National Energy Research Scientific Computing Research Articles

Search for two-neutrino double-beta decay of 136Xe to the excited state of 136Ba with the complete EXO-200 dataset* *EXO-200 is supported by DOE and NSF in the United States, NSERC in Canada, SNF in Switzerland, IBS in Korea, DFG in Germany, and CAS in China. EXO-200 data analysis and simulation uses resources of the National Energy Research Scientific Computing Center

A new search for two-neutrino double-beta (2νββ) decay of 136Xe to the excited state of 136Ba is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90% confidence level half-life sensitivity of 2νββ decay to the state of 136Ba is yr using a total 136Xe exposure of 234.1 kg yr. No statistically significant evidence for 2νββ decay to the state is observed, leading to a lower limit of yr at 90% confidence level, improved by 70% relative to the current world's best constraint.

NERSC in the loop: supporting experimental facilities at the National Energy Research Scientific Computing Center

NERSC in the loop: supporting experimental facilities at the National Energy Research Scientific Computing Center

Planting a Lyman alpha forest on AbacusSummit

ABSTRACT The full-shape correlations of the Lyman alpha (Ly α) forest contain a wealth of cosmological information through the Alcock–Paczyński effect. However, these measurements are challenging to model without robustly testing and verifying the theoretical framework used for analysing them. Here, we leverage the accuracy and volume of the N-body simulation suite AbacusSummit to generate high-resolution Ly α skewers and quasi-stellar object (QSO) catalogues. One of the main goals of our mocks is to aid in the full-shape Ly α analysis planned by the Dark Energy Spectroscopic Instrument (DESI) team. We provide optical depth skewers for six of the fiducial cosmology base-resolution simulations ($L_{\rm box} = 2\, h^{-1}\, {\rm Gpc}$, N = 69123) at z = 2.5. We adopt a simple recipe based on the Fluctuating Gunn–Peterson Approximation (FGPA) for constructing these skewers from the matter density in an N-body simulation and calibrate it against the 1D and 3D Ly α power spectra extracted from the hydrodynamical simulation IllustrisTNG (TNG; $L_{\rm box} = 205\, h^{-1}\, {\rm Mpc}$, N = 25003). As an important application, we study the non-linear broadening of the baryon acoustic oscillation (BAO) peak and show the cross-correlation between DESI-like QSOs and our Ly α forest skewers. We find differences on small scales between the Kaiser approximation prediction and our mock measurements of the Ly α × QSO cross-correlation, which would be important to account for in upcoming analyses. The AbacusSummit Ly α forest mocks open up the possibility for improved modelling of cross-correlations between Ly α and cosmic microwave background (CMB) lensing and Ly α and QSOs, and for forecasts of the 3-point Ly α correlation function. Our catalogues and skewers are publicly available on Globus via the National Energy Research Scientific Computing Center (NERSC) (full link under the section ‘Data Availability’).

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows.

