends on precise measurements of the following stellar parameters: T {sub eff}, parallax, radius, metallicity, and rotational speed v sin i . In this paper, our goal is to focus on the determination of these parameters for a sample of K and M dwarfs. In a future paper (Paper II), we will combine our rotational data with activity data in order to construct RACs. Here, we report on a determination of effective temperatures based on the ( R – I ){sub C} color from the calibrations of Mann et al. and Kenyon and Hartmann for four samples of late-K, dM2, dM3, and dM4 stars. We also determine stellar parameters ( T {sub eff}, log( g ), and [M/H]) using the principal component analysis–based inversion technique for a sample of 105 late-K dwarfs. We compile all effective temperatures from the literature for this sample. We determine empirical radius–[M/H] correlations in our stellar samples. This allows us to propose new effective temperatures, stellar radii, and metallicities for a large sample of 612 late-K and M dwarfs. Our mean radii agree well with those of Boyajian et al. We analyze HARPS and SOPHIE spectra of 105 late-K dwarfs, and we have detected v sin i in 92 stars. In combination with our previous v sin i measurements in M and K dwarfs, we now derive P /sin i measures for a sample of 418 K and M dwarfs. We investigate the distributions of P /sin i , and we show that they are different from one spectral subtype to another at a 99.9% confidence level.
The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1\%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.
We present initial work towards an experimental implementation of a nonuniversal continuous variable quantum compiler capable of learning a linear phase operation with increased precision when quantum resources are used.
We implement a continuous-variable quantum compiler that can learn a linear phase operation with a fourfold increase in precision and a factor of 80 reduction in time to solution when quantum resources are used.
Quantum computing is in an era of limited resources. Current hardware lacks high fidelity gates, long coherence times, and the number of computational units required to perform meaningful computation. Contemporary quantum devices typically use a binary system, where each qubit exists in a superposition of the 0 and 1 states. Furthermore, it is often possible to access the 2 or even 3 states in the same physical unit by manipulating the system in different ways. In this work, we consider automatically encoding two qubits into one four-state ququart via a compression scheme. We use quantum optimal control to design efficient proof-of-concept gates that fully replicate standard qubit computation on these encoded qubits.
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