High Performance Computing Resources Research Articles

Democratizing digital design and manufacturing using high performance cloud computing: Performance evaluation and benchmarking

Cloud computing is an innovative computing paradigm that can bridge the gap between increasing computing demands in computationally intensive tasks for digital design and manufacturing applications and limited resources, scalability, flexibility, and agility in traditional computing paradigms. In light of the benefits of cloud computing, cloud-based high performance computing (HPC) has the potential to enable users to not only accelerate computationally expensive tasks, but also to reduce costs by utilizing on-demand, ubiquitous, seamless, and user-friendly access to remote engineering application packages as well as remote HPC resources. However, due to uncertainty about computing performance on the cloud, many manufacturers find it challenging to justify and adopt Cloud-Based Design and Manufacturing (CBDM). Therefore, the objective of this research is to evaluate the performance of solving a large-scale engineering problem using finite element analysis on several public HPC clouds as well as introduce a new workflow for CBDM. A set of experiments is conducted to compare the performance of the public HPC clouds with that of a standard workstation and a dedicated in-house supercomputer. The performance metrics include elapsed time, speedup, scalability, and stability. Experimental results have shown that the Azure Cloud with 32 cores and the Nimbix Cloud with 16 nodes speed up the finite element analysis over a workstation with 8 cores by more than seven-fold and eight-fold. A dedicated in-house supercomputer speeds up the finite element analysis over cloud computing by approximately two-fold because of better I/O performance and larger memory. In addition, considerable variations of elapsed time for solving the finite element model with multiple nodes in the cloud were observed due to resource sharing in cloud computing.

Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS

The suites of numerical models used for simulating climate of our planet are usually run on dedicated high-performance computing (HPC) resources. This study investigates an alternative to the usual approach, i.e. carrying out climate model simulations on commercially available cloud computing environment. We test the performance and reliability of running the CESM (Community Earth System Model), a flagship climate model in the United States developed by the National Center for Atmospheric Research (NCAR), on Amazon Web Service (AWS) EC2, the cloud computing environment by Amazon.com, Inc. StarCluster is used to create virtual computing cluster on the AWS EC2 for the CESM simulations. The wall-clock time for one year of CESM simulation on the AWS EC2 virtual cluster is comparable to the time spent for the same simulation on a local dedicated high-performance computing cluster with InfiniBand connections. The CESM simulation can be efficiently scaled with the number of CPU cores on the AWS EC2 virtual cluster environment up to 64 cores. For the standard configuration of the CESM at a spatial resolution of 1.9° latitude by 2.5° longitude, increasing the number of cores from 16 to 64 reduces the wall-clock running time by more than 50% and the scaling is nearly linear. Beyond 64 cores, the communication latency starts to outweigh the benefit of distributed computing and the parallel speedup becomes nearly unchanged.

PGen: large-scale genomic variations analysis workflow and browser in SoyKB.

BackgroundWith the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed “PGen”, an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way.ResultsWe have developed both a Linux version in GitHub (https://github.com/pegasus-isi/PGen-GenomicVariations-Workflow) and a web-based implementation of the PGen workflow integrated within the Soybean Knowledge Base (SoyKB), (http://soykb.org/Pegasus/index.php). Using PGen, we identified 10,218,140 single-nucleotide polymorphisms (SNPs) and 1,398,982 indels from analysis of 106 soybean lines sequenced at 15X coverage. 297,245 non-synonymous SNPs and 3330 copy number variation (CNV) regions were identified from this analysis. SNPs identified using PGen from additional soybean resequencing projects adding to 500+ soybean germplasm lines in total have been integrated. These SNPs are being utilized for trait improvement using genotype to phenotype prediction approaches developed in-house. In order to browse and access NGS data easily, we have also developed an NGS resequencing data browser (http://soykb.org/NGS_Resequence/NGS_index.php) within SoyKB to provide easy access to SNP and downstream analysis results for soybean researchers.ConclusionPGen workflow has been optimized for the most efficient analysis of soybean data using thorough testing and validation. This research serves as an example of best practices for development of genomics data analysis workflows by integrating remote HPC resources and efficient data management with ease of use for biological users. PGen workflow can also be easily customized for analysis of data in other species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1227-y) contains supplementary material, which is available to authorized users.

Chemical Structure Similarity Search for Ligand-based Virtual Screening: Methods and Computational Resources.