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

Alvin William Trivelpiece

An outstanding leader, physicist, and administrator, Alvin William Trivelpiece died on 7 August 2022 in Rancho Santa Margarita, California.Alvin William TrivelpieceAIP ESVA/PHYSICS TODAY COLLECTIONPPT|High resolutionBorn on 15 March 1931 in Stockton, California, Al put himself through California Polytechnic State College and graduated in 1953 with a degree in electrical engineering. He earned a PhD in electrical engineering in 1958 from Caltech under the guidance of Roy Gould. His dissertation was titled “Space charge waves in cylindrical plasma columns.”After spending a year in the Netherlands as a Fulbright scholar, Al became a professor of electrical engineering at the University of California, Berkeley. In 1966 he became a professor of physics at the University of Maryland, College Park, where he supervised 19 doctoral candidates and published more than 40 papers on plasma physics. Al also wrote, with Nicholas Krall, Principles of Plasma Physics, which remains a classic textbook 50 years after publication.Taking a leave of absence from the University of Maryland in 1973, Al served for two years with the Atomic Energy Commission as an assistant director for research in the division of controlled thermonuclear research. Among his achievements was the establishment of the national Controlled Thermonuclear Research Computer Center (CTRCC), which provided fusion researchers across the country with remote access to leading-edge computing resources. The idea was met with considerable skepticism, but it became a model used by other science agencies.In 1976 Al became vice president for engineering and research at Maxwell Laboratories, and in 1978 he was appointed corporate vice president of Science Applications Inc. Three years later Al was nominated by President Ronald Reagan and confirmed by the Senate as director of the Department of Energy’s Office of Energy Research (now the Office of Science), where he had responsibility for 5 research programs and 10 of DOE’s laboratories. He also served as the energy secretary’s science adviser.As director, Al developed a plan for construction of five major scientific research facilities: the Advanced Light Source, the Advanced Photon Source, the Relativistic Heavy Ion Collider, the Continuous Electron Beam Accelerator Facility, and the Spallation Neutron Source (SNS). Those facilities continue to sustain US leadership in many fields of science today. Al’s planning was informed by federal advisory committees that he established and the National Academy of Sciences, an approach that continues to serve DOE’s Office of Science well.Al and Charles DeLisi in 1986 initiated the Human Genome Project, which has had an enormous impact on the life sciences and medical research. Al was masterful in obtaining support for the project from DOE management, the Office of Management and Budget, and the US Congress. The National Institutes of Health joined the project two years later.Al expanded the scope of the CTRCC to form the National Energy Research Scientific Computing Center, which continues to serve the research programs in the Office of Science as well as other DOE programs.In 1987 Al became the executive officer of the American Association for the Advancement of Science and publisher of Science. Then in January 1989, he became director of Oak Ridge National Laboratory, where he was responsible for several initiatives. He created the Office of Laboratory Computing to enhance Oak Ridge’s ability to meet complex scientific challenges. Al led the development of a successful proposal for the Center for Computational Sciences, which brought together computational-science teams, computer scientists, and the hardware vendor Intel. It was designated as one of two DOE high-performance computing research centers in 1993.A second major achievement was the SNS. The lab had been designing a reactor-based neutron source. When the cost became unacceptable, Al took the lead in shifting to the accelerator-based SNS and gathered a multilab consortium to execute design and construction and broaden the project’s base of support. The SNS returned the US to a world-leading position in neutron-scattering research.Al was an advocate for international cooperation in science. His work behind the scenes with Evgeny Velikhov led to an agreement between the US and the Soviet Union on developing a magnetic-fusion demonstration model, which laid the groundwork for the ITER project.Al’s outstanding achievements extend across research, education, science administration, and national security. He was an innovator in the positions he held. Many of his accomplishments resulted from his understanding of science and politics and his ability to guide projects through government processes.Those of us who worked closely with Al have been fortunate to have had the opportunity. Al was a continuous source of ideas and wisdom. He had a great sense of humor and was quick to generate puns. He enjoyed playing poker, chess, and golf and was an avid runner. He piloted his own airplane to the age of 85. His wisdom, leadership, and friendship will be missed.© 2023 American Institute of Physics.

