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Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity maps that provide essential information for Earth subsurface exploration. To rigorously generate EM subsurface models, one must address both the forward problem and the inverse problem. From these subsurface resistivity maps, also referred to as volumes of resistivity distribution, it is possible to extract useful information (lithology, temperature, porosity, permeability, among others) to improve our knowledge about geo-resources on which modern society depends (e.g., energy, groundwater, and raw materials, among others). However, this ability to detect electrical resistivity contrasts also makes EM imaging techniques sensitive to metallic structures whose EM footprint often exceeds their diminutive stature compared to surrounding materials. Depending on target applications, this behavior can be advantageous or disadvantageous. In this work, we review EM modeling and inverse solutions in the presence of metallic structures, emphasizing how these structures affect EM data acquisition and interpretation. By addressing the challenges posed by metallic structures, our aim is to enhance the accuracy and reliability of subsurface EM characterization, ultimately leading to improved management of geo-resources and environmental monitoring. Here, we consider the latter through the lens of a triple helix approach: physics behind metallic structures in EM modeling and imaging, development of computational tools (conventional strategies and artificial intelligence schemes), and configurations and applications. The literature review shows that, despite recent scientific advancements, EM imaging techniques are still being developed, as are software-based data processing and interpretation tools. Such progress must address geological complexities and metallic casing measurements integrity in increasing detail setups. We hope this review will provide inspiration for researchers to study the fascinating EM problem, as well as establishing a robust technological ecosystem to those interested in studying EM fields affected by metallic artifacts. [ABSTRACT FROM AUTHOR]

AbstractSpatial processes observed in various fields, such as climate and environmental science, often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a Gaussian process with a nonstationary Matérn covariance is challenging, as it requires handling the complexity and computational demands associated with modeling the varying spatial dependencies over large and heterogeneous domains. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we utilize the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data by employing a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. We rely on the ExaGeoStat software for large-scale geospatial modeling to implement the nonstationary Matérn covariance for large-scale exact computation of nonstationary Gaussian likelihood. We also assess the performance of the proposed method with synthetic and real datasets at large-scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches. [ABSTRACT FROM AUTHOR]

High reliability must be ensured in distributed storage systems (DSSs) to maintain the stability of warehouse-scale computing and high-performance computing (HPC) systems. For system-level reliability, a repair operation using redundant storage nodes can be used in conjunction with erasure coding (EC), which can also affect the system performance. The existing EC design mainly focused on minimizing the required bandwidth for the repair and storage overheads. However, the computing performance for EC should be considered to achieve high bandwidth in order to exploit back-end network link capacity with heterogeneous and high-speed interconnects over 10 Gbps Ethernet. In this study, a new computing acceleration method for repair operation in EC is proposed using multiple repair paths and modifying the computation kernel on the graphics processing unit (GPU) device. For the Cauchy Reed–Solomon (CRS) codes, the proposed scheme is observed to achieve sufficient repair bandwidth compared to the theoretical bound or exceed the current maximum Ethernet link bandwidth. [ABSTRACT FROM AUTHOR]

For information and communication technology power consumption to be sustainable, the energy efficiency of computing systems must grow at least as fast as the demand for computing services. It is therefore crucial to understand how energy efficiency is evolving and how it will trend in the future, in order to take appropriate measures where possible. This article analyses the evolution of this parameter by analysing high-performance computers from 2008 to 2023, contrasting the results with those from Koomey's Law. It is concluded, after comparing the two that in the studied period and in the near future, energy efficiency continues to grow exponentially but at a slower rate than that established by Koomey's Law (maximum energy efficiency doubles every 2.29 years instead of every 1.57 years). Another interesting result is that energy efficiency grows at a slower rate (doubling every 2.29 years) than performance (doubling every 1.85 years). [ABSTRACT FROM AUTHOR]

Recently, Egypt has recognized the pivotal role of High Performance Computing in advancing science and innovation. Additionally, Egypt realizes the importance of collaboration between different institutions and universities to consolidate their own computational and data resources into a unified platform to serve different disciplines (e.g., scientific, industrial, governmental). Otherwise, additional resources would be needed to be purchased with the associated cost, effort, and time difficulties (e.g., setup, administration, maintenance, etc.). Thus, this paper delves into the architecture and capabilities of the EN-HPCG grid using two different workload management systems: (i) Slurm (Open-Source) and (ii) PBS Pro (Licensed). This paper compares the performance of the grid between Slurm and PBS Pro in specific high-throughput computing (HTC) applications using the NAS Grid parallel benchmark (NGB) to determine which workload manager is more suitable for EN-HPCG. The evaluation includes grid-level performance metrics such as throughput, and the number of tasks completed as a function of time. Also, the presented methodology aims to assist potential partners in their decision-making process to join the EN-HPCG grid, with a focus on the site speed-up metric. Our results showed that, unless an open-source solution without cost and license problems is an obligation (in which case, Slurm is the viable solution), then it is not advisable to integrate a cluster with high-speed hardware with a cluster possessing outdated hardware when using the Slurm scheduler. In contrast, the PBS Pro scheduler takes into account online decision-making in a dynamic environment using a unified grid. [ABSTRACT FROM AUTHOR]

Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics, and bioinformatics, inevitably becoming a significant category of workload on high-performance computing (HPC) systems. Existing AI benchmarks tend to customize well-recognized AI applications, so as to evaluate the AI performance of HPC systems under the predefined problem size, in terms of datasets and AI models. However, driven by novel AI technology, most