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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

Journal of Molecular Biology 2025年第15期437卷 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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Novel Metabolomic and Adiposity Markers Enhance Prediction of Obstructive Sleep Apnea: A Prospective Analysis from the China Human Phenobank

SSRN

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SSRN 2025年

作者： Wu, Yudan Ba, Yihan Chen, Chen Wang, Qi Chen, Qinsheng Zhang, Jinxian Chen, Longlong Zhao, Yining Shi, Biru Ye, Lisong Chen, Wei Wang, Chengyan Hu, Zixin Zhang, Jun Feng, Kai Wang, Yan Liu, Yuehua Qi, Yuan Tang, Huiru Luo, Jingchun Tian, Mei State Key Laboratory of Genetic Engineering School of Life Sciences Human Phenome Institute Zhangjiang Fudan International Innovation Center Fudan University Shanghai201203 China Department of Endocrinology and Metabolism Zhongshan Hospital Fudan University Shanghai200032 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Chinese Academy of Sciences Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of the Chinese Academy of Sciences Chinese Academy of Sciences Shanghai200031 China Fudan University Shanghai201203 China Department of Radiology Huashan Hospital State Key Laboratory of Medical Neurobiology National Center for Neurological Disorders Fudan University Shanghai200040 China Department of Preventive Dentistry Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200433 China Shanghai Academy of Artificial Intelligence for Science Shanghai200232 China

Background: Obstructive sleep apnea (OSA) prevalence is increasing among Asian populations, yet effective biomarker-based screening tools remain underdeveloped. Metabolic dysregulation and adiposity patterns show potential as novel biomarkers for OSA risk stratification, but their predictive utility has not been comprehensively evaluated in Chinese populations. We aimed to identify key metabolic and adiposity biomarkers for OSA and subsequently develop an integrated prediction model to enhance risk assessment in a Chinese ***: In this prospective cohort study (2018-2023), we enrolled 972 adults (aged 20-60 years) from the China Human Phenobank. All participants underwent overnight polysomnography, sleep questionnaires, metabolomic profiling (1,840 metabolites), and DXA-derived body fat distribution analysis (58 adiposity parameters). OSA was defined according to international clinical criteria as apnea hypopnoea index (AHI) ≥5/h accompanied by characteristic symptoms (excessive daytime sleepiness, fatigue, or impaired cognition) or associated comorbidities. Participants were randomly allocated to training (n=777) and test (n=195) cohorts. We employed a multi-staged feature selection approach integrating differential analysis, correlation filtering, and machine learning algorithms (mRMR and LASSO) to identify key biomarkers. Selected features were integrated into a predictive model combining clinical, metabolomic, and adiposity data. Metabolic pathway enrichment analysis identified biological mechanisms underlying OSA *** identified two critical fat distribution markers (visceral adipose tissue volume and gynoid fat) and ten metabolic signatures (including specific TAG-FAs, lipoprotein subtypes, and steroid derivatives) significantly associated with OSA. The gender-specific fat distribution patterns and protective effects of gynoid fat distribution provided new insights into OSA pathophysiology. Notably, we identified H2FC, a specific H

关键词： Risk assessment

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DronePick: Object Picking and Delivery Teleoperation with the Drone Controlled by a Wearable Tactile Display

DronePick: Object Picking and Delivery Teleoperation with th...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Roman Ibrahimov Evgeny Tsykunov Vladimir Shirokun Andrey Somov Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Russian Federation Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russian Federation

ISBN: (纸本)9781728126234

We report on the teleoperation system DronePick which provides remote object picking and delivery by a human- controlled quadcopter. The main novelty of the proposed system is that the human user continuously gets the visual and haptic feedback for accurate teleoperation. DronePick consists of a quadcopter equipped with a magnetic grabber, a tactile glove with finger motion tracking sensor, hand tracking system, and the Virtual Reality (VR) application. The human operator teleoperates the quadcopter by changing the position of the hand. The proposed vibrotactile patterns representing the location of the remote object relative to the quadcopter are delivered to the glove. It helps the operator to determine when the quadcopter is right above the object. When the “pick” command is sent by clasping the hand in the glove, the quadcopter decreases its altitude and the magnetic grabber attaches the target object. The whole scenario is in parallel simulated in VR. The air flow from the quadcopter and the relative positions of VR objects help the operator to determine the exact position of the delivered object to be picked. The experiments showed that the vibrotactile patterns were recognized by the users at the high recognition rates: the average 99% recognition rate and the average 2.36s recognition time. The real-life implementation of DronePick featuring object picking and delivering to the human was developed and tested.

