http://www.rssboard.org/rss-specification 720 https://escholarship.org/uc/lbnl_cs_com/rss Recent eScholarship items from Computing Sun, 6 Sep 2026 00:34:45 +0000 https://escholarship.org/uc/item/0550p5hf Sampling from probability distributions of the form [Formula: see text], where [Formula: see text] is a continuous potential, is a fundamental task across physics, chemistry, biology, computer science, and statistics. However, when [Formula: see text] is nonconvex, the resulting distribution becomes non-logconcave, and classical methods such as Langevin dynamics often exhibit poor performance. We introduce a quantum algorithm that provably accelerates a broad class of continuous-time sampling dynamics. For Langevin dynamics, our method encodes the target Gibbs measure into the amplitudes of a quantum state, identified as the kernel of a block matrix derived from a factorization of the Witten Laplacian operator. This connection enables Gibbs sampling via singular value thresholding and yields up to a quartic quantum speedup over best-known classical Langevin-based methods in the non-logconcave setting. Building on this framework, we further develop the first quantum algorithm that... https://escholarship.org/uc/item/0550p5hf Mon, 29 Jun 2026 00:00:00 +0000 Leng, Jiaqi Ding, Zhiyan Chen, Zherui Lin, Lin https://escholarship.org/uc/item/8k04199v Derivative-free optimization (DFO) problems are optimization problems where derivative information is unavailable or extremely difficult to obtain. Model-based DFO solvers have been applied extensively in scientific computing. Powell's NEWUOA (2004) [Powell, The NEWUOA software for unconstrained optimization without derivatives, in Large-Scale Nonlinear Optimization, Nonconvex Optimization and its Applications Vol. 83, G. Di Pillo and M. Roma, eds., Springer, 2006, pp. 255–297] and Wild's POUNDerS (2014) [Wild, Solving derivative-free nonlinear least squares problems with POUNDERS, in Advances and Trends in Optimization with Engineering Applications, T. Terlaky, M.F. Anjos, and S. Ahmed, eds., SIAM, 2017, pp. 529–540] explore the numerical power of the minimal norm Hessian (MNH) model for DFO and contributed to the open discussion on building better models with fewer data to achieve faster numerical convergence. Another decade later, we propose the regional minimal updating (ReMU)... https://escholarship.org/uc/item/8k04199v Thu, 25 Jun 2026 00:00:00 +0000 Xie, Pengcheng https://orcid.org/0000-0001-5973-1535 Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/0363b4cj Nonconvex, nonlinear optimization problems arise naturally in parameter fitting and machine learning. While augmented Lagrangian methods have demonstrated robust convergence for classes of these problems, their convergence for block updates has been relatively unexplored outside of the context of the alternating direction method of multipliers (ADMM). ADMM has seen extensive use in these applications, but may exhibit uncertain convergence behavior in many practical nonconvex settings, and struggles with general nonlinear constraints. In contrast, filter methods have proved effective in enforcing convergence for sequential quadratic programming methods and interior point methods with feasibility criteria. We develop an ADMM-filter method for highly nonlinear and nonconvex problems. We show convergence under mild assumptions for several types of coordinate descent schemes, and demonstrate our algorithm on nonnegative matrix factorization and completion problems in imaging and chemical... https://escholarship.org/uc/item/0363b4cj Fri, 19 Jun 2026 00:00:00 +0000 Baraldi, Robert Leyffer, Sven Wild, Stefan https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/2m28t4fw We present Q-SASS, a quasi-Newton method for unconstrained stochastic optimization that does not rely on common random numbers. Most existing quasi-Newton approaches leverage common random numbers to construct second-order updates. However, motivated by challenges in variational quantum algorithms—where such coordination is not possible—we consider the setting in which function values and gradients are accessible only through noisy probabilistic zeroth- and first-order oracles, and no common random numbers can be exploited. We derive high-probability tail bounds on the iteration complexity of our algorithm for nonconvex, convex, and strongly convex (more generally, those satisfying the PL condition) objective functions. Finally, we demonstrate the empirical benefits of our quasi-Newton updating scheme on both synthetic and quantum chemistry problems. https://escholarship.org/uc/item/2m28t4fw Mon, 30 Mar 2026 00:00:00 +0000 Menickelly, Matt Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Xie, Miaolan https://escholarship.org/uc/item/7610998f The HFBTHO code implements a nuclear energy density functional solver to model the structure of atomic nuclei. HFBTHO has previously been used to calibrate energy functionals and perform sensitivity analysis by using derivative-free methods. To enable derivative-based optimization and uncertainty quantification approaches, we must compute the derivatives of HFBTHO outputs with respect to the parameters of the energy functional, which are a subset of all input parameters of the code. We use the algorithmic/automatic differentiation (AD) tool Tapenade to differentiate HFBTHO. We compare the derivatives obtained using AD against finite-difference approximation and examine the performance of the derivative computation. https://escholarship.org/uc/item/7610998f Wed, 18 Mar 2026 00:00:00 +0000 Hascoët, Laurent Menickelly, Matt Narayanan, Sri Hari Krishna O’Neal, Jared Schunck, Nicolas Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/97d274dz The escalating demands of scientific collaborations necessitate advanced networking for deterministic, secure, and orchestrated services across multiple administrative domains. The Software-Defined Network for End-to-end Networked Science at the Exascale (SENSE) paradigm addresses these needs through intent-based networking and multi-domain orchestration. This paper evaluates SENSE’s performance on a comprehensive multi-domain testbed, including GNA-G AutoGOLE, the National Research Platform (NRP), FABRIC, and production LHC CMS infrastructure. Our results demonstrate that intent-based service requests are successfully translated into network configurations, with average provisioning times of 183 seconds for simple services and 290 seconds for complex multi-domain workflows. Performance monitoring confirms that SENSE maintains guaranteed bandwidth allocations, enabling higher-priority data flows to complete significantly faster than in best-effort scenarios. This capability transforms... https://escholarship.org/uc/item/97d274dz Thu, 26 Feb 2026 00:00:00 +0000 Monga, Inder https://orcid.org/0000-0003-4524-0457 Hussain, Mazahir Balcas, Justas Arora, Aashay https://orcid.org/0000-0003-3453-4740 Davila, Diego Cho, Buseung Yang, Xi De Laat, Cees https://escholarship.org/uc/item/5wx7q92m Quantum network control is a major research area of the QUANT-NET project. We strive to build a quantum network control plane to orchestrate and manage all the physicallayer technologies, and to explore what a quantum network control plane should look like in the future, so as to automate high-rate and high-fidelity entanglement generation, distribution, and storage in an efficient, reliable, and cost-effective way. To these ends, we have designed a two-level control framework for quantum networks. Within such a framework, a two-level scheduler has been implemented to support synchronous time slot scheduling, network-wide non-real-time control, and nodewide real-time control. This two-level control framework and the scheduler are being deployed and evaluated in the QUANTNET testbed. Enabled by this two-level control framework and the scheduler, several basic quantum network operations have been automated in the testbed, which include automated quantum node calibration and on-demand... https://escholarship.org/uc/item/5wx7q92m Wed, 25 Feb 2026 00:00:00 +0000 Yu, Se-young Perego, Elia Phillips, Justin Cheah, You-Wei Umesh, Prathwiraj Gao, Guangqi Liu, Jiarui Kissel, Ezra Bregar, Michael Sun, Ke Wu, Qiming Valivarthi, Raju Saglamyurek, Erhan Wu, Wenji Spiropulu, Maria Häffner, Hartmut Monga, Inder https://orcid.org/0000-0003-4524-0457 https://escholarship.org/uc/item/1203r78b As quantum networking experiments move from laboratory experiments to larger scale deployments, integrated control software becomes essential for managing complex interactions between the numerous distributed resources involved. While a number of laboratory-scale control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. With the QUANT-NET Control Plane (QNCP), we introduce a model-based, extensible control plane implementation that offers a framework for enabling network-wide orchestration in quantum information network environments. QCNP provides a quantum network data model, resource management, communication primitives, and a plugin interface for defining orchestration and protocol interactions across distributed quantum network devices and services. This paper describes the design and architecture of QNCP, its implementation, and opportunities for extensibility... https://escholarship.org/uc/item/1203r78b Wed, 25 Feb 2026 00:00:00 +0000 Yu, Se-young Zhang, Liang Kissel, Ezra https://orcid.org/0000-0003-3972-9651 Wu, Wenji Monga, Inder https://orcid.org/0000-0003-4524-0457 https://escholarship.org/uc/item/5393w8g5 The rapid growth of data volumes from large scientific collaborations, such as the Large Hadron Collider (LHC), presents significant challenges for the High Energy Physics (HEP) community. With annual data volumes projected to increase by a factor of thirty by 2028, efficient data management has become a critical concern. The HEP community’s reliance on wide-area networks for global data distribution often results in redundant long-distance transfers, leading to network congestion and degraded application performance. This study investigates the effectiveness of regional data caches in mitigating network congestion and enhancing application performance, using a large-scale dataset of millions of access records from regional caches in Southern California, Chicago, and Boston, which serve the LHC’s CMS experiment. Our analysis reveals the substantial potential of in-network caching to transform large-scale scientific data dissemination, enabling faster and more efficient data access... https://escholarship.org/uc/item/5393w8g5 Tue, 2 Dec 2025 00:00:00 +0000 Sim, Alex https://orcid.org/0000-0002-6295-1982 