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

How Halide Sub-Lattice Affects Li Ion Transport in Antiperovskites

Li-based antiperovskites (LiAP, Li3-x OH x X, X = Cl, Br) are an emergent class of Li-ion conductors that are potential candidates for electrolytes in all-solid-state batteries. As a material class, pLiAP shows vast compositional design freedom; however, the resulting properties are susceptible to synthesis and processing methodologies. For example, proton incorporation and halide mixing stabilize the perovskite cubic phase near room temperature, and halides mixtures near the eutectic points drive the solid-state reaction temperature down, allowing for faster synthesis and processing conditions (< 1 h). The mixed halogen compositions, such as Li2OHCl0.37Br0.63, also show a 30-fold improvement in room temperature ionic conductivity of a single halide structure, 1.5 x 10-6 vs. 4.9 x 10-8 S cm-1 (Li2OHCl). Despite the growing interest in these materials, important questions remain about LiAPs on the structure-property correlation upon halide substitution and the correlations between the OH/halide dynamics and the Li-ion transport. We thus attempted to deconvolute how proton dynamics and halide substitution enhance or impede ionic conduction in pLiAP at compositions near the halide salts' eutectic points.We combined infrared spectroscopy and nuclear magnetic resonance (NMR) with first-principles density functional theory (DFT) calculations to deconvolute halide mixing effects from local proton dynamics on Li-ion transport. The NMR results and ab initio molecular dynamics suggest that Li+ transport is more strongly correlated with halide dynamics. While the hydroxide does stabilize the highly conductive cubic structure, it limits correlative ionic transport and thus lowers Li+ conductivity.Experiment design, data analysis, and manuscript preparation (RLS) were supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Science and Engineering. Synthesis (THB and JN) were supported by Asst. Secretary, Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (VTO) through the Advanced Battery Materials Research (BMR) Program. P. J. acknowledges partial support by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-FG02-96ER45579. H. F. was supported from U.S. Department of Energy (Award No. DE-EE0008865). This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The NMR characterization part of the work is supported by the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, and Basic Energy Sciences. The NMR work was performed at the W. R. Wiley Environmental Molecular Sciences Laboratory, a DOE User Facility sponsored by the Office of Biological and Environmental Research, located at Pacific Northwest National Laboratory. Figure 1

Design and implementation of dynamic I/O control scheme for large scale distributed file systems

In this work, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever-increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article, we introduce the Delta framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, Delta allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describes the modular and expandable software architecture of Delta and presents performance benchmarks of individual components as well as of an example workflow. Focusing on a streaming analysis workflow where electron cyclotron emission imaging (ECEi) data is measured at KSTAR on the National Energy Research Scientific Computing Center’s (NERSC’s) supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. In NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes them in under 5 min. We further discuss how Delta uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. By providing session leaders and physics operators, results of higher-order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

Hidden physicists

Like me, my friend Vincent Kargatis earned a PhD in high-energy astrophysics and worked at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. Also like me, Kargatis left Goddard in the late 1990s to pursue a career outside of astronomy—in his case, computer programming. Now, 23 years later, he manages a team that ensures the quality of software produced by Oracle Utilities.Since I joined Physics Today in 1997, I’ve been obliged to keep up with developments in physics and to nurture contacts with physicists and their close professional relatives. Still, neither Kargatis nor I think of ourselves as physicists any longer.Debbie Bard leads the data science engagement group at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. Before then, she worked at Imperial College London and SLAC. She earned a PhD in experimental particle physics at Edinburgh University in Scotland.Bard’s team helps researchers who need supercomputers for their data analysis and simulations. The job entails performing two translations: from what scientists need into what NERSC’s systems engineers can provide and from what NERSC’s hardware and software can do into what the scientists can implement.“My work is very far from actual physics,” Bard told me by email. “I’m mainly a manager of people and projects these days. But if someone asks me what I do, I still answer ‘physicist.’ It’s pretty deeply ingrained in me.”I learned about Bard from Spotlight on Hidden Physicists, an ongoing series of articles in Radiations, the magazine of Sigma Pi Sigma, the physics honor society. Each article is about a person with a physics degree who, like Bard, Kargatis, and me, went on to do other things. The range of other things is wide. Among the 37 hidden physicists are a jazz singer, a Disney animator, a Wall Street analyst, and a theologian.Kargatis qualifies as a hidden physicist. Indeed, people who work in computer software of one kind or another form the largest group in the Radiations series. Computer software is also the private-sector profession that employs the most PhD physicists, according to surveys conducted by the American Institute of Physics’s Statistical Research Center. (AIP publishes Physics Today and hosts Sigma Pi Sigma.)Sigma Pi Sigma’s Spotlight on Hidden Physicists succeeds in showing the variety of rewarding careers that people with a bachelor’s, master’s, or PhD in physics choose for themselves. High school seniors contemplating what subject to major in can be justifiably confident that a physics degree opens doors to careers beyond physics itself.But when the professional physics community contemplates hidden physicists, it should be careful. Although Bard continues to think of herself as a physicist, Kargatis does not. When he and I get together, we talk mostly about music, food, and wine and occasionally about physics. He is not a prime candidate for becoming a Physics Today subscriber or for rejoining the American Astronomical Society.Of the physicists in the private sector, how many still consider themselves to be physicists? I don’t know, but my hunch is that the answer has to do with whether they continue to do physics. This past May Google announced its intention to spend billions of dollars to build a quantum computer that can perform large-scale, error-free calculations by 2029. Out of curiosity, I looked up Google’s jobs site. To accomplish that ambitious goal, the company sought to recruit just six additional physicists.Two other companies in physics-flavored industries, IBM and Tesla, had no postings for physicists. That’s not to say either company had no jobs for which physicists were qualified. Tesla is currently looking for someone to do computer-aided engineering and computational fluid dynamics. Some physicists meet the position’s requirements, but so, too, do engineers and applied mathematicians.If “hidden physicist” is intended to mean someone outside academia who identifies as a physicist and who does physics, some adjustment might be needed. There could be fewer of them than expected.© 2021 American Institute of Physics.

