Search NASA⌕ Search

SEARCH · Search NASA

Results for “Google”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Throughput Analytics of Cloud Networks

A network of virtual machines at cloud server sites connected over virtual IO connections is a flexible, easily deployable, and cost-effective alternative to a physical network infrastructure with dedicated servers connected over leased fiber lines. We study the throughput performance of such a cloud network by collecting measurements over Google Cloud infrastructure spanning multiple continents. To study its ideal performance and impact of packet losses, we utilize its emulation using dedicated servers and connection hardware emulation devices. We compare the measurements over the cloud network to those over its emulation on a testbed. We examine the throughput profiles of both networks as a function of the round trip time and their utilization-concavity coefficients, estimated using measurements for common TCP versions. The throughput profile's concave-convex shape and its coefficient are critical indicators of the network performance, qualitatively and quantitatively, respectively. The results indicate their overall agreement between the production cloud network and its emulation using dedicated connections, and a near optimal throughput performance of the former except for a few under-performing connections. Also, the number of parallel flows is found to be a dominant factor in optimizing the throughput across various conditions and TCP versions.

Phanekham, Derek↗
DOE OSTI2020

Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications

The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.

Bolme, David↗
DOE OSTI2021

A Hybrid System for Learning Classical Data in Quantum States

Deep neural network-powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of computing resources. Especially in the post Moore’s Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high intensity training data. Meanwhile, quantum computing has exhibited its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrates quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world’s largest supercomputers. To this end, quantum-based learning becomes an area of interest, with the promising of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, comparing with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.86% and converging speedup by 66.67% faster.

Stein, Samuel A.↗
DOE OSTI2024

Lamellar: A Rust-based Asynchronous Tasking and PGAS Runtime for High Performance Computing

Cybersecurity is one of the largest concerns in modern computing, impacting and dictating how governments, private corporations, and individuals interact with and live in an increasingly digital world. The NSA has recently released a memo [ 1] on “Software Memory Safety” where they highlight that both Microsoft and Google have stated around 70% of software vulnerabilities were due to memory safety issues. Although languages such as C and C++ provide freedom and flexibility with memory management, guaran- teeing safety falls mostly on the developer. The NSA recommends using “memory safe” languages whenever possible. In this paper we introduce Lamellar, an asynchronous tasking and PGAS HPC runtime written in Rust, one such "memory safe" language. We describe the entire Lamellar stack, from network interfaces to high- level abstractions such as distributed LamellarArrays and Active Messages. We conclude by showing comparable performance to legacy PGAS runtimes (e.g. OpenSHMEM) on a subset of the BALE kernel suite while maintaining strong memory safety principles.

HPC Software Systems, Rust Programming Language, P↗
DOE OSTI2021

Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

digital oligarchy↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
DOE OSTI2021

SV-Sim: Scalable PGAS-based State Vector Simulation of Quantum Circuits

High-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ era. Observing that the major obstacle of scalable state-vector quantum simulation arises from the massively fine-grained irregular data-exchange with remote nodes, in this paper we present SV-Sim to apply the emerging PGAS-based communication models (i.e., direct peer access for intra-node CPUs/GPUs and SHMEM for inter-node CPU/GPU clusters) for efficient scalable quantum circuit simulation. Through an orchestrated device functional pointer design, SV-Sim is able to abstract the quantum gate sets across various heterogeneous backends, including IBM/Intel/AMD CPUs, NVIDIA /AMD GPUs, and Intel MIC, in a unified framework, but still asserting outstanding performance and tractable interface to higher-level quantum programming environments, such as IBM Qiskit, Microsoft Q\# and Google Cirq. Circumventing the disability of polymorphism in GPUs and leveraging the device-initiated one-sided communication, SV-Sim can process dynamically synthesized quantum circuit in a single GPU/CPU kernel without the need of expensive JIT or runtime branching, significantly improving the performance and simplifying the programming complexity for the emerging variational quantum algorithms. Evaluations on NVIDIA A100-DGX-1, V100-DGX-2, AMD MI100, ALCF Theta, and OLCF Summit HPCs show that SV-Sim can delivery scalable performance on various state-of-the-art HPC platforms, offering a useful tool for quantum algorithm validation and verification.