Wang, Erica Monga, Ronak Wu, Kesheng Balcas, Justas White, Brendan Guok, Chin https://orcid.org/0000-0003-4532-1222 Monga, Inder https://orcid.org/0000-0003-4524-0457 Davila, Diego Würthwein, Frank Newman, Harvey https://escholarship.org/uc/item/38q3q585 AmeriFlux is a community of scientists measuring ecosystem carbon, water, and energy fluxes across the Americas with eddy covariance techniques. The network’s data team collects flux data for quality assessment and provides standardized data products to the earth science research community. Critical for scientists’ use of the flux data are the supporting Biological, Ancillary, Disturbance and Metadata (BADM) that provide context, such as measurement heights, instrument operations, and disturbance events. Managing and collating BADM into standardized data products are challenging due to their inherent long-tail data characteristics, i.e., they are diverse, free-formed, and infrequently measured. Over the past 12 years, we have worked with the community to standardize and then manage BADM using a SQL database with strong data quality criteria. BADM’s inherent nature demands rigorous quality checks of submitted data. Some of these checks provide feedback to data providers for correction,... https://escholarship.org/uc/item/38q3q585 Fri, 31 Oct 2025 00:00:00 +0000 Cheah, You-Wei https://orcid.org/0000-0003-2241-4901 Christianson, Danielle Chu, Housen https://orcid.org/0000-0002-8131-4938 Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 O'Brien, Fianna Ong, Yeongshnn van Ingen, Catharine Torn, Margaret https://orcid.org/0000-0002-8174-0099 Agarwal, Deb https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/83m6c6wn In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated... https://escholarship.org/uc/item/83m6c6wn Fri, 10 Oct 2025 00:00:00 +0000 Bollapragada, Raghu Karamanli, Cem Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/9x1858hh Superfacility: The Convergence of Data, Compute, Networking, Analytics and Software https://escholarship.org/uc/item/9x1858hh Mon, 22 Sep 2025 00:00:00 +0000 Antypas, Katie Canon, Shane Dart, Eli https://orcid.org/0000-0002-8229-5433 Fagnan, Kjiersten Gerhardt, Lisa https://orcid.org/0000-0003-0166-5162 Jacobsen, Doug Lockwood, Glenn K https://orcid.org/0000-0002-9241-9372 Monga, Inder https://orcid.org/0000-0003-4524-0457 Nugent, Peter https://orcid.org/0000-0002-3389-0586 Ramakrishnan, Lavanya Snavely, Cory https://orcid.org/0000-0003-2021-4746 Parkinson, Dilworth Hexemer, Alexander https://orcid.org/0000-0002-5269-0125 Tull, Craig https://escholarship.org/uc/item/2px1n07k Atom-cavity systems offer unique advantages for building large-scale distributed quantum computers by providing strong atom-photon coupling while allowing for high-fidelity local operations of atomic qubits. However, in prevalent schemes where the photonic state is encoded in polarization, cavity birefringence introduces an energy splitting of the cavity eigenmodes and alters the polarization states, thus limiting the fidelity of remote entanglement generation. To address this challenge, we propose a scheme that encodes the photonic qubit in the frequency degree of freedom. The scheme relies on resonant coupling of multiple transverse cavity modes to different atomic transitions that are well separated in frequency. We numerically investigate the temporal properties of the photonic wave packet, two-photon interference visibility, and atom-atom entanglement fidelity under various cavity polarization-mode splittings and find that our scheme is less affected by cavity birefringence.... https://escholarship.org/uc/item/2px1n07k Tue, 1 Jul 2025 00:00:00 +0000 Zhang, Chengxi Phillips, Justin Monga, Inder https://orcid.org/0000-0003-4524-0457 Saglamyurek, Erhan Wu, Qiming Haeffner, Hartmut https://orcid.org/0000-0002-5113-9622 https://escholarship.org/uc/item/0737p2dd Approaching the end of the FABRIC project construction phase, many experimenters expressed a need for integrating heterogeneous types of resources from external testbed and cloud providers. This prompted research in cross-testbed federation paradigms, practically in pursuit of the vision of 'testbed of testbeds'. With past experience and lessons learned, we propose to adopt a 'tool-based federation paradigm' with the hypothesis that a tool-based federation approach is viable and performant for automating large, complex cross-testbed experiments. In this paper, we discuss the challenges and solutions in developing the FABRIC Federation Extension (FabFed), a software framework that implements the tool-based federation approach and enables FABRIC users to run large experiments across multiple testbed and cloud providers. We validate our approach through extensive use of FabFed to build complex experiments across both the FABRIC and partner testbeds. We also share our observations... https://escholarship.org/uc/item/0737p2dd Tue, 1 Jul 2025 00:00:00 +0000 Yang, Xi Kissel, Ezra https://orcid.org/0000-0003-3972-9651 Essiari, Abdelilah Zhang, Liang Lehman, Tom Monga, Inder https://orcid.org/0000-0003-4524-0457 Ruth, Paul Thareja, Komal Baldin, Ilya https://escholarship.org/uc/item/6nf470xc Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been shown to provide an exponential speedup on a learning version of the discrete logarithm problem. Understanding the generalization of quantum models is essential to realizing similar speedups on problems of practical interest. Recent results demonstrate that generalization is hindered by the exponential size of the quantum feature space. Although these results suggest that quantum models cannot generalize when the number of qubits is large, in this paper we show that these results rely on overly restrictive assumptions. We consider a wider class of models by varying a hyperparameter that we call quantum kernel bandwidth. We analyze the large-qubit limit and provide explicit formulas for the generalization of a quantum model that can be solved in closed form. Specifically, we show that changing the value... https://escholarship.org/uc/item/6nf470xc Tue, 22 Apr 2025 00:00:00 +0000 Canatar, A Peters, E Pehlevan, C Wild, SM https://orcid.org/0000-0002-6099-2772 Shaydulin, R https://escholarship.org/uc/item/3gr340t0 Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the... https://escholarship.org/uc/item/3gr340t0 Tue, 22 Apr 2025 00:00:00 +0000 Chang, Tyler H Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/7zc245gv Research Spotlights https://escholarship.org/uc/item/7zc245gv Tue, 11 Mar 2025 00:00:00 +0000 Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/44c6z7w9 libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator–allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager–worker communication substrates each of which has different benefits. These include Python’s multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We... https://escholarship.org/uc/item/44c6z7w9 Tue, 11 Mar 2025 00:00:00 +0000 Hudson, Stephen Larson, Jeffrey Navarro, John-Luke Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/3sq2921c This paper presents an algorithmic framework for solving unconstrained stochastic optimization problems using only stochastic function evaluations. We employ central finite-difference based gradient estimation methods to approximate the gradients and dynamically control the accuracy of these approximations by adjusting the sample sizes used in stochastic realizations. We analyze the theoretical properties of the proposed framework on nonconvex functions. Our analysis yields sublinear convergence results to the neighborhood of the solution, and establishes the optimal worst-case iteration complexity (O(ε−1)) and sample complexity (O(ε−2)) for each gradient estimation method to achieve an ε-accurate solution. Finally, we demonstrate the performance of the proposed framework and the quality of the gradient estimation methods through numerical experiments on nonlinear least squares problems. https://escholarship.org/uc/item/3sq2921c Tue, 11 Mar 2025 00:00:00 +0000 Bollapragada, Raghu Karamanli, Cem Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/2651d06p Research Spotlights https://escholarship.org/uc/item/2651d06p Tue, 11 Mar 2025 00:00:00 +0000 Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/12z7x7hj The Johnson-Lindenstrauss (JL) lemma is a powerful tool for dimensionality reduction in modern algorithm design. The lemma states that any set of high-dimensional points in a Euclidean space can be projected into lower dimensions while approximately preserving pairwise Euclidean distances. Random matrices satisfying this lemma are called JL transforms (JLTs). Inspired by existing s-hashing JLTs with exactly s nonzero elements on each column, the present work introduces an ensemble of sparse matrices encompassing so-called s-hashing-like matrices whose expected number of nonzero elements on each column is s. The independence of the sub-Gaussian entries of these matrices and the knowledge of their exact distribution play an important role in their analyses. Using properties of independent sub-Gaussian random variables, these matrices are demonstrated to be JLTs, and their smallest nontrivial singular values and largest singular values are estimated nonasymptotically using a technique... https://escholarship.org/uc/item/12z7x7hj Tue, 11 Mar 2025 00:00:00 +0000 Dzahini, KJ Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/0x7138n5 Unmatched: 50 Years of Supercomputing, A Personal Journey Accompanying the Evolution of a Powerful Tool https://escholarship.org/uc/item/0x7138n5 Tue, 3 Dec 2024 00:00:00 +0000 Barkai, David https://escholarship.org/uc/item/0555v6rb In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer the features for tasks such as machine learning, instead, we explore dimensionality reduction (DR) as a data-driven approach for engineering these low-dimensional representations. Through a careful examination of available feature selection and feature extraction techniques, we propose a new class named feature clustering. These new methods could utilize different forms of clustering to help evaluate the relative importance of features and take on properties different from the well-known DR algorithms. To evaluate these algorithms, we develop a parallel computing framework that optimizes their hyperparameters on a sample of application datasets. This framework harnesses the parallel computing power to examine a large number of parameter combinations and enables hyperparameter tuning and model tuning... https://escholarship.org/uc/item/0555v6rb Tue, 3 Dec 2024 00:00:00 +0000 Chung, Joshua De