Performance Portability for Advanced Architectures

The papers in this special section focus on performance portability for advanced architectures. This is a very timely topic as we continue our journey to exascale computers. The leader on the Top 500 list today is the Fujitsu Fugaku computer in Japan, using a modified ARM architecture and 512-bit scalable vector extension (SVE) instructions. In the USA, the National Energy Research Scientific Computing Center (NERSC) has just dedicated Perlmutter, named for the Berkeley Nobel Laureate Saul Perlmutter. Perlmutter, built by Cray/HPE, contains both CPU-only and CPU/GPU nodes. The CPUs are produced by AMD and the GPUs come from NVIDIA. Frontier at Oak Ridge National Laboratory and El Capitan at Lawrence Livermore National Laboratory will contain new AMD GPUs, and Aurora at Argonne National Laboratory will feature new GPUs from Intel. Los Alamos National Laboratory’s next-generation machine Crossroads will use Intel CPUs with high-bandwidth memory and AVX-512 vector instructions. Optimizing code for three different GPUs is a daunting challenge for application programmers. Further, there are still many computers that do not feature GPUs, and most computational scientists would not be eager to totally abandon such machines.

The Atacama Cosmology Telescope: Summary of DR4 and DR5 Data Products and Data Access

Abstract Two recent large data releases for the Atacama Cosmology Telescope (ACT), called DR4 and DR5, are available for public access. These data include temperature and polarization maps that cover nearly half the sky at arcminute resolution in three frequency bands; lensing maps and component-separated maps covering ∼2100 deg2 of sky; derived power spectra and cosmological likelihoods; a catalog of over 4000 galaxy clusters; and supporting ancillary products including beam functions and masks. The data and products are described in a suite of ACT papers; here we provide a summary. In order to facilitate ease of access to these data, we present a set of Jupyter IPython notebooks developed to introduce users to DR4, DR5, and the tools needed to analyze these data. The data products (excluding simulations) and the set of notebooks are publicly available on the NASA Legacy Archive for Microwave Background Data Analysis; simulation products are available on the National Energy Research Scientific Computing Center.