For many years the assumption that "Chemical compounds with similar structures may have similar activities" has been a foundation for lead identification. The similarity can be computed based upon topological, steric, electronic, and/or physical properties. The chemical structure similarity search differs from the chemical substructure search in that the former requires assessment of the properties of each compound and thus no filter can be applied for skipping structures before they are assessed to accelerate the computation. The latter can be accelerated by pre-screening compounds and omitting those that miss one (or more) specified fragments from the query. Moreover, three-dimensional similarity search requires superimposing many conformation pairs for each compound in the library. This makes 3-D similarity search algorithms time-consuming, and in general requires high performance computing (HPC) resources. This review will summarize recent progress in the techniques for HPC-supported two and three-dimensional chemical structure similarity search algorithms, and their applications in ligand-based virtual screening.

Automating NEURON Simulation Deployment in Cloud Resources.

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the OpenStack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon's proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model.

Quasi real-time FEM calibration by 3D displacement measurements of large shaking table tests using HPC resources

The present paper concerns the implementation of a methodology aiming to achieve a quasi real-time calibration of Finite Element Models (FEMs) of large structural mock-ups during shaking table tests. The damage achieved after each test step is commonly evaluated by visual inspection, since running large numerical analyses or processing the experimental data for damage estimation with common computing resources is very time-consuming and in case of a prolonged stand-by the shaking tables oil temperature stability can be put at risk. On the other hand, the specimen damage level can be monitored through the modal parameters estimated using Finite Element Analyses (FEAs), but FEMs require to be calibrated to provide accountable results.In the proposed approach the FEM calibration was carried out using 3D motion data of a large number of passive markers. They were acquired and processed by a dedicated Displacement Data Processing (DDP) procedure combining the Savitzky-Golay filtering for data noise reduction and the convolution derivation approach for the extraction of the motion parameters. To obtain results in reasonable time between test steps (a few minutes) the methodology exploits the hardware and software resources available on the ENEA High-Performance Computing (HPC) system. Also, the proposed approach allowed to integrate within a single web interface the possibility to share the seismic experiments in real time, while providing updated FEM calibration at each step of the test sequence during the experimental session.

HPC and the Big Data challenge

High performance computing (HPC) and Big Data are technologies vital for advancement in science, business and industry. HPC combines computing power of supercomputers and computer clusters, and parallel and distributed processing techniques for solving complex computational problems. The term Big Data refers to the fact that more data are being produced, consumed and stored than ever before. This is resulting in datasets that are too large, complex, and/or dynamic to be managed and analysed by traditional methods. Access to HPC systems and the ability to model, simulate and manipulate massive and dynamic data, is now critical for research, business and innovation. In this paper an overview of HPC and Big Data technology is presented. The paper outlines the advances in computer technology enabling Peta and Exa scale and energy efficient computing, and Big Data challenges of extracting meaning and new information from the data. As an example of HPC and Big Data synergy in risk analysis, a case study of processing close-call data is conducted using HPC resources at the University of Huddersfield. A parallel program was designed and implemented on the university's Hadoop cluster to speed up processing of unstructured free form text records pertaining to close call railway events, in order to identify potential risks and incidents. This case study demonstrates the benefits of using HPC with parallel programming techniques, and the improvements achieved compared to serial processing on a standard workstation computer system. However, it also highlights the challenges in risk analysis of Big Data that require novel approaches in HPC system and software design.

Exploring the Design Tradeoffs for Extreme-Scale High-Performance Computing System Software

Owing to the extreme parallelism and the high component failure rates of tomorrow's exascale, high-performance computing (HPC) system software will need to be scalable, failure-resistant, and adaptive for sustained system operation and full system utilizations. Many of the existing HPC system software are still designed around a centralized server paradigm and hence are susceptible to scaling issues and single points of failure. In this article, we explore the design tradeoffs for scalable system software at extreme scales. We propose a general system software taxonomy by deconstructing common HPC system software into their basic components. The taxonomy helps us reason about system software as follows: (1) it gives us a systematic way to architect scalable system software by decomposing them into their basic components; (2) it allows us to categorize system software based on the features of these components, and finally (3) it suggests the configuration space to consider for design evaluation via simulations or real implementations. Further, we evaluate different design choices of a representative system software, i.e. key-value store, through simulations up to millions of nodes. Finally, we show evaluation results of two distributed system software, Slurm++ (a distributed HPC resource manager) and MATRIX (a distributed task execution framework), both developed based on insights from this work. We envision that the results in this article help to lay the foundations of developing next-generation HPC system software for extreme scales.

XGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud.

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching.