关键词： drone honey bees Gloves remote operation Picking Human Touch Variable reluctance Obstetric Delivery commands air flow

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Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes

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Journal of Medical Internet Research 2025年第1期27卷 e71873页

作者： Huang, Feiqing Hou, Jue Zhou, Ningxuan Greco, Kimberly Lin, Chenyu Sweet, Sara Morini Wen, Jun Shen, Lechen Gonzalez, Nicolas Zhang, Sinian Liao, Katherine P. Cai, Tianrun Xia, Zongqi Bourgeois, Florence T. Cai, Tianxi Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Harvard-MIT Center for Regulatory Science Harvard Medical School Boston MA United States Division of Biostatistics and Health Data Science School of Public Health University of Minnesota Minneapolis MN United States Department of Biomedical Informatics Harvard Medical School Boston MA United States Department of Engineering University of Toronto Toronto ON Canada Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Boston MA United States Department of Neurology University of Pittsburgh Pittsburgh PA United States Computational Health Informatics Program (CHIP) Boston Children's Hospital Boston MA United States Department of Pediatrics Harvard Medical School Boston MA United States

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction. ©Feiqing Huang, Jue Hou, Ningxuan Zhou, Kimberly Greco, Chenyu Lin, Sara Morini Sweet, Jun Wen, Lechen Shen, Nicolas Gonzalez, Sinian Zhang, Katherine P Liao, Tianrun Cai, Zongqi Xia, Florence T Bourgeois, Tianxi Cai.

关键词： calibration causal inference chronic disease outcomes data imputation electronic health records longitudinal disease activity machine learning real-world evidence

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A Consensus Privacy Metrics Framework for Synthetic data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Sim2Ls: FAIR simulation workflows and data

arXiv

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arXiv 2021年

作者： Hunt, Martin Clark, Steven Mejia, Daniel Desai, Saaketh Strachan, Alejandro Halıcıoglu Data Science Institute University of California at San Diego San DiegoCA92093 United States San Diego Supercomputer Center University of California at San Diego San DiegoCA92093 United States Network for Computational Nanotechnology Purdue University West LafayetteIN47907 United States Center for Integrated Nanotechnologies Sandia National Laboratories AlbuqerqueNM87123 United States School of Materials Engineering Network for Computational Nanotechnology Purdue University West LafayetteIN47907 United States

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data. Copyright © 2021, The Authors. All rights reserved.

关键词： Simulation platform

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Genes and comorbidities of thyroid cancer

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Informatics in Medicine Unlocked 2021年 25卷

作者： Ljubic, Branimir Pavlovski, Martin Roychoudhury, Shoumik Van Neste, Christophe Salhi, Adil Essack, Magbubah Bajic, Vladimir B. Obradovic, Zoran Temple University Center for Data Analytics and Biomedical Informatics (DABI) Philadelphia 19122 PA United States Rutgers University the Office of Advanced Research Computing (OARC) Piscataway 08854 NJ United States Center for Medical Genetics (CMGG) Ghent University Ghent Belgium King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Center (CBRC) Computer Electrical and Mathematical Science and Engineering (CEMSE) Division Thuwal Saudi Arabia