Prado, Marcos Lopez Simon, Horst https://orcid.org/0000-0003-0832-3720 Wu, Kesheng https://escholarship.org/uc/item/8kf319cb The types of constraints encountered in black-box simulation-based optimization problems differ significantly from those addressed in nonlinear programming. We introduce a characterization of constraints to address this situation. We provide formal definitions for several constraint classes and present illustrative examples in the context of the resulting taxonomy. This taxonomy, denoted KARQ, is useful for modeling and problem formulation, as well as optimization software development and deployment. It can also be used as the basis for a dialog with practitioners in moving problems to increasingly solvable branches of optimization. https://escholarship.org/uc/item/8kf319cb Tue, 24 Sep 2024 00:00:00 +0000 Le Digabel, Sébastien Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/7sk0f70s libEnsemble: A complete Python toolkit for dynamic ensembles of calculations https://escholarship.org/uc/item/7sk0f70s Tue, 24 Sep 2024 00:00:00 +0000 Hudson, Stephen Larson, Jeffrey Navarro, John-Luke Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/6t07v64x Owing to large pressure gradients at early times, standard hydrodynamic model simulations of relativistic heavy-ion collisions do not become reliable until O(1) fm/c after the collision. To address this one often introduces a prehydrodynamic stage that models the early evolution microscopically, typically as a conformal, weakly interacting gas. In such an approach the transition from the prehydrodynamic to the hydrodynamic stage is discontinuous, introducing considerable theoretical model ambiguity. Alternatively, fluids with large anisotropic pressure gradients can be handled macroscopically using the recently developed viscous anisotropic hydrodynamics (VAH). In high-energy heavy-ion collisions VAH is applicable already at very early times, and at later times transitions smoothly into conventional second-order viscous hydrodynamics. We present a Bayesian calibration of the VAH model with experimental data for Pb-Pb collisions at the LHC at sNN=2.76 TeV. We find that the VAH... https://escholarship.org/uc/item/6t07v64x Tue, 24 Sep 2024 00:00:00 +0000 Liyanage, Dananjaya Sürer, Özge Plumlee, Matthew Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Heinz, Ulrich https://escholarship.org/uc/item/69x6n85g We consider the solution of finite-sum minimization problems, such as those appearing in nonlinear least-squares or general empirical risk minimization problems. We are motivated by problems in which the summand functions are computationally expensive and evaluating all summands on every iteration of an optimization method may be undesirable. We present the idea of stochastic average model (SAM) methods, inspired by stochastic average gradient methods. SAM methods sample component functions on each iteration of a trust-region method according to a discrete probability distribution on component functions; the distribution is designed to minimize an upper bound on the variance of the resulting stochastic model. We present promising numerical results concerning an implemented variant extending the derivative-free model-based trust-region solver POUNDERS, which we name SAM-POUNDERS. https://escholarship.org/uc/item/69x6n85g Tue, 24 Sep 2024 00:00:00 +0000 Menickelly, Matt Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/4381990k The ptychographic iterative engine (PIE) is a widely used algorithm that enables phase retrieval at nanometer-scale resolution over a wide range of imaging experiment configurations. By analyzing diffraction intensities from multiple scanning locations where a probing wavefield interacts with a sample, the algorithm solves a difficult optimization problem with constraints derived from the experimental geometry as well as sample properties. The effectiveness at which this optimization problem is solved is highly dependent on the ordering in which we use the measured diffraction intensities in the algorithm, and random ordering is widely used due to the limited ability to escape from stagnation in poor-quality local solutions. In this study, we introduce an extension to the PIE algorithm that uses ideas popularized in recent machine learning training methods, in this case minibatch stochastic gradient descent. Our results demonstrate that these new techniques significantly improve... https://escholarship.org/uc/item/4381990k Tue, 24 Sep 2024 00:00:00 +0000 Tripathi, Ashish Wendy Di, Zichao Jiang, Zhang Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/1gz0p9z4 This work proposes a framework for large-scale stochastic derivative-free optimization (DFO) by introducing STARS, a trust-region method based on iterative minimization in random subspaces. This framework is both an algorithmic and theoretical extension of a random subspace derivative-free optimization (RSDFO) framework, and an algorithm for stochastic optimization with random models (STORM). Moreover, like RSDFO, STARS achieves scalability by minimizing interpolation models that approximate the objective in low-dimensional affine subspaces, thus significantly reducing per-iteration costs in terms of function evaluations and yielding strong performance on largescale stochastic DFO problems. The user-determined dimension of these subspaces, when the latter are defined, for example, by the columns of so-called Johnson-Lindenstrauss transforms, turns out to be independent of the dimension of the problem. For convergence purposes, inspired by the analyses of RSDFO and STORM, both... https://escholarship.org/uc/item/1gz0p9z4 Tue, 24 Sep 2024 00:00:00 +0000 Dzahini, KJ Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/0f49090p This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers... https://escholarship.org/uc/item/0f49090p Tue, 24 Sep 2024 00:00:00 +0000 Akinlana, Damola M Fadikar, Arindam Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Zuniga-Garcia, Natalia Auld, Joshua https://escholarship.org/uc/item/5cg43163 The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we explore the effect of adding isovector pairing terms, which are responsible for different proton and neutron pairing fields, by comparing a 13D model without the isovector pairing term against the extended 14D model. At the heart of the calibration is a carefully selected heterogeneous dataset of experimental observables representing ground-state properties of spherical even–even nuclei. To quantify the impact of the calibration dataset on model parameters and the importance of the new terms, we carry out advanced sensitivity and correlation analysis on both models. The extension to 14D improves the overall quality of the model... https://escholarship.org/uc/item/5cg43163 Tue, 10 Sep 2024 00:00:00 +0000 Reinhard, Paul-Gerhard O’Neal, Jared Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Nazarewicz, Witold https://escholarship.org/uc/item/8md8q9kq An Architecture For Edge Networking Services https://escholarship.org/uc/item/8md8q9kq Wed, 28 Aug 2024 00:00:00 +0000 Brown, Lloyd Marx, Emily Bali, Dev Amaro, Emmanuel Sur, Debnil Kissel, Ezra https://orcid.org/0000-0003-3972-9651 Monga, Inder https://orcid.org/0000-0003-4524-0457 Katz-Bassett, Ethan Krishnamurthy, Arvind McCauley, James Narechania, Tejas https://orcid.org/0000-0001-6495-6413 Panda, Aurojit Shenker, Scott https://escholarship.org/uc/item/8vv9390p Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future. https://escholarship.org/uc/item/8vv9390p Tue, 18 Jun 2024 00:00:00 +0000 Sim, Caitlin Wu, Kesheng Sim, Alex https://orcid.org/0000-0002-6295-1982 Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 Hazen, Damian Würthwein, Frank Davila, Diego Newman, Harvey Balcas, Justas https://escholarship.org/uc/item/9fg8k5xh ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report) https://escholarship.org/uc/item/9fg8k5xh Tue, 7 Nov 2023 00:00:00 +0000 Dart, Eli https://orcid.org/0000-0002-8229-5433 Zurawski, Jason https://orcid.org/0000-0001-8389-4705 Hawk, Carol Brown, Benjamin Monga, Inder https://orcid.org/0000-0003-4524-0457 https://escholarship.org/uc/item/8gs7k78f Data-intensive sciences are becoming increasingly important for modern sciences. The transport control plane (TC-Plane) of the networks supporting data-intensive sciences can be important to achieve efficient and controlled transport of data for data-intensive sciences. In this paper, we analyze FTS, which is the de facto TC-Plane of the largest data-intensive network, revealing both efficiency and resource control issues of the current design. We then present the design and initial evaluation of Transport Control Networking (TCN), a design that is based on FTS but introduces (1) network-application co-design/coordination, which uses ALTO to realize network-wide resource control, and (2) a general, efficient, flexible optimization framework for TC-Plane, which allows both zero-order and first-order (e.g., bottleneck structure) gradient-based algorithms. We also discuss future work to engage the broad networking and data-intensive sciences communities. https://escholarship.org/uc/item/8gs7k78f Tue, 10 Oct 2023 00:00:00 +0000 Dunefsky, Jacob Soleimani, Mahdi Yang, Ryan Ros-Giralt, Jordi Lassnig, Mario Monga, Inder https://orcid.org/0000-0003-4524-0457 Wuerthwein, Frank K Zhang, Jingxuan Gao, Kai Yang, Y Richard https://escholarship.org/uc/item/7hk9g5d3 QUANT-NET: A testbed for quantum networking research over deployed fiber https://escholarship.org/uc/item/7hk9g5d3 Tue, 10 Oct 2023 00:00:00 +0000 Monga, Inder https://orcid.org/0000-0003-4524-0457 Saglamyurek, Erhan Kissel, Ezra Haffner, Hartmut Wu, Wenji https://escholarship.org/uc/item/3qd4z7b0 AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest... https://escholarship.org/uc/item/3qd4z7b0 Wed, 27 Sep 2023 00:00:00 +0000 Chu, Housen https://orcid.org/0000-0002-8131-4938 Christianson, Danielle S https://orcid.org/0000-0002-8663-7701 Cheah, You-Wei https://orcid.org/0000-0003-2241-4901 Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 O’Brien, Fianna Geden, Joshua Ngo, Sy-Toan Hollowgrass, Rachel Leibowitz, Karla Beekwilder, Norman F Sandesh, Megha Dengel, Sigrid https://orcid.org/0000-0002-4774-9188 Chan, Stephen W https://orcid.org/0000-0002-4583-1559 Santos, André https://orcid.org/0000-0002-7320-7649 Delwiche, Kyle Yi, Koong https://orcid.org/0000-0002-8630-3031 Buechner, Christin https://orcid.org/0000-0002-9725-2671 Baldocchi, Dennis https://orcid.org/0000-0003-3496-4919 Papale, Dario Keenan, Trevor F https://orcid.org/0000-0002-3347-0258 Biraud, Sébastien C Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 