Hydrodynamic description of D meson production in high-energy heavy-ion collisions * *Supported by the National Key Research and Development Program of China (2020YFE0202002); the National Natural Science Foundation of China (11935007, 11221504, 11861131009, 12075098, 11890714); the Director, Office of Energy Research, Office of High Energy and Nuclear Physics, Division of Nuclear Physics, of the U.S. Department of Energy (DOE) (DE-

The large values and constituent-quark-number scaling of the elliptic flow of low- D mesons imply that charm quarks, initially produced through hard processes, might be partially thermalized through strong interactions with quark-gluon plasma (QGP) in high-energy heavy-ion collisions. To quantify the degree of thermalization of low- charm quarks, we compare the meson spectra and elliptic flow from a hydrodynamic model to experimental data as well as transport model simulations. We use an effective charm chemical potential at the freeze-out temperature to account for the initial charm quark production from hard processes and assume that they are thermalized in the local comoving frame of the medium before freeze-out. mesons are sampled statistically from the freeze-out hyper-surface of the expanding QGP as described by the event-by-event (3+1)D viscous hydrodynamic model CLVisc. Both the hydrodynamic and transport models can describe the elliptic flow of mesons at GeV/c as measured in Au+Au collisions at GeV. Though the experimental data on spectra are consistent with the hydrodynamic result at small GeV/c, they deviate from the hydrodynamic model at high transverse momentum, GeV/c. The diffusion and parton energy loss mechanisms in the transport model can describe the measured spectra reasonably well within the theoretical uncertainty. Our comparative study indicates that charm quarks only approach local thermal equilibrium at small , even though they acquire sizable elliptic flow comparable to light-quark hadrons at both small and intermediate .

Interactive Supercomputing With Jupyter

Rich user interfaces like Jupyter have the potential to make interacting with a supercomputer easier and more productive, consequently attracting new kinds of users and helping to expand the application of supercomputing to new science domains. For the scientist user, the ideal rich user interface delivers a familiar, responsive, introspective, modular, and customizable platform upon which to build, run, capture, document, re-run, and share analysis workflows. From the provider or system administrator perspective, such a platform would also be easy to configure, deploy securely, update, customize, and support. Jupyter checks most if not all of these boxes. But from the perspective of leadership computing organizations that provide supercomputing power to users, such a platform should also make the unique features of a supercomputer center more accessible to users and more composable with high-performance computing (HPC) workflows. Project Jupyter’s ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://jupyter.org/about</uri> ) core design philosophy of extensibility, abstraction, and agnostic deployment has allowed HPC centers like National Energy Research Scientific Computing Center to bring in advanced supercomputing capabilities that can extend the interactive notebook environment. This has enabled a rich scientific discovery platform, particularly for experimental facility data analysis and machine learning problems.

STAR Data Production Workflow on HPC: Lessons Learned & Best Practices

The Solenoidal Tracker at RHIC (STAR) is a multi-national supported experiment located at Brookhaven National Lab. The raw physics data captured from the detector is on the order of tens of PBytes per data acquisition campaign, which makes STAR fit well within the definition of a big data science experiment. The production of the data has typically run on standard nodes or on standard Grid computing environments. All embedding simulations (complex workflow mixing real and simulated events) have been run on standard Linux resources at the National Energy Research Scientific Computing Center (NERSC) aka PDSF. However, HPC resources such as Cori have become available for STAR’s data production as well as embedding, and STAR has been the very first experiment to show feasibility of running a sustainable data production campaign on this computing resource. The use of Docker containers with Shifter is required to run on HPC @ NERSC – this approach encapsulates the environment in which a standard STAR workflow runs. From the deployment of a tailored Scientific Linux environment (requiring many of its own libraries and special configurations required to run) to the deployment of third-party software and the STAR specific software stack, it has become impractical to rely on a set of containers containing each specific software release. To this extent, solutions based on the CERN VM File System (CVMFS) for the deployment of software and services have been employed in HENP, but one needs to make careful scalability considerations when using a resource like Cori, such as not allowing all software to be deployed in containers or bare node. Additionally, CVMFS clients are not compatible on Cori nodes and one needs to rely on an indirect NFS/DVS mount scheme. In our contribution, we will discuss our strategies from the past and our current solution based on CVMFS. Furthermore, running on HPC is not a simple task as each aspect of the workflow must be enabled to scale, run efficiently, and the workflow needs to fit within the boundaries of the provided queue system (SLURM in this case). Lastly, we will also discuss what we have learned so far about what is the best method for grouping jobs to maximize a single 48 core HPC node within a specific time frame and maximize our workflow efficiency.