Neuroscience Gateway � Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research

Neuroscience Gateway � Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research

High performance computing in multi-scale modeling, graph science and meta-heuristic optimization

One of the main activities within the Group for Scientific Computing at the Faculty of Science are methods for efficiently utilizing real parallel architectures, typically clusters of SMP nodes, shared-memory systems, and GPUs. Focus is on the design, development and implementation of parallel algorithms and data structures for fundamental scientific and engineering problems. Message Passing Interface (MPI) is an important paradigm that still poses interesting design and implementation problems, especially combined with other programming models, like CUDA. In addition to standard HPC (High Performance Computing) technology stack, the Group also utilizes computing stacks like Hadoop and Spark. In this paper we present a short review of the recent research of the Group, focused on large-scale applications in various research fields with references to original articles. The first part considers multi-scale muscle modeling in mixed MPI-CUDA environment. In our approach, a finite element macro model is coupled with the microscopic Huxley kinetics model. The original approach in scheduling tasks within multi-scale simulation ensures good load balance, leading to speed-up of over two orders of magnitude and high scalability. The second part considers application of HPC in graph science for the task of establishing the basic structural features of the minimum-ABC index trees. In order to analyze large amounts of data (all trees of certain order) we utilize grid computing services like storage and computing in order to reduce analysis time up to three orders of magnitude. The last part presents WoBinGO framework for solving optimization problems on HPC resources. It overcomes the shortcomings of earlier static pilot-job frameworks by providing elastic resource provisioning using adaptive allocation of jobs with limited lifetime. The obtained results show that despite WoBinGO's adaptive and frugal allocation of computing resources, it provides significant speed-up when dealing with problems with computationally expensive evaluations, as found in hydro-informatics and market risk management.

Visualizations of molecular dynamics simulations of high-performance polycrystalline structural ceramics

The implications of improving the strength of synthetic ceramics are described.HPC simulations are having a significant impact on engineered materials.Visualizing the data from LAMMPS required the development of a custom program.VTK was used to connect the atoms. 3D Studio Max was used to create the images.VisIt accomplishes the feat of drawing more than 70 million atoms at once. Initiated by the Department of Defense (DOD) High Performance Computing Modernization Program (HPCMP), the Data Analysis and Assessment Center (DAAC), serves the needs of DOD HPCMP scientists by facilitating the analysis of an ever-increasing volume and complexity of data 1. A research scientist and HPCMP user ran nanoscale molecular dynamics simulations using Large-scale Atomic/Molecular Massively Parallel Simulator code (LAMMPS) from Sandia National Labs. The largest simulation contained over 70 million atoms (Fig. 7). Data sets this large are required to study crack propagation and failure mechanisms that span multiple length scales with atomic resolution. The DAAC developed new methods to visualize the time evolution of data sets this large. The size and complexity of the molecular dynamics simulations and the analytics required the use of DOD HPCMP High Performance Computing (HPC) resources.

Application kernels: HPC resources performance monitoring and variance analysis

SummaryApplication kernels are computationally lightweight benchmarks or applications run repeatedly on high performance computing (HPC) clusters in order to track the Quality of Service (QoS) provided to the users. They have been successful in detecting a variety of hardware and software issues, some severe, that have subsequently been corrected, resulting in improved system performance and throughput. In this work, the application kernels performance monitoring module of eXtreme Data Metrics on Demand (XDMoD) is described. Through the XDMoD framework, the application kernels have been run repetitively on the Texas Advanced Computing Center's Stampede and Lonestar4 clusters for a total of over 14,000 jobs. This provides a body of data on the HPC clusters operation that can be used to statistically analyze how the application performance, as measured by metrics such as execution time and communication bandwidth, is affected by the cluster's workload. We discuss metric distributions, carry out regression and correlation analyses, and use a PCA study to describe the variance and relate the variance to factors such as the spatial distribution of the application in the cluster. Ultimately, these types of analyses can be used to improve the application kernel mechanism, which in turn results in improved QoS of the HPC infrastructure that is delivered to the end users. Copyright © 2015 John Wiley & Sons, Ltd.

Applying Industry 4.0 to the Jurong Island Eco-industrial Park

Abstract This paper presents new insights into the implementation of Industry 4.0 technologies (novel mathematical and computer-based methods) for designing and optimising the eco-industrial park (EIP) of Jurong Island in Singapore. The concept of Industry 4.0 translation to an EIP is introduced, which delivers an expert system allowing users to monitor, control, and optimise the social, economic and environmental repercussions of the industrial activities on Jurong Island. This expert system is a cyber-infrastructure making use of the latest advances in high performance computing (HPC), advanced mathematical modelling, and semantic web technologies. The proposed work addresses end-user driven demands by harnessing HPC resources and advanced data analytics to enable intelligent design, operation, and management of all entities on Jurong Island. The outcome of the work can serve stakeholders from both the private and public sectors.