Torn, Margaret S https://orcid.org/0000-0002-8174-0099 https://escholarship.org/uc/item/65s4w5fv Large scientific collaborations often share huge volumes of data around the world. Consequently a significant amount of network bandwidth is needed for data replication and data access. Users in the same region may possibly share resources as well as data, especially when they are working on related topics with similar datasets. In this work, we study the network traffic patterns and resource utilization for scientific data caches connecting European networks to the US. We explore the efficiency of resource utilization, especially for network traffic which consists mostly of transatlantic data transfers, and the potential for having more caching node deployments. Our study shows that these data caches reduced network traffic volume by 97% during the study period. This demonstrates that such caching nodes are effective in reducing wide-area network traffic. https://escholarship.org/uc/item/65s4w5fv Tue, 29 Aug 2023 00:00:00 +0000 Deng, Ziyue Sim, Alex https://orcid.org/0000-0002-6295-1982 Wu, Kesheng Guok, Chin https://orcid.org/0000-0003-4532-1222 Hazen, Damian Monga, Inder https://orcid.org/0000-0003-4524-0457 Andrijauskas, Fabio https://orcid.org/0000-0002-1254-8570 Würthwein, Frank Weitzel, Derek https://escholarship.org/uc/item/9db4k51q In x-ray spectromicroscopy, a set of images can be acquired across an absorption edge to reveal chemical speciation. We previously described the use of non-negative matrix approximation methods for improved classification and analysis of these types of data. We present here an approach to find appropriate values of regularization parameters for this optimization approach. https://escholarship.org/uc/item/9db4k51q Sat, 19 Aug 2023 00:00:00 +0000 Mak, Rachel Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Jacobsen, Chris https://escholarship.org/uc/item/7bd612c9 Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2021 lightning talks, which cover four broad topics: (i) libEnsemble, a Python library to coordinate the concurrent evaluation of dynamic ensembles of calculations; (ii) Edu WRENCH, a set of online pedagogic modules that provides simulation-driven hands-on activity in the browser; (iii) VisDict, an envisioned visual dictionary framework that will translate terms, jargon, and concepts between research domains and workflow providers; and (iv) Pegasus Kickstart, a lightweight tool for capturing workflow tasks' performance, including performance metrics from Nvidia GPUs. https://escholarship.org/uc/item/7bd612c9 Sat, 19 Aug 2023 00:00:00 +0000 Casanova, Henri Deelman, Ewa Gesing, Sandra Hildreth, Michael Hudson, Stephen Koch, William Larson, Jeffrey McDowell, Mary Ann Meyers, Natalie Navarro, John-Luke Papadimitriou, George Tanaka, Ryan Taylor, Ian Thain, Douglas Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Filgueira, Rosa da Silva, Rafael Ferreira https://escholarship.org/uc/item/6pt626qh X-ray fluorescence tomography is based on the detection of fluorescence x-ray photons produced following x-ray absorption while a specimen is rotated; it provides information on the 3D distribution of selected elements within a sample. One limitation in the quality of sample recovery is the separation of elemental signals due to the finite energy resolution of the detector. Another limitation is the effect of self-absorption, which can lead to inaccurate results with dense samples. To recover a higher quality elemental map, we combine x-ray fluorescence detection with a second data modality: conventional x-ray transmission tomography using absorption. By using these combined signals in a nonlinear optimization-based approach, we demonstrate the benefit of our algorithm on real experimental data and obtain an improved quantitative reconstruction of the spatial distribution of dominant elements in the sample. Compared with single-modality inversion based on x-ray fluorescence alone,... https://escholarship.org/uc/item/6pt626qh Sat, 19 Aug 2023 00:00:00 +0000 Di, Zichao Wendy Chen, Si Hong, Young Pyo Jacobsen, Chris Leyffer, Sven Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/63z6p1mv A roadmap is developed that integrates simulation methodology and data science methods to target new theories that traverse the multiple length- and time-scale features of many-body phenomena. https://escholarship.org/uc/item/63z6p1mv Sat, 19 Aug 2023 00:00:00 +0000 Clark, Aurora E Adams, Henry Hernandez, Rigoberto Krylov, Anna I Niklasson, Anders MN Sarupria, Sapna Wang, Yusu https://orcid.org/0000-0001-7950-4348 Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Yang, Qian https://escholarship.org/uc/item/3xk50079 We describe the Bayesian analysis of nuclear dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology’s ability to leverage insights from multiple models. In order to facilitate understanding of these tools, we provide a simple and accessible example of the BAND framework’s application. Four case studies are presented to highlight how elements of the framework will enable progress in complex, far-ranging problems in nuclear physics (NP). By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the NP and statistics communities can contribute to and build upon the BAND framework. https://escholarship.org/uc/item/3xk50079 Sat, 19 Aug 2023 00:00:00 +0000 Phillips, DR Furnstahl, RJ Heinz, U Maiti, T Nazarewicz, W Nunes, FM Plumlee, M Pratola, MT Pratt, S Viens, FG Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/3dn1k3g9 Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recognizing these tradeoffs as well as greater user functionality in a near-term exascale computing era, we present libEnsemble, a library aimed at particular scalability- and capability-stretching uses. libEnsemble enables running concurrent instances of an application in dynamically allocated ensembles through an extensible Python library. We highlight the structure, execution, and capabilities of the library on leading pre-exascale environments as well as advanced capabilities for exascale environments and beyond. https://escholarship.org/uc/item/3dn1k3g9 Sat, 19 Aug 2023 00:00:00 +0000 Hudson, Stephen Larson, Jeffrey Navarro, John-Luke Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/2d43x0c7 Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021. https://escholarship.org/uc/item/2d43x0c7 Sat, 19 Aug 2023 00:00:00 +0000 Buluc, Aydin Kolda, Tamara G Wild, Stefan M https://orcid.org/0000-0002-6099-2772 Anitescu, Mihai DeGennaro, Anthony Jakeman, John Kamath, Chandrika Kannan, Ramakrishnan Lopes, Miles E Martinsson, Per-Gunnar Myers, Kary Nelson, Jelani Restrepo, Juan M Seshadhri, C Vrabie, Draguna Wohlberg, Brendt Wright, Stephen J Yang, Chao https://orcid.org/0000-0001-7172-7539 Zwart, Peter https://escholarship.org/uc/item/0nk806k1 In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process (GP), we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of GPs. We demonstrate how uncertainty on GP hyperparameters may be propagated to uncertainty on the sensitivity... https://escholarship.org/uc/item/0nk806k1 Sat, 19 Aug 2023 00:00:00 +0000 Wycoff, Nathan Binois, Mickaël Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/06t0m88v X-ray ptychography is becoming the standard method for sub-30 nm imaging of thick extended samples. Available algorithms and computing power have traditionally restricted sample reconstruction to 2D slices. We build on recent progress in optimization algorithms and high performance computing to solve the ptychographic phase retrieval problem directly in 3D. Our approach addresses samples that do not fit entirely within the depth of focus of the imaging system. Such samples pose additional challenges because of internal diffraction effects within the sample. We demonstrate our approach on a computational sample modeled with 17 million complex variables. https://escholarship.org/uc/item/06t0m88v Sat, 19 Aug 2023 00:00:00 +0000 Gilles, MA Nashed, YSG DU, M Jacobsen, C Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/02x259pf Randomized Algorithms for Scientific Computing (RASC) https://escholarship.org/uc/item/02x259pf Sat, 19 Aug 2023 00:00:00 +0000 Buluc, Aydin Kolda, Tamara Wild, Stefan https://orcid.org/0000-0002-6099-2772 Anitescu, Mihai Degennaro, Anthony Jakeman, John Kamath, Chandrika Kannan, Ramakrishnan https://orcid.org/0000-0002-5852-4806 Lopes, Miles Martinsson, Per-Gunnar Myers, Kary Nelson, Jelani Restrepo, Juan https://orcid.org/0000-0003-2609-2882 Seshadri, C Vrabie, Draguna Wohlberg, Brendt Wright, Stephen Yang, Chao Zwart, Peter https://escholarship.org/uc/item/9pv8v4s1 Breakup reactions are one of the favored probes to study loosely bound nuclei, particularly those in the limit of stability forming a halo. In order to interpret such breakup experiments, the continuum discretized coupled channel method is typically used. In this study, the first Bayesian analysis of a breakup reaction model is performed. We use a combination of statistical methods together with a three-body reaction model (the continuum discretized coupled channel method) to quantify the uncertainties on the breakup observables due to the parameters in the effective potential describing the loosely bound projectile of interest. The combination of tools we develop opens the path for a Bayesian analysis of not only breakup processes, but also a wide array of complex processes that require computationally intensive reaction models. https://escholarship.org/uc/item/9pv8v4s1 Fri, 18 Aug 2023 00:00:00 +0000 Sürer, Ö Nunes, FM Plumlee, M Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/9p57606c We develop and solve a constrained optimization model to identify an integrable optics rapid-cycling synchrotron lattice design that performs well in several capacities. Our model encodes the design criteria into 78 linear and nonlinear constraints, as well as a single nonsmooth objective, where the objective and some constraints are defined from the output of Synergia, an accelerator simulator. We detail the difficulties of optimizing within the 32-dimensional, simulation-constrained decision space and establish that the space is nonempty. We use a derivative-free manifold sampling algorithm to account for structured nondifferentiability in the objective function. Our numerical results quantify the dependence of approximate solutions on constraint parameters and the effect of the form of objective function. https://escholarship.org/uc/item/9p57606c Fri, 18 Aug 2023 00:00:00 +0000 Eldred, Jeffrey S Larson, Jeffrey Padidar, Misha Stern, Eric Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/7cs6h5sd