Interactions of Ionophoric Polyphenols with Human Serum Albumin

The distribution of a drug is determined, at least in part, by its interactions with serum proteins. Human serum albumin (HSA), the most abundant blood plasma protein, serves as the most common mode of transport for drugs to their target. In our previous work, we have synthesized and studied important characteristics of a series of ionophoric polyphenols as potential anti‐Alzheimer’s disease agents. As part of our ongoing investigations on the biological activity of these compounds, we are now exploring possible transport mechanisms. In our current work, the interactions of the ionophoric polyphenols with HSA were studied via in vitro and in silico methods. In line with in vitro studies, the interaction of the ionophoric polyphenols with HSA were studied by fluorescence and circular dichroism (CD) spectroscopies to obtain binding affinities, bimolecular quenching constants, theoretical number of binding sites and HSA secondary structural changes. Moreover, in line with in silico studies, a preferred mode of binding was predicted by molecular docking and the predicted systems were subject to a 5 ns MD simulation; consequently, RMSD studies were performed to attest for stability of the systems, MM/GBSA binding free energies were calculated to correlate with experimental binding affinities and per‐residue energy decomposition analyses were carried out to analyze residues critical to binding. Using resveratrol, a polyphenolic compound which has demonstrated strong neuroprotective and anti‐Alzheimer disease properties, as a control, in vitro results showed the formation of adducts (static quenching), one theoretical binding site, binding constants (Ka) ranging 104 to 106 M−1 and only slight secondary structure modifications between HSA and the ionophoric polyphenols. Further, in silico results revealed the ionophoric polyphenols and resveratrol favorably binding subdomain IIA of HSA, forming critical interactions to Trp 214 and Asp451. RMSD studies showed the HSA‐IIA system plateauing after a short time indicating stable binding events; in addition, a Pearson correlation coefficient of R=0.80 between the experimental binding affinity and computational binding free energies showed a strong correlation between in vitro and in silico results. Overall, our results seem to indicate that there is a ionophoric polyphenol‐serum albumin interaction, which could have a potential impact on the use of ionophoric polyphenols as therapeutics for Alzheimer’s disease.Support or Funding InformationThis research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE‐AC02‐05CH11231.

Computational Studies of Glycosylated Photosensitizers with Plasma Proteins

Photodynamic therapy (PDT) is a promising treatment against cancer, it utilizes photosensitizers that when excited by light undergo photochemical reactions that can kill abnormal cancer cells. However, in order for photosensitizers to reach their target, they must effectively bind serum albumin, the most abundant plasma protein circulated in an organism’s blood stream. In our previous study, we synthesized three glycosylated photosensitizers (PGlu, CGlu and IGlu) and performed in vitro spectroscopy studies that revealed each glycosylated photosensitizer effectively binding bovine serum albumin and human serum albumin (BSA and HSA). The aim of this work was to implement an in silico approach to study how PGlu, CGlu, and IGlu are binding onto BSA and HSA. We performed molecular docking simulations to predict a favorable mode of binding between each of our glycosylated photosensitizers to BSA and HSA. Further, we performed a 5 ns molecular dynamics (MD) simulation for each of our predicted systems; consequently, we calculated the RMSD of the systems to attest for stability, performed binding free energy calculations to correlate with experimental binding constant results and analyzed residue interactions critical to each binding event. We observed PGlu, CGlu and IGlu favorably binding the hemin site located in subdomain IB of BSA, forming strong interactions with the Trp134 residue, as well as favorably binding Sudlow Site 1 located in subdomain IIA of HSA, forming strong interactions to Trp214. RMSD studies revealed the BSA‐IB and HSA‐IIA systems plateauing after a short time indicating stable binding events; in addition, a Pearson correlation coefficient of R=0.87 between the experimental binding constants and computational binding free energies showed a strong correlation between in vitro and in silico results. Overall, our computational results corroborated our experimental results, revealing that our glycosylated photosensitizers can bind serum albumin effectively through critical tryptophan residue interactions and can potentially aid in the progression of drug development for PDT.Support or Funding InformationThis research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE‐AC02‐05CH11231.