Li, Ang↗
DOE OSTI2022

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. Here, we present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Nguyen, Thien↗
DOE OSTI2025

A Digital Twin of Scalable Quantum Clouds

Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the limit of classical systems. The rapid development of quantum processors has led to the proliferation of cloud-based quantum computing services offered by platforms such as IBM, Google, and Amazon. These platforms introduce unique challenges in resource allocation, job scheduling, and multi-device orchestration as quantum workloads become increasingly complex. In this work, we present a digital twin of quantum cloud infrastructures: a framework designed to model and simulate the behavior of real quantum cloud systems. Developed in Python using the SimPy discrete-event simulation library, the framework replicates key aspects of quantum cloud environments, including detailed quantum device modeling, job lifecycle management, and job fidelity. It incorporates noise-aware fidelity estimation, making it the first of its kind to simulate superconducting gate-based quantum cloud systems at an administrative level with job fidelity. We present use cases as proof of concept, demonstrating that our quantum cloud simulation framework can act as a digital twin of a quantum cloud and support the modeling and implementation of practical systems.

Luo, Waylon [Kent State University]↗
DOE OSTI2020

Quantum Search Compiler (Qsearch) v2.0

Quantum Gate Synthesis is the process of taking a desired quantum gate operation, in this case specified as a unitary matrix, and decomposing it into a list of operations that can actually be performed on a quantum computer. Qsearch is an implementation of a quantum synthesis algorithm based on combining A* search with numerical optimization. It is designed to minimize CNOT count at the expense of having a long runtime. It produces particularly efficient quantum circuits in terms of CNOT count, up until about 4 qubits. It is generally impractical to run Qsearch on circuits with more than 4 qubits, but it can be used as a subroutine in algorithms designed to work with these larger circuits. Qsearch takes the form of a Python package, taking advantage of Numpy and Scipy, with part of the package written in Rust and taking advantage of OpenBLAS, NLOPT, and Google Ceres for faster runtime.

Lancu, Costin↗
DOE OSTI2022

MEASUR - Manufacturing Energy Assessment Software for Utility Reduction

MEASUR your energy savings with the free DOE MEASUR software The Department of Energy (DOE), with Oak Ridge National Laboratory (ORNL), released version 1.0 of their energy efficiency software tool MEASUR (Manufacturing Energy Assessment Software for Utility Reduction). MEASUR has been available for several years as a beta version, being tested by industry experts and real users, and will continue to be updated and improved in the coming years. It is an integrated suite of tools to aid manufacturers in improving the efficiency of energy systems and equipment within a plant, including motors, pumps, fans, process heating, steam, and compressed air. Additionally, there are modules for wastewater energy analysis and to help perform energy treasure hunts. Several calculators are also included, allowing users to independently perform smaller calculations and analyses (such as estimating pump head, performing a fan traverse analysis, estimating waste heat recovery potential, and cataloging compressed air leaks). The MEASUR modules are based on previous DOE software tools that have been used by industry since the early 2000s (such as MotorMaster, AirMaster+, PSAT, PHAST, and FSAT). The original tools only ran on Windows operating systems, and by Windows 10, most of them were inoperable. DOE started their energy efficiency software tool revitalization effort in 2016, first with PSAT (for pumps), then began to integrate the other tools and expand their functionality and utility. The new MEASUR suite provides an extensively more user-friendly, modern, and versatile set of tools. All the assessment modules and most of the calculators have several visual components and graphs and detailed help text for every user input. To help reach international users, the tool utilizes Google translate and users can easily change unit systems, even converting existing user inputs if desired. The assessment files can be organized within the internal file system and easily shared to other users, regardless of their operating system. The entire suite is free, open-source, and can be downloaded on Windows, Mac, or Linux operating systems.