Plasma-based acceleration has emerged as a promising candidate as an accelerator technology for a future linear collider or a next-generation light source. We consider the plasma wakefield accelerator (PWFA) concept where a plasma wave wake is excited by a particle beam and a trailing beam surfs on the wake. For a linear collider, the energy transfer efficiency from the drive beam to the wake and from the wake to the trailing beam must be large, while the emittance and energy spread of the trailing bunch must be preserved. One way to simultaneously achieve this when accelerating electrons is to use longitudinally shaped bunches and nonlinear wakes. In the linear regime, there is an analytical formalism to obtain the optimal shapes. In the nonlinear regime, however, the optimal shape of the driver to maximize the energy transfer efficiency cannot be precisely obtained because currently no theory describes the wake structure and excitation process for all degrees of nonlinearity.... https://escholarship.org/uc/item/7cs6h5sd Fri, 18 Aug 2023 00:00:00 +0000 Su, Q Larson, J Dalichaouch, TN Li, F An, W Hildebrand, L Zhao, Y Decyk, V Alves, P Wild, SM https://orcid.org/0000-0002-6099-2772 Mori, WB https://escholarship.org/uc/item/72q0r6wd Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model. https://escholarship.org/uc/item/72q0r6wd Fri, 18 Aug 2023 00:00:00 +0000 Sürer, Özge Plumlee, Matthew Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/69v071q7 Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential “flattening” of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter-tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless... https://escholarship.org/uc/item/69v071q7 Fri, 18 Aug 2023 00:00:00 +0000 Slattery, Lucas Shaydulin, Ruslan Chakrabarti, Shouvanik Pistoia, Marco Khairy, Sami Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/67t402mh ParMOO: A Python library for parallel multiobjectivesimulation optimization https://escholarship.org/uc/item/67t402mh Fri, 18 Aug 2023 00:00:00 +0000 Chang, Tyler H Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/51r0m427 Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of quantum machine learning models is expected to be crucial given the central role hyperparameters play in determining the performance of classical machine learning methods. In this work we introduce the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical data sets to show consistent change in the model behavior from underfitting (bandwidth too large) to overfitting (bandwidth too small), with optimal generalization in between. We draw a connection between the bandwidth of classical and quantum kernels and show analogous behavior in both cases. Furthermore, we show that optimizing the bandwidth can help mitigate the exponential decay of kernel values... https://escholarship.org/uc/item/51r0m427 Fri, 18 Aug 2023 00:00:00 +0000 Shaydulin, Ruslan Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/4zg0g0zw Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from... https://escholarship.org/uc/item/4zg0g0zw Fri, 18 Aug 2023 00:00:00 +0000 Ćiprijanović, A Lewis, A Pedro, K Madireddy, S Nord, B Perdue, GN Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/4d03f284 We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated. Our method reformulates the unrelaxable bound-constrained problem as an unconstrained optimization problem that is amenable to existing unconstrained optimization methods. The reformulation relies on a domain warping to form a merit function; the choice of the warping determines the level of exactness with which the unconstrained problem can be used to find solutions to the bound-constrained problem, as well as key properties of the unconstrained formulation such as smoothness. We develop theory when the domain warping is a multioutput sigmoidal warping, and we explore the practical elements of applying unconstrained optimization methods to the formulation. We develop an algorithm that exploits the structure of the sigmoidal warping to guarantee that unconstrained optimization algorithms applied to the... https://escholarship.org/uc/item/4d03f284 Fri, 18 Aug 2023 00:00:00 +0000 Larson, Jeffrey Padidar, Misha Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/315614hd We consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required. https://escholarship.org/uc/item/315614hd Fri, 18 Aug 2023 00:00:00 +0000 Bollapragada, Raghu Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/23r0f11d Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output... https://escholarship.org/uc/item/23r0f11d Fri, 18 Aug 2023 00:00:00 +0000 Chan, Moses Y-H Plumlee, Matthew Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/0128899w With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations,... https://escholarship.org/uc/item/0128899w Fri, 18 Aug 2023 00:00:00 +0000 Ćiprijanović, Aleksandra Kafkes, Diana Snyder, Gregory Sánchez, F Javier Perdue, Gabriel Nathan Pedro, Kevin Nord, Brian Madireddy, Sandeep Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/5f119462 The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences... https://escholarship.org/uc/item/5f119462 Tue, 1 Aug 2023 00:00:00 +0000 Shannigrahi, Susmit Papadopoulos, Christos Yeh, Edmund Newman, Harvey Barczyk, Artur Jerzy Liu, Ran Sim, Alex https://orcid.org/0000-0002-6295-1982 Mughal, Azher Monga, Inder https://orcid.org/0000-0003-4524-0457 Vlimant, Jean-Roch Wu, John https://orcid.org/0000-0002-6907-3393 https://escholarship.org/uc/item/1507s9df Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12. 3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files... https://escholarship.org/uc/item/1507s9df Tue, 20 Jun 2023 00:00:00 +0000 Sim, Caitlin Wu, Kesheng Sim, Alex https://orcid.org/0000-0002-6295-1982 Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 Würthwein, Frank Davila, Diego Newman, Harvey Balcas, Justas https://escholarship.org/uc/item/9371x126 Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models’ hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root... https://escholarship.org/uc/item/9371x126 Tue, 6 Jun 2023 00:00:00 +0000 Müller, Juliane Park, Jangho https://orcid.org/0000-0002-9901-8555 Sahu, Reetik https://orcid.org/0000-0003-0681-0509 Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Arora, Bhavna Faybishenko, Boris https://orcid.org/0000-0003-0085-8499 Agarwal, Deborah https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/8nb5w553 Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide... https://escholarship.org/uc/item/8nb5w553 Tue, 6 Jun 2023 00:00:00 +0000 Crystal-Ornelas, Robert Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 O’Ryan, Dylan Beilsmith, Kathleen Bond-Lamberty, Benjamin Boye, Kristin Burrus, Madison https://orcid.org/0000-0003-2296-4698 Cholia, Shreyas https://orcid.org/0000-0002-4775-8201 Christianson, Danielle S https://orcid.org/0000-0002-8663-7701 Crow, Michael Damerow, Joan https://orcid.org/0000-0003-2601-5043 Ely, Kim S Goldman, Amy E Heinz, Susan L Hendrix, Valerie C https://orcid.org/0000-0001-9061-8952 Kakalia, Zarine Mathes, Kayla O’Brien, Fianna Pennington, Stephanie C Robles, Emily https://orcid.org/0000-0003-3720-6566 Rogers, Alistair https://orcid.org/0000-0001-9262-7430 Simmonds, Maegen Velliquette, Terri Weisenhorn, Pamela Welch, Jessica Nicole Whitenack, Karen Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/4fv3m3z6 The Carbon Capture Simulation Initiative (CCSI) project has developed and deployed scientific infrastructure called the CCSI Toolset. The CCSI Toolset provides state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. Carbon capture technologies have the potential to dramatically reduce the carbon emissions from power plants. The CCSI Toolset provides end users in industry with a comprehensive, integrated suite of leading-edge, scientifically validated models with simulation, uncertainty quantification, optimization, risk analysis and decision making support. The CCSI Toolset has at its core an integrated framework that enables execution of simulations and workflows including optimization and uncertainty parameter sweeps using a wide variety of computing platforms including desktops, clusters,... https://escholarship.org/uc/item/4fv3m3z6 Tue, 6 Jun 2023 00:00:00 +0000 Cheah, You-Wei https://orcid.org/0000-0003-2241-4901 Boverhof, Joshua https://orcid.org/0000-0003-2553-6613 Elbashandy, Abdelrahman Agarwal, Deb https://orcid.org/0000-0001-5045-2396 Leek, Jim Epperly, Thomas Eslick, John Miller, David https://escholarship.org/uc/item/01h5s1jc Identifying Time Series Similarity in Large-Scale Earth System Datasets https://escholarship.org/uc/item/01h5s1jc Tue, 6 Jun 2023 00:00:00 +0000 Linton, Payton Melodia, William Lazar, Alina Agarwal, Deborah https://orcid.org/0000-0001-5045-2396 Bianchi, Ludovico Ghoshal, Devarshi https://orcid.org/0000-0002-6819-6949 Wu, keshang Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 Ramakrishnan, Lavanya https://escholarship.org/uc/item/63f2185k We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap... https://escholarship.org/uc/item/63f2185k Mon, 5 Jun 2023 00:00:00 +0000 Park, Jangho Müller, Juliane Arora, Bhavna Faybishenko, Boris https://orcid.org/0000-0003-0085-8499 Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Sahu, Reetik Agarwal, Deborah https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/3ks4r91k The XRootD system is used to transfer, store, and cache large datasets from high-energy physics (HEP). In this study we focus on its capability as distributed on-demand storage cache. Through exploring a large set of daily log files between 2020 and 2021, we seek to understand the data access patterns that might inform future cache design. Our study begins with a set of summary statistics regarding file read operations, file lifetimes, and file transfers. We observe that the number of read operations on each file remains nearly constant, while the average size of a read operation grows over time. Furthermore, files tend to have a consistent length of time during which they remain open and are in use. Based on this comprehensive study of the cache access statistics, we developed a cache simulator to explore the behavior of caches of different sizes. Within a certain size range, we find that increasing the XRootD cache size improves the cache hit rate, yielding faster overall file... https://escholarship.org/uc/item/3ks4r91k Tue, 28 Feb 2023 00:00:00 +0000 Bellavita, Julian Sim, Alex https://orcid.org/0000-0002-6295-1982 Wu, Kesheng Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 Würthwein, Frank