Implementing efficient data compression and encryption in a persistent key-value store for HPC

Recently, persistent data structures, like key-value stores (KVSs), which are stored in a high-performance computing (HPC) system’s nonvolatile memory, provide an attractive solution for a number of emerging challenges like limited I/O performance. Data compression and encryption are two well-known techniques for improving several properties of such data-oriented systems. This article investigates how to efficiently integrate data compression and encryption into persistent KVSs for HPC with the ultimate goal of hiding their costs and complexity in terms of performance and ease of use. Our compression technique exploits deep memory hierarchy in an HPC system to achieve both storage reduction and performance improvement. Our encryption technique provides a practical level of security and enables sharing of sensitive data securely in complex scientific workflows with nearly imperceptible cost. We implement the proposed techniques on top of a distributed embedded KVS to evaluate the benefits and costs of incorporating these capabilities along different points in the dataflow path, illustrating differences in effective bandwidth, latency, and additional computational expense on Swiss National Supercomputing Centre’s Grand Tavé and National Energy Research Scientific Computing Center’s Cori.

Performance of the AMS Offline Software at National Energy Research Scientific Computing Centre and Argonne Leadership Computing Facility

The Alpha Magnetic Spectrometer [1] (AMS) is a high energy physics experiment installed and operating on board of the International Space Station (ISS) from May 2011 and expected to last through year 2024 and beyond. More than 50 million of CPU hours has been delivered for AMS Monte Carlo simulations using NERSC and ALCF facilities in 2017. The details of porting of the AMS software to the 2nd Generation Intel Xeon Phi Knights Landing architecture are discussed, including the MPI emulation module to allow the AMS offline software to be run as multiple-node batch jobs. The performance of the AMS simulation software at NERSC Cori (KNL 7250), ALCF Theta (KNL 7230), and Mira (IBM BG/Q) farms is also discussed.

Advances in Multiphysics Modeling for Parallel Finite-Element Code Suite ACE3P

Developed by SLAC, the advanced computational electromagnetics three-dimensional parallel (ACE3P) code suite is a comprehensive set of parallel finite-element codes for multiphysics modeling of particle accelerators. Running on massively parallel computer platforms for high fidelity and high accuracy simulation, ACE3P enables rapid virtual prototyping of accelerator and radio frequency (RF) component design, optimization, and analysis. Recent advances of ACE3P have been focused on the implementation of advanced numerical algorithms, enhancement of multiphysics capabilities, and integration of ACE3P with other particle beams and plasmas codes toward end-to-end simulations in accelerators, and improvement of code performance on the state-of-the-art high-performance computing (HPC) platforms for large-scale RF modeling in accelerator applications and beyond. In this article, we will present a nonlinear electromagnetic (EM) eigensolver for damping calculation of resonant modes for cavities coupled with waveguides, the thermal solver for accurate evaluation of heat loads in superconducting RF (SRF) cavity power coupler, the elastic solver for investigation of SRF cavity cryomodule (CM) bowing due to thermal effect, the frequency-domain mechanical solver for studying SRF cavity mechanical oscillation modes, the particle tracking code for dark current simulation in SRF CM using a hybrid MPI+OpenMP parallel programming model, and the time-domain solver for high-fidelity modeling of EM wave propagation for powering midfield medical devices in human body. All the simulations have been performed on the computing resources at the National Energy Research Scientific Computing Center, and the impact of HPC on multiphysics modeling will be discussed.