Introduction: Thyroid cancer represents 3.1 % of diagnosed cancers in the United States. The objective of this research was to identify comorbidities and discover additional genes potentially related to thyroid cancer and improve current knowledge of genetics and comorbidities associated with this cancer. Methods: Healthcare Cost and Utilization Project (HCUP) California State Inpatient database (SID) was used to extract and rank the comorbidities of thyroid cancer. The text mining software - BeFree was utilized to identify and extract genes associated with thyroid cancer and the comorbidities from PubMed abstracts and the DisGeNET expert-curated repositories. Results: Female patients had 4,485, and male patients 2912 different comorbidities in early stages of thyroid cancer. Females had 3,587, and males 2817 different comorbidities in advanced stages. Through PubMed utilizing the BeFree method, 504 different genes associated with thyroid cancer were discovered, as well as five genes on DisGeNET. The most often genes on PubMed, associated with thyroid cancer were: BRAF, RET, SLC5A5, RAS, and PTEN. Genes found via DisGeNET were BRAF, RET, KRAS, NRAS, and PRKAR1A. Conclusion: Identified genes and comorbidities, as potential additional risk factors for thyroid cancer, not previously known, could improve the early diagnosis and the survival of patients with thyroid cancer. Genes discovered in this research in association with thyroid cancer could be used to direct decision making for optimal, more personalized treatment of thyroid cancer. © 2021

关键词： Biomedical informatics Comorbidities DisGeNET Genes Text mining Thyroid cancer

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The Multiband Imaging Survey for High-Alpha PlanetS (MISHAPS) I: Preliminary Constraints on the Occurrence Rate of Hot Jupiters in 47 Tucanae

arXiv

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arXiv 2024年

作者： Crisp, Alison L. Klüter, Jonas Newman, Marz L. Penny, Matthew T. Beatty, Thomas G. Cleeves, L. Ilsedore Collins, Karen A. Johnson, Jennifer A. Johnson, Marshall C. Lund, Michael B. Martínez-Vázquez, Clara E. Ness, Melissa K. Rodriguez, Joseph E. Siverd, Robert Stevens, Daniel J. Villanueva, Steven Ziegler, Carl Department of Physics & Astronomy Louisiana State University Baton RougeLA70803 United States Department of Astronomy The Ohio State University ColumbusOH43210 United States Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States Department of Astronomy University of Wisconsin – Madison MadisonWI53706 United States Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Department of Chemistry University of Virginia CharlottesvilleVA22904 United States Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States Center for Cosmology & Astro Particle Physics The Ohio State University ColumbusOH43210 United States NASA Exoplanet Science Institute California Institute of Technology PasadenaCA91125 United States Gemini North Observatory NOIRLab HiloHI96720 United States Department of Astronomy Columbia University New YorkNY10027 United States Simons Center for Computational Astrophysics Flatiron Institute New YorkNY10004 United States Center for Data Intensive and Time Domain Astronomy Department of Physics & Astronomy Michigan State University East LansingMI48824 United States Institute for Astronomy University of Hawai’i at Mānoa HonoluluHI96822 United States Department of Physics & Astronomy University of Minnesota – Duluth DuluthMN55812 United States Exoplanets & Stellar Astrophysics Laboratory NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Physics Engineering & Astronomy Stephen F. Austin State University NacogdochesTX75962 United States

The first generation of transiting planet searches in globular clusters yielded no detections, and in hindsight, only placed occurrence rate limits slightly higher than the measured occurrence rate in the higher-metallicity Galactic thick disk. To improve these limits, we present the first results of a new wide field search for transiting hot Jupiters in the globular cluster 47 Tucanae. We have observed 47 Tuc as part of the Multiband Imaging Survey for High-Alpha Planets (MISHAPS). Using 24 partial and full nights of observations taken with the Dark Energy Camera on the 4-m Blanco telescope at CTIO, we perform a search on 19,930 stars in the outer regions of the cluster. Though we find no clear planet detections, by combining our result with the upper limit enabled by Gilliland et al.’s (2000) Hubble search for planets around an independent sample of 34,091 stars in the inner cluster, we place the strongest limit to date on hot Jupiters with periods of 0.8 ≤ P ≤ 8.3 days and 0.5 RJup ≤ RP ≤ 2.0 RJup of fHJ © 2024, CC BY.