Davila, Diego https://escholarship.org/uc/item/03r8w0sb Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user communities. Each user in the community often selects a different subset of data for their analysis tasks; however, members of a research group often are working on related research topics that require similar data objects. Thus, there is a significant amount of data sharing possible. In this work, we study the access traces of a federated storage cache known as the Southern California Petabyte Scale Cache. By studying the access patterns and potential for network traffic reduction by this caching system, we aim to explore the predictability of the cache uses and the potential for a more general in-network data caching. Our study shows that this distributed storage cache is able to reduce the network traffic volume by a factor of 2.35 during a part of the study period. We further show that machine learning... https://escholarship.org/uc/item/03r8w0sb Tue, 28 Feb 2023 00:00:00 +0000 Han, Ruize Sim, Alex https://orcid.org/0000-0002-6295-1982 Wu, Kesheng Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 Würthwein, Frank Davila, Diego Balcas, Justas Newman, Harvey https://escholarship.org/uc/item/1qs17737 Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many... https://escholarship.org/uc/item/1qs17737 Wed, 15 Feb 2023 00:00:00 +0000 Brantley, Susan L Wen, Tao Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 Catalano, Jeffrey G Schroeder, Paul A Lehnert, Kerstin Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Pett-Ridge, Julie Engle, Mark Castronova, Anthony M Hooper, Richard P Ma, Xiaogang Jin, Lixin McHenry, Kenton Aronson, Emma https://orcid.org/0000-0002-5018-2688 Shaughnessy, Andrew R Derry, Louis A Richardson, Justin Bales, Jerad Pierce, Eric M https://escholarship.org/uc/item/3q12x1gh Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. https://escholarship.org/uc/item/3q12x1gh Tue, 14 Feb 2023 00:00:00 +0000 Martin, Hector G Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean https://orcid.org/0000-0003-3566-9719 Arnold, Dan https://orcid.org/0000-0001-8897-1132 Hillson, Nathan https://orcid.org/0000-0002-9169-3978 Babnigg, Gyorgy Marti, Jose M Mungall, Christopher J Beckham, Gregg T Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb https://orcid.org/0000-0001-5045-2396 Simmons, Blake A https://orcid.org/0000-0002-1918-3463 Backman, Tyler Banerjee, Deepanwita https://orcid.org/0000-0002-0083-0608 Tanjore, Deepti https://orcid.org/0000-0001-6507-4359 Ramakrishnan, Lavanya Singh, Anup https://escholarship.org/uc/item/2623q1rm We present the results of a National Science Foundation Project Scoping Workshop, the purpose of which was to assess the current status of calculations for the nuclear matrix elements governing neutrinoless double-beta decay and determine if more work on them is required. After reviewing important recent progress in the application of effective field theory, lattice quantum chromodynamics, and ab initio nuclear-structure theory to double-beta decay, we discuss the state of the art in nuclear-physics uncertainty quantification and then construct a roadmap for work in all these areas to fully complement the increasingly sensitive experiments in operation and under development. The roadmap includes specific projects in theoretical and computational physics as well as the use of Bayesian methods to quantify both intra- and inter-model uncertainties. The goal of this ambitious program is a set of accurate and precise matrix elements, in all nuclei of interest to experimentalists, delivered... https://escholarship.org/uc/item/2623q1rm Tue, 14 Feb 2023 00:00:00 +0000 Cirigliano, V Davoudi, Z Engel, J Furnstahl, RJ Hagen, G Heinz, U Hergert, H Horoi, M Johnson, CW Lovato, A Mereghetti, E Nazarewicz, W Nicholson, A Papenbrock, T Pastore, S Plumlee, M Phillips, DR Shanahan, PE Stroberg, SR Viens, F Walker-Loud, A https://orcid.org/0000-0002-4686-3667 Wendt, KA Wild, SM https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/6vb1r6c3 Reacting flow simulations for combustion applications require extensive computing capabilities. Leveraging the AMReX library, the Pele suite of combustion simulation tools targets the largest supercomputers available and future exascale machines. We introduce PeleC, the compressible solver in the Pele suite, and detail its capabilities, including complex geometry representation, chemistry integration, and discretization. We present a comparison of development efforts using both OpenACC and AMReX’s C++ performance portability framework for execution on multiple GPU architectures. We discuss relevant details that have allowed PeleC to achieve high performance and scalability. PeleC’s performance characteristics are measured through relevant simulations on multiple supercomputers. The success of PeleC’s design for exascale is exhibited through demonstration of a 160 billion cell simulation and weak scaling onto 100% of Summit, an NVIDIA-based GPU supercomputer at Oak Ridge National... https://escholarship.org/uc/item/6vb1r6c3 Fri, 9 Dec 2022 00:00:00 +0000 de Frahan, Marc T Henry Rood, Jon S Day, Marc S Sitaraman, Hariswaran Yellapantula, Shashank Perry, Bruce A Grout, Ray W Almgren, Ann Zhang, Weiqun https://orcid.org/0000-0001-8092-1974 Bell, John B Chen, Jacqueline H https://escholarship.org/uc/item/9wp5j0dq Nuclear Physics Exascale Requirements Review: An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Nuclear Physics, June 15 - 17, 2016, Gaithersburg, Maryland https://escholarship.org/uc/item/9wp5j0dq Tue, 24 May 2022 00:00:00 +0000 Carlson, Joseph Savage, Martin J Gerber, Richard https://orcid.org/0000-0002-1002-5688 Antypas, Katie Bard, Deborah Coffey, Richard Dart, Eli Dosanjh, Sudip Hack, James Monga, Inder Papka, Michael E Riley, Katherine Rotman, Lauren Straatsma, Tjerk Wells, Jack Avakian, Harut Ayyad, Yassid Bass, Steffen A Bazin, Daniel Boehnlein, Amber Bollen, Georg Broussard, Leah J Calder, Alan Couch, Sean Couture, Aaron Cromaz, Mario Detmold, William Detwiler, Jason Duan, Huaiyu Edwards, Robert Engel, Jonathan Fryer, Chris Fuller, George M Gandolfi, Stefano Gavalian, Gagik Georgobiani, Dali Gupta, Rajan Gyurjyan, Vardan Hausmann, Marc Heyes, Graham Hix, W Ralph ito, Mark Jansen, Gustav Jones, Richard Joo, Balint Kaczmarek, Olaf Kasen, Dan Kostin, Mikhail Kurth, Thorsten Lauret, Jerome Lawrence, David Lin, Huey-Wen Lin, Meifeng Mantica, Paul Maris, Peter Messer, Bronson Mittig, Wolfgang Mosby, Shea Mukherjee, Swagato Nam, Hai Ah https://orcid.org/0000-0003-4892-6286 navratil, Petr Nazarewicz, Witek Ng, Esmond O'Donnell, Tommy Orginos, Konstantinos Pellemoine, Frederique Petreczky, Peter Pieper, Steven C Pinkenburg, Christopher H Plaster, Brad Porter, R Jefferson Portillo, Mauricio Pratt, Scott Purschke, Martin L Qiang, Ji Quaglioni, Sofia Richards, David Roblin, Yves Schenke, Bjorn Schiavilla, Rocco Schlichting, Soren Schunck, Nicolas Steinbrecher, Patrick Strickland, Michael Syritsyn, Sergey Terzic, Balsa Varner, Robert Vary, James Wild, Stefan https://orcid.org/0000-0002-6099-2772 Winter, Frank Zegers, Remco Zhang, He Ziegler, Veronique Zingale, Michael https://escholarship.org/uc/item/8kb0h1gb Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable,... https://escholarship.org/uc/item/8kb0h1gb Tue, 24 May 2022 00:00:00 +0000 Simmonds, Maegen B Riley, William J Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 Chen, Xingyuan Cholia, Shreyas https://orcid.org/0000-0002-4775-8201 Crystal-Ornelas, Robert https://orcid.org/0000-0002-6339-1139 Coon, Ethan T Dwivedi, Dipankar Hendrix, Valerie C https://orcid.org/0000-0001-9061-8952 Huang, Maoyi Jan, Ahmad Kakalia, Zarine Kumar, Jitendra Koven, Charles D https://orcid.org/0000-0002-3367-0065 Li, Li Melara, Mario Ramakrishnan, Lavanya Ricciuto, Daniel M Walker, Anthony P Zhi, Wei Zhu, Qing Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 https://escholarship.org/uc/item/1ds6v8ww Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework... https://escholarship.org/uc/item/1ds6v8ww Tue, 24 May 2022 00:00:00 +0000 Faybishenko, B https://orcid.org/0000-0003-0085-8499 Versteeg, R Pastorello, G https://orcid.org/0000-0002-9387-3702 Dwivedi, D Varadharajan, C https://orcid.org/0000-0002-4142-3224 Agarwal, D https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/97d284ps Machine learning-based Analysis of COVID-19 Pandemic Impact on US Research Networks https://escholarship.org/uc/item/97d284ps Fri, 18 Feb 2022 00:00:00 +0000 Kiran, Mariam Campbell, Scott https://orcid.org/0000-0002-6542-7473 Wala, Fatema Bannat Buraglio, Nick Monga, Inder https://orcid.org/0000-0003-4524-0457 https://escholarship.org/uc/item/36n6f5xk This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-At-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends. https://escholarship.org/uc/item/36n6f5xk Fri, 18 Feb 2022 00:00:00 +0000 Kiran, Mariam Campbell, Scott https://orcid.org/0000-0002-6542-7473 Wala, Fatema Bannat Buraglio, Nick Monga, Inder https://orcid.org/0000-0003-4524-0457 https://escholarship.org/uc/item/3nw2w949 A principal component analysis (PCA) and artificial neural network (ANN) based chemistry tabulation approach is presented. ANNs are used to map the thermochemical state onto a low-dimensional manifold consisting of five control variables that have been identified using PCA. Three canonical configurations are considered to train the PCA-ANN model: a series of homogeneous reactors, a nonpremixed flamelet, and a two-dimensional lifted flame. The performance of the model in predicting the thermochemical manifold of a spatially-developing turbulent jet flame in diesel engine thermochemical conditions is a priori evaluated using direct numerical simulation (DNS) data. The PCA-ANN approach is compared with a conventional tabulation approach (tabulation using ad hoc defined control variables and linear interpolation). The PCA-ANN model provides higher accuracy and requires several orders of magnitude less memory. These observations indicate that the PCA-ANN model is superior for chemistry... https://escholarship.org/uc/item/3nw2w949 Mon, 14 Feb 2022 00:00:00 +0000 Dalakoti, Deepak K Wehrfritz, Armin Savard, Bruno Day, Marc S Bell, John B Hawkes, Evatt R https://escholarship.org/uc/item/6tp8n25g Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological... https://escholarship.org/uc/item/6tp8n25g Tue, 18 Jan 2022 00:00:00 +0000 Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Hendrix, Valerie C https://orcid.org/0000-0001-9061-8952 Christianson, Danielle S https://orcid.org/0000-0002-8663-7701 Burrus, Madison https://orcid.org/0000-0003-2296-4698 Wong, Catherine