Accawi, Gina [Oak Ridge National Lab. (ORNL), Oak ↗
DOE OSTI2022

NISQ Benchmarking

Test suite of quantum algorithms for Noisy Intermediate Scale Quantum (NISQ) computers. The test suite includes benchmark-style code for quantum volume circuits (QV), fairness sampling circuits, quantum telecloning circuits, and other NISQ benchmark style algorithms on small problems (i.e., up to 100 qubits), such as Variational Quantum Eigensolver (VQE), Hamiltonian Simulation, and Grover unstructured search example circuits. These benchmark-style applications are implemented in quantum software packages, mostly IBM's QISKIT, but may include vendor-specific frameworks, such as PyQuil (for Rigetti) or Q\# for Microsoft, or CirQ (for Google) as the test suite grows with the vendor sample. The test suite also includes numerical simulation code for Quantum Alternating Operator Ansatz (QAOA) algorithms, VQE, Hamiltonian Simulation and search examples. Numerical simulation code simulates quantum computers on classical computers, which is only possible for small problem instances; the implementation framework of choice is typically within Python, using the numpy/scipy libraries as well as extensions to the Julia language.

Pelofske, Elijah↗
DOE OSTI2020

JaqalPaq v.1.1

Jaqal-pup (Jaqal Programming Utilities Project) is a high-level Python metaprogramming interface for Jaqal, a quantum programming language for near-term quantum hardware (see https://arxiv.org/abs/2003.09382 for a description of Jaqal). In addition to parsing and writing out Jaqal programs, it includes the ability to read in and write out quantum programs in other languages and frameworks, e.g. Qiskit (IBM), Quil (Rigetti), Cirq (Google), Q# (Microsoft), ProjectQ (ETH Zurich), and tket (Cambridge Quantum Computing). It implements a gate scheduler, an emulator of a small-scale quantum computer programmed using Jaqal, and tie-ins for other simulation back-ends. SAND2020-12710 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Russo, Antonio↗
DOE OSTI2023

Azure Data Tools (FKA: OEDI (Open Energy Data Initiative) Data Access Tools) [SWR-23-92]

The Open Energy Data Initiative (OEDI) provides a number of tools to enable the use of the open data published through this initiative. The source is largely written in Python, including Jupyter notebooks. The Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from DOE Program Offices, National Laboratories and other collaborators.

Jonathan, Weers↗
DOE OSTI2024

AGILE GETRS

AGILE GETRS provides an optimized implementation of DETRS that is faster than vendor optimized libraries for small (< 4000) problem sizes. These small problems sizes are of interest to researchers in the Grid Research Integration and Deployment Center (GRID-C) researching real-time power electronics simulations. In addition to our implementation of DETRS, this code provides a test using Google Benchmark which generates matrices of various sizes, fills them with random numbers, runs GETRS, verifies the solution is correct and reports average runtime and other performance metrics.

Hahn, Steven [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2024

pnnl/JAX-CanVeg

Differentiable land surface model reimplementing an existing simulator, CANOAK, in JAX—a Google-developed Python package for high-performance machine learning research using automatic differentiation. The model's purpose is to perform hybrid land surface modeling that seamlessly couples process-based components with deep neural networks

Jiang, Peishi↗
DOE OSTI2025

2024 Workshop - Remote Sensing and Fluxes Upscaling for Real-world Impact - Tutorial v1

The software-tutorial was developed within the 2024 Remote Sensing and Fluxes Upscaling for Real-world Impact workshop as part of the hands-on session. The workshop was supported by AmeriFlux, National Ecological Observatory Network (NEON) and CarbonDew. The software provides basic tools to perform the following tasks: - gather remote sensing images using Google Earth Engine API; - gather flux data; - perform basic functions, such as plotting time-series, perform QA of the data, compute vegetation indices; - perform correlation analysis between flux data and remote sensing data; - perform flux predictions based on remote sensing data integrated in different modalities.

Falco, Nicola [Lawrence Berkeley National Laborato↗
DOE OSTI2025

PRIME: Protein Representation Inference for Mutation Evaluation

Protein language machine learning models built upon existing ESM-2 model developed by Evolutionary Scale (evolutionaryscale.ai) and an in-house protein language model based on the BERT model developed by Google. The code also includes model training scripts and saved checkpoints from our own training using publicly available SARS-CoV-2 protein sequences.

Gibson, Kaetlyn [Los Alamos National Lab]↗