关键词： Stars

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A continuum and computational framework for viscoelastodynamics: Finite deformation linear models

arXiv

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arXiv 2021年

作者： Liu, Ju Latorre, Marcos Marsden, Alison L. Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen Guangdong518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen Guangdong518055 China Institute for Computational and Mathematical Engineering Stanford University Clark Center E1.3 318 Campus Drive StanfordCA94305 United States Department of Biomedical Engineering Yale University New HavenCT06520 United States Department of Bioengineering Stanford University Clark Center E1.3 318 Campus Drive StanfordCA94305 United States

This work concerns the continuum basis and numerical formulation for deformable materials with viscous dissipative mechanisms. We derive a viscohyperelastic modeling framework based on fundamental thermomechanical principles. Since most large deformation problems exhibit isochoric properties, our modeling work is constructed based on the Gibbs free energy in order to develop a continuum theory using pressure-primitive variables, which is known to be well-behaved in the incompressible limit. A set of general evolution equations for the internal state variables is derived. With that, we focus on a family of free energies that leads to the so-called finite deformation linear model. Our derivation elucidates the origin of the evolution equations of that model, which was originally proposed heuristically and thus lacked formal compatibility with the underlying thermodynamics. In our derivation, the thermodynamic inconsistency is clarified and rectified. A classical model based on the identical polymer chain assumption is revisited and is found to have non-vanishing viscous stresses in the equilibrium limit, which is counter-intuitive in the physical sense. Because of that, we then discuss the relaxation property of the non-equilibrium stress in the thermodynamic equilibrium limit and its implication on the form of free energy. A modified version of the identical polymer chain model is then proposed, with a special case being the model proposed by G. Holzapfel and J. Simo. Based on the consistent modeling framework, a provably energy stable numerical scheme is constructed for incompressible viscohyperelasticity using inf-sup stable elements. In particular, we adopt a suite of smooth generalization of the Taylor-Hood element based on Non-Uniform Rational B-Splines (NURBS) for spatial discretization. The temporal discretization is performed via the generalized-α scheme. We present a suite of numerical results to corroborate the proposed numerical properties, including the n

关键词： Free energy

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Solvers for Large-Scale Electronic Structure Theory: ELPA and ELSI

arXiv

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arXiv 2025年

作者： Karpov, Petr Marek, Andreas Melson, Tobias Pöppl, Alexander Yu, Victor Wen-Zhe Hourahine, Ben Garcia, Alberto Dawson, William Yao, Yi Huhn, William Moussa, Jonathan Hall, Sam Maurer, Reinhard Herath, Uthpala Lion, Konstantin Kokott, Sebastian Blum, Volker Max Planck Computing and Data Facility Garching Germany Intel Corporation Santa ClaraCA United States Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Department of Physics SUPA University of Strathclyde John Anderson Building107 Rottenrow GlasgowG4 0NG United Kingdom Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain RIKEN Center for Computational Science Kobe650-0047 Japan Molecular Simulations from First Principles e.V. BerlinD-14195 Germany Intel Corporation 500 Beaver Brook Road BoxboroughMA01719 United States Molecular Sciences Software Institute BlacksburgVA24060 United States University of Warwick United Kingdom Department of Chemistry Duke University DurhamNC27708 United States

In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims. ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem. ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others. ELSI, ELPA, and other solver libraries supported in ELSI are mature, well-tested software, ensuring efficient support for large-scale simulations on current HPC systems. Future plans for ELPA include further optimization of GPU routines, particularly integrating the GPU collective communication libraries (NVIDIA’s NCCL and AMD’s RCCL) into the solution tridiagonal of the matrix problem and backtransformation of the eigenvectors in standard eigenproblem solvers. Similarly expanding support for Intel GPUs through deeper integration with Intel’s oneAPI libraries, specifically oneCCL, is planned. These advancements aim to ensure that ELPA, accessible either with its own Fortran/C/C++ APIs or via ELSI, remains at the forefront of large-scale computations, offering researchers

关键词： Open source software

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