Hubbard, Susan S Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/35z8t8gn The U.S. Department of Energy Computational Graduate Fellowship Program, celebrating 30 years of existence in 2021, is one of the most successful graduate fellowships in the world as well as one of the longest running programs in the U.S. Department of Energy. This article discusses the conception, early years and evolution of the fellowship over the past thirty years. https://escholarship.org/uc/item/35z8t8gn Tue, 23 Nov 2021 00:00:00 +0000 Brown, David https://orcid.org/0000-0002-9505-2959 Hack, James Voigt, Robert https://escholarship.org/uc/item/91f9q777 Analyzing Scientific Data Sharing Patterns for In-network Data Caching. https://escholarship.org/uc/item/91f9q777 Tue, 26 Oct 2021 00:00:00 +0000 Copps, Elizabeth Zhang, Huiyi Sim, Alex https://orcid.org/0000-0002-6295-1982 Wu, Kesheng Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 Würthwein, Frank Davila, Diego Hernandez, Edgar Fajardo https://escholarship.org/uc/item/61j6m742 2019 Computing Sciences Strategic Plan https://escholarship.org/uc/item/61j6m742 Tue, 26 Oct 2021 00:00:00 +0000 Yelick, Kathy Agarwal, Deb https://orcid.org/0000-0001-5045-2396 Bard, Debbie https://orcid.org/0000-0002-5162-5153 Shalf, John https://orcid.org/0000-0002-0608-3690 Almgren, Ann Bhimji, Wahid Brown, Ben Carter, Jonathan https://orcid.org/0000-0001-9006-7636 Jong, Bert Doerfler, Doug https://orcid.org/0000-0001-5016-8854 Donofrio, David Guok, Chin https://orcid.org/0000-0003-4532-1222 Iancu, Costin Kiran, Mariam Li, Sherry Nugent, Peter https://orcid.org/0000-0002-3389-0586 Prabhat, M Ramakrishnan, Lavanya Vasudevan, Dilip Wright, Nick https://orcid.org/0000-0003-1883-6108 Cademartori, Helen Antypas, Katie Kincade, Kathy https://escholarship.org/uc/item/5dk3195q The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of 'big data' driven by Exascale, cloud computing, machine learning and AI. The project's architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive 'intent' based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a first-class schedulable resource akin to instruments, compute, and storage, to enable well defined... https://escholarship.org/uc/item/5dk3195q Tue, 26 Oct 2021 00:00:00 +0000 Monga, Inder https://orcid.org/0000-0003-4524-0457 Yang, Xi Guok, Chin https://orcid.org/0000-0003-4532-1222 MacAuley, John Sim, Alex https://orcid.org/0000-0002-6295-1982 Newman, Harvey Balcas, Justas DeMar, Phil Winkler, Linda Lehman, Tom https://escholarship.org/uc/item/2jp1s60h Abstract The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological,... https://escholarship.org/uc/item/2jp1s60h Wed, 4 Aug 2021 00:00:00 +0000 Kakalia, Zarine Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Alper, Erek Brodie, Eoin L https://orcid.org/0000-0002-8453-8435 Burrus, Madison https://orcid.org/0000-0003-2296-4698 Carroll, Rosemary WH Christianson, Danielle S https://orcid.org/0000-0002-8663-7701 Dong, Wenming https://orcid.org/0000-0003-2074-8887 Hendrix, Valerie C https://orcid.org/0000-0001-9061-8952 Henderson, Matthew Hubbard, Susan S Johnson, Douglas Versteeg, Roelof Williams, Kenneth H https://orcid.org/0000-0002-3568-1155 Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/7298g7m9 Abstract Data standardization combined with descriptive metadata facilitate data reuse, which is the ultimate goal of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Community data or metadata standards are increasingly created through an approach that emphasizes collaboration between various stakeholders. Such an approach requires platforms for collaboration on the development process that centers on sharing information and receiving feedback. Our objective in this study was to conduct a systematic review to identify data standards and reporting formats that use version control for developing data standards and to summarize common practices, particularly in earth and environmental sciences. Out of 108 data standards and reporting formats identified in our review, 32 used GitHub as the version control platform, and no other platforms were used. We found no universally accepted methodology for developing and publishing data standards. Many GitHub repositories... https://escholarship.org/uc/item/7298g7m9 Thu, 22 Jul 2021 00:00:00 +0000 Crystal‐Ornelas, Robert Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Bond‐Lamberty, Ben Boye, Kristin Burrus, Madison https://orcid.org/0000-0003-2296-4698 Cholia, Shreyas https://orcid.org/0000-0002-4775-8201 Crow, Michael Damerow, Joan https://orcid.org/0000-0003-2601-5043 Devarakonda, Ranjeet Ely, Kim S Goldman, Amy Heinz, Susan Hendrix, Valerie https://orcid.org/0000-0001-9061-8952 Kakalia, Zarine Pennington, Stephanie C Robles, Emily https://orcid.org/0000-0003-3720-6566 Rogers, Alistair https://orcid.org/0000-0001-9262-7430 Simmonds, Maegen Velliquette, Terri Weierbach, Helen https://orcid.org/0000-0001-6348-9120 Weisenhorn, Pamela Welch, Jessica N Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/59m140sn Scientific data volumes have been growing expo-nentially. This has resulted in the need for new tools that enable users to operate on and analyze data. Cyberinfrastructure tools, including workflow tools, that have been developed in the last few years has often fallen short of user needs and suffered from lack of wider adoption. User-centered Design (UCD) process has been used as an effective approach to develop usable software with high adoption rates. However, UCD has largely been applied for user-interfaces and there has been limited work in applying UCD to application program interfaces and cyberinfrastructure tools. We use an adapted version of UCD that we refer to as Scientist-Centered Design (SCD) to engage with users in the design and development of Tigres, a workflow application programming interface. Tigres provides a simple set of programming templates (e.g., sequence, parallel, split, merge) that can be can used to compose and execute computational and data transformation... https://escholarship.org/uc/item/59m140sn Wed, 16 Jun 2021 00:00:00 +0000 Ramakrishnan, Lavanya Poon, Sarah Hendrix, Valerie https://orcid.org/0000-0001-9061-8952 Gunter, Daniel https://orcid.org/0000-0002-2779-2744 Pastorello, Gilberto Z https://orcid.org/0000-0002-9387-3702 Agarwal, Deborah https://orcid.org/0000-0001-5045-2396 https://escholarship.org/uc/item/1hs1q8xw The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data... https://escholarship.org/uc/item/1hs1q8xw Tue, 11 May 2021 00:00:00 +0000 Copps, Elizabeth Zhang, Huiyi Sim, Alex https://orcid.org/0000-0002-6295-1982 Wu, Kesheng Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 Würthwein, Frank Davila, Diego Fajardo, Edgar https://escholarship.org/uc/item/3nx4d18c A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9. https://escholarship.org/uc/item/3nx4d18c Tue, 30 Mar 2021 00:00:00 +0000 Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 Trotta, Carlo Canfora, Eleonora Chu, Housen https://orcid.org/0000-0002-8131-4938 Christianson, Danielle https://orcid.org/0000-0002-8663-7701 Cheah, You-Wei https://orcid.org/0000-0003-2241-4901 Poindexter, Cristina Chen, Jiquan Elbashandy, Abdelrahman Humphrey, Marty Isaac, Peter Polidori, Diego Reichstein, Markus Ribeca, Alessio van Ingen, Catharine Vuichard, Nicolas Zhang, Leiming Amiro, Brian Ammann, Christof Arain, M Altaf Ardö, Jonas Arkebauer, Timothy Arndt, Stefan K Arriga, Nicola Aubinet, Marc Aurela, Mika Baldocchi, Dennis https://orcid.org/0000-0003-3496-4919 Barr, Alan Beamesderfer, Eric Marchesini, Luca Belelli Bergeron, Onil Beringer, Jason Bernhofer, Christian Berveiller, Daniel Billesbach, Dave Black, Thomas Andrew Blanken, Peter D Bohrer, Gil Boike, Julia Bolstad, Paul V Bonal, Damien Bonnefond, Jean-Marc Bowling, David R Bracho, Rosvel Brodeur, Jason Brümmer, Christian Buchmann, Nina Burban, Benoit Burns, Sean P Buysse, Pauline Cale, Peter Cavagna, Mauro Cellier, Pierre Chen, Shiping Chini, Isaac Christensen, Torben R Cleverly, James Collalti, Alessio Consalvo, Claudia Cook, Bruce D Cook, David Coursolle, Carole Cremonese, Edoardo Curtis, Peter S D’Andrea, Ettore da Rocha, Humberto Dai, Xiaoqin Davis, Kenneth J De Cinti, Bruno de Grandcourt, Agnes De Ligne, Anne De Oliveira, Raimundo C Delpierre, Nicolas Desai, Ankur R Di Bella, Carlos Marcelo di Tommasi, Paul Dolman, Han Domingo, Francisco Dong, Gang Dore, Sabina Duce, Pierpaolo Dufrêne, Eric Dunn, Allison Dušek, Jiří Eamus, Derek Eichelmann, Uwe ElKhidir, Hatim Abdalla M Eugster, Werner Ewenz, Cacilia M Ewers, Brent Famulari, Daniela Fares, Silvano Feigenwinter, Iris Feitz, Andrew Fensholt, Rasmus Filippa, Gianluca Fischer, Marc Frank, John Galvagno, Marta Gharun, Mana https://escholarship.org/uc/item/5f87m12s Summary Scientific datasets are growing rapidly and becoming critical to next‐generation scientific discoveries. The validity of scientific results relies on the quality of data used and data are often subject to change, for example, due to observation additions, quality assessments, or processing software updates. The effects of data change are not well understood and difficult to predict. Datasets are often repeatedly updated and recomputing derived data products quickly becomes time consuming and resource intensive and may in some cases not even be necessary, thus delaying scientific advance. Despite its importance, there is a lack of systematic approaches for best comparing data versions to quantify the changes, and ad‐hoc or manual processes are commonly used. In this article, we propose a novel hierarchical approach for analyzing data changes, including real‐time (online) and offline analyses. We employ a variety of fast‐to‐compute numerical analyses, graphical data change... https://escholarship.org/uc/item/5f87m12s Thu, 18 Mar 2021 00:00:00 +0000 Müller, Juliane Faybishenko, Boris https://orcid.org/0000-0003-0085-8499 Agarwal, Deborah https://orcid.org/0000-0001-5045-2396 Bailey, Stephen https://orcid.org/0000-0003-4162-6619 Jiang, Chongya Ryu, Youngryel Tull, Craig Ramakrishnan, Lavanya https://escholarship.org/uc/item/5dq7547j Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of both data producers and consumers, the most pressing need is to create user-friendly solutions that provide credit for data producers and enable accurate citation of data, particularly integrated data. Providing easy-to-use data citations is a critical foundation that is required to address the socio-technical challenges around data integration. Studies that integrate data from dozens or hundreds... https://escholarship.org/uc/item/5dq7547j Wed, 10 Mar 2021 00:00:00 +0000 Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 Damerow, Joan https://orcid.org/0000-0003-2601-5043 Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Christianson, Danielle S https://orcid.org/0000-0002-8663-7701 Pastorello, Gilberto Z https://orcid.org/0000-0002-9387-3702 Cheah, You-Wei https://orcid.org/0000-0003-2241-4901 Ramakrishnan, Lavanya https://escholarship.org/uc/item/2853b6wd Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories... https://escholarship.org/uc/item/2853b6wd Tue, 16 Feb 2021 00:00:00 +0000 Ely, Kim S Rogers, Alistair https://orcid.org/0000-0001-9262-7430 Agarwal, Deborah A https://orcid.org/0000-0001-5045-2396 Ainsworth, Elizabeth A Albert, Loren P Ali, Ashehad Anderson, Jeremiah Aspinwall, Michael J Bellasio, Chandra Bernacchi, Carl Bonnage, Steve Buckley, Thomas N https://orcid.org/0000-0001-7610-7136 Bunce, James Burnett, Angela C Busch, Florian A Cavanagh, Amanda Cernusak, Lucas A Crystal-Ornelas, Robert https://orcid.org/0000-0002-6339-1139 Damerow, Joan https://orcid.org/0000-0003-2601-5043 Davidson, Kenneth J De Kauwe, Martin G Dietze, Michael C Domingues, Tomas F Dusenge, Mirindi Eric Ellsworth, David S Evans, John R Gauthier, Paul PG Gimenez, Bruno O Gordon, Elizabeth P Gough, Christopher M Halbritter, Aud H Hanson, David T Heskel, Mary Hogan, J Aaron Hupp, Jason R Jardine, Kolby https://orcid.org/0000-0001-8491-9310 Kattge, Jens Keenan, Trevor https://orcid.org/0000-0002-3347-0258 Kromdijk, Johannes Kumarathunge, Dushan P Lamour, Julien Leakey, Andrew DB LeBauer, David S Li, Qianyu Lundgren, Marjorie R McDowell, Nate Meacham-Hensold, Katherine Medlyn, Belinda E Moore, David JP Negrón-Juárez, Robinson Niinemets, Ülo Osborne, Colin P Pivovaroff, Alexandria L Poorter, Hendrik Reed, Sasha C Ryu, Youngryel Sanz-Saez, Alvaro Schmiege, Stephanie C Serbin, Shawn P Sharkey, Thomas D Slot, Martijn Smith, Nicholas G Sonawane, Balasaheb V South, Paul F Souza, Daisy C Stinziano, Joseph Ronald Stuart-Haëntjens, Ellen Taylor, Samuel H Tejera, Mauricio D Uddling, Johan Vandvik, Vigdis Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 Walker, Anthony P Walker, Berkley J Warren, Jeffrey M Way, Danielle A Wolfe, Brett T Wu, Jin Wullschleger, Stan D Xu, Chonggang https://orcid.org/0000-0002-0937-5744 Yan, Zhengbing Yang, Dedi https://escholarship.org/uc/item/4sf7n3pc The Energy Sciences Network (ESnet) is the high-performance network user facility for the U.S. Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the United States and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE National Laboratories, User Facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large data sets, and access distributed data repositories.... https://escholarship.org/uc/item/4sf7n3pc Thu, 14 Jan 2021 00:00:00 +0000 Arrington, John https://orcid.org/0000-0002-0702-1328 Brown, Ben Beher, Steve Cromaz, Mario Dart, Eli https://orcid.org/0000-0002-8229-5433 Edwards, Robert Heyes, Graham Jones, Clinton Lauret, Jerome Liddick, Sean Mantica, Paul Melo, Andrew Monga, Inder https://orcid.org/0000-0003-4524-0457 Morris, Brent Pinkenburg, Chris Porter, Jeff Rai, Gulshan Rockwell, Thomas Rotman, Lauren Simon, Richard Stromsness, Rune Wefel, Paul Wiedlea, Andrew Wilkinson, Sean Winkler, Linda Zurawski, Jason https://orcid.org/0000-0001-8389-4705 https://escholarship.org/uc/item/60k9g6d1 This paper describes some high-order collocation-like methods for the numerical solution of stiff boundary-value problems with turning points. The presentation concentrates on the implementation of these methods in conjunction with the implementation of the a priori mesh construction algorithm introduced by Kreiss, Nichols and Brown [SIAM J. Numer. Anal., 23 (1986), pp. 325-368] for such problems. Numerical examples are given showing the high accuracy which can be obtained in solving the boundary value problem for singularly perturbed ordinary differential equations with turning points. https://escholarship.org/uc/item/60k9g6d1 Mon, 7 Dec 2020 00:00:00 +0000 Brown, David https://orcid.org/0000-0002-9505-2959 Lorenz, Jens https://escholarship.org/uc/item/61q1d1bk Software-Defined Network for End-to-end Networked Science at the Exascale. https://escholarship.org/uc/item/61q1d1bk Tue, 1 Sep 2020 00:00:00 +0000 Monga, Inder https://orcid.org/0000-0003-4524-0457 Guok, Chin https://orcid.org/0000-0003-4532-1222 MacAuley, John Sim, Alex https://orcid.org/0000-0002-6295-1982 Newman, Harvey Balcas, Justas DeMar, Phil Winkler, Linda Lehman, Tom Yang, Xi https://escholarship.org/uc/item/4d82175t Quality metadata and data are critical to advancing science and preserving data for long-term use. The Next Generation Ecosystem Experiments (NGEE) Tropics project funded by the U.S. Department of Energy generates and utilizes ecological, hydrological, and meteorological data from tropical forests for scientific analysis and model parameterization. The project’s data team manages an archive for users to internally curate and publish data with a digital object identifier (DOI). A key focus of our project is to ensure NGEE Tropics data can be interpreted and utilized by current and future research teams. However, the education and participation of project members to prioritize and be involved in data curation is necessary to reach this goal. We have taken an interdisciplinary approach involving domain and data scientists to create a process that makes it easy for scientists to curate high-quality data packages for archival. First, the NGEE Tropics Archive and metadata reporting... https://escholarship.org/uc/item/4d82175t Fri, 17 Jul 2020 00:00:00 +0000 Robles, Emily https://orcid.org/0000-0003-3720-6566 Agarwal, Deb https://orcid.org/0000-0001-5045-2396 Christianson, Danielle https://orcid.org/0000-0002-8663-7701 Faybishenko, Boris https://orcid.org/0000-0003-0085-8499 Negron-Juarez, Robinson Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 Varadharajan, Charuleka https://orcid.org/0000-0002-4142-3224 https://escholarship.org/uc/item/2xf0f1dj The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed... https://escholarship.org/uc/item/2xf0f1dj Fri, 17 Jul 2020 00:00:00 +0000 Pastorello, Gilberto https://orcid.org/0000-0002-9387-3702 Trotta, Carlo Canfora, Eleonora Chu, Housen https://orcid.org/0000-0002-8131-4938 Christianson, Danielle https://orcid.org/0000-0002-8663-7701 Cheah, You-Wei https://orcid.org/0000-0003-2241-4901 Poindexter, Cristina Chen, Jiquan Elbashandy, Abdelrahman Humphrey, Marty Isaac, Peter Polidori, Diego Reichstein, Markus Ribeca, Alessio van Ingen, Catharine Vuichard, Nicolas Zhang, Leiming Amiro, Brian Ammann, Christof Arain, M Altaf Ardö, Jonas Arkebauer, Timothy Arndt, Stefan K Arriga, Nicola Aubinet, Marc Aurela, Mika Baldocchi, Dennis https://orcid.org/0000-0003-3496-4919 Barr, Alan Beamesderfer, Eric Marchesini, Luca Belelli Bergeron, Onil Beringer, Jason Bernhofer, Christian Berveiller, Daniel Billesbach, Dave Black, Thomas Andrew Blanken, Peter D Bohrer, Gil Boike, Julia Bolstad, Paul V Bonal, Damien Bonnefond, Jean-Marc Bowling, David R Bracho, Rosvel Brodeur, Jason Brümmer, Christian Buchmann, Nina Burban, Benoit Burns, Sean P Buysse, Pauline Cale, Peter Cavagna, Mauro Cellier, Pierre Chen, Shiping Chini, Isaac Christensen, Torben R Cleverly, James Collalti, Alessio Consalvo, Claudia Cook, Bruce D Cook, David Coursolle, Carole Cremonese, Edoardo Curtis, Peter S D’Andrea, Ettore da Rocha, Humberto Dai, Xiaoqin Davis, Kenneth J Cinti, Bruno De Grandcourt, Agnes de Ligne, Anne De De Oliveira, Raimundo C Delpierre, Nicolas Desai, Ankur R Di Bella, Carlos Marcelo Tommasi, Paul di Dolman, Han Domingo, Francisco Dong, Gang Dore, Sabina Duce, Pierpaolo Dufrêne, Eric Dunn, Allison Dušek, Jiří Eamus, Derek Eichelmann, Uwe ElKhidir, Hatim Abdalla M Eugster, Werner Ewenz, Cacilia M Ewers, Brent Famulari, Daniela Fares, Silvano Feigenwinter, Iris Feitz, Andrew Fensholt, Rasmus Filippa, Gianluca Fischer, Marc Frank, John Galvagno, Marta Gharun, Mana https://escholarship.org/uc/item/4mj3d1jb ESnet provides a platform for moving large data sets and accelerating worldwide scientific collaboration. It provides high-bandwidth, reliable connections that link scientists at national laboratories, universities and other research institutions, enabling them to collaborate on some of the world's most important scientific challenges including renewable energy sources, climate science, and the origins of the universe. ESnet has embarked on a major project to provide substantial visibility into the inner-workings of the network by aggregating diverse data sources, exposing them via web services, and visualizing them with user-centered interfaces. The portal’s strategy is driven by understanding the needs and requirements of ESnet’s user community and carefully providing interfaces to the data to meet those needs. The 'MyESnet Portal ' allows users to monitor, troubleshoot, and understand the real time operations of the network and its associated services. This paper will describe... https://escholarship.org/uc/item/4mj3d1jb Tue, 7 Jul 2020 00:00:00 +0000 Dugan, Jon Vaswani, Gopal Bell, Gregory Monga, Inder https://orcid.org/0000-0003-4524-0457 https://escholarship.org/uc/item/4z36d54q We are grateful for the many insightful comments provided by the discussants. One team politely pointed out oversights in our literature review and the subsequent omission of a formidable comparator. Another made an important clarification about when a more aggressive variation (the so-called NoMax) would perform poorly. A third team offered enhancements to the framework, including a derivation of closed-form expressions and a more aggressive updating scheme; these enhancements were supported by an empirical study comparing new alternatives with old. The last team suggested hybridizing the statistical augmented Lagrangian (AL) method with modern stochastic search. Here we present our responses to these contributions and detail some improvements made to our own implementations in light of them. We conclude with some thoughts on statistical optimization using surrogate modeling and open-source software. https://escholarship.org/uc/item/4z36d54q Sun, 14 Jun 2020 00:00:00 +0000 Gramacy, Robert B Gray, Genetha A Le Digabel, Sébastien Lee, Herbert KH Ranjan, Pritam Wells, Garth Wild, Stefan M https://orcid.org/0000-0002-6099-2772 https://escholarship.org/uc/item/8pj0n2v2 FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities. https://escholarship.org/uc/item/8pj0n2v2 Wed, 6 May 2020 00:00:00 +0000 Baldin, Ilya Nikolich, Anita Griffioen, James Monga, Indermohan Inder S https://orcid.org/0000-0003-4524-0457 Wang, Kuang-Ching Lehman, Tom Ruth, Paul