Open XDMoD: A Tool for the Comprehensive Management of High-Performance Computing Resources

Open XDMoD is an open source tool designed to facilitate the management of high-performance computing (HPC) systems. The Open XDMoD portal provides a rich set of analysis and charting tools that let users quickly display a wide variety of job accounting metrics over any desired timeframe. Two additional tools, which provide quality-of-service metrics and job-level performance data, have been developed and integrated with Open XDMoD to extend its functionality. These tools, combined in an integrated package through Open XDMoD, enable the comprehensive management of HPC resources, allowing HPC center personnel to ensure that the resource is operating efficiently and to determine what applications are running, how efficiently they're running, and what resources they're consuming, all of which are important to optimizing the HPC system.

Quantum Mechanical Study of Mg Rechargeable Battery Electrolyte Stability

The ability of Mg rechargeable battery to provide much higher volumetric capacity at a lower cost makes the technology an attractive candidate for future batteries. Early studies have shown that most organic solvents and common salts fail to enable reversible plating and stripping of the Mg metal anode due to formation of a passive layer blocking ion transport at the electrode interface.1 Particularly, in the case of Mg(TFSI)2 conflicting concentration-dependent cathodic stability at Mg reduction potential is reported.2–6 We studied both the electrochemical stability and chemical decomposition of promising electrolyte candidates using quantum mechanical calculations. We employed a systematic approach, using an electrolyte matrix consisting of 8 anions and 7 solvents. The anion electrochemical stability windows (IP/EA) were calculated for all the combinations in the chosen electrolyte matrix at the B3LYP/6-31+G* level. It was found that all the anions except bis(malonato)borate are predicted as stable in the well solvated form. However, we have previously established that Mg salts are much more prone to ion pair formation at moderate (> 0.5 M) concentrations as compared to Li electrolytes.7 Comparing the electrochemical stability of the well-solvated anion to the ion-paired complex reveals that, although free anions are stable, the ion pair complexes (e.g. Mg2+TFSI-) are significantly easier to reduce. Moreover, Mg2+was identified as the main reduction site, which corresponds to a partial charge transfer at the Mg metal electrode. We calculated all the possible bond dissociation energies (BDE) for the reduced (transient) MgTFSI, MgBF4 and MgBH4 ion pair complexes. Figure 1 shows the result for the Mg – TFSI case. It can be seen that the anion C-S bond is stable for the well solvated form as well as the Mg2+ ion pair form (Mg2+TFSI-) with a BDE of 2.49 eV and 2.34 eV, respectively. However, the Mg+ cation dramatically decreases the C-S BDE to 0.07 eV. The extremely low BDE strongly indicates that the transient Mg+TFSI- complex is likely to rapidly decompose to into the fragments CF3 and [CF3SO2NSO2]Mg. However, such an effect is not observed for BF4 and BH4. This Mg+ initiated TFSI decomposition mechanism may provide an explanation for the conflicting reports on TFSI stability at Mg metal potentials2–5 and the recently found concentration-dependent formation of a blocking layer .6 Acknowledgement:This work was supported as part of the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the U. S. Department of Energy, under Contract No. DE-AC02-06CH11357. High performance computational resources for this research were provided by the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The Materials Project (BES DOE Grant No. EDCBEE) is acknowledged for infrastructure and algorithmic support.

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery.

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study (GWAS) approach, which has been used to identify single nucleotide polymorphisms (SNPs) associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype-phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

HPC-enabled Nuclear Structure Studies – Description and Applications of the Symmetry-adapted No-Core Shell Model

By exploiting symmetries that enable the accounting of vital collective correlations in nuclei, we achieve significantly reduced dimensions for equivalent ultra-large model spaces, and hence resolve the scale explosion problem in nuclear structure calculations, i.e, the explosive growth in computational resource demands with increasing number of particles and size of the spaces in which they reside. As a result, we provide – with the help of High Performance Computing (HPC) resources – first solutions for selected benchmark calculations with remarkable findings of large-deformation and low-spin dominance in low-lying nuclear states. In the framework of a complementary symmetry-adapted study, one is able, facilitated by symmetry-preserving pieces of the inter-nucleon interaction, to accommodate unprecedented shell-model spaces critical to capture the physics governing the Hoyle state in 12C, thereby addressing a 60-year-old puzzle on the emergence of cluster substructures within a no-core shell model framework. All of these findings underline the key role of symmetries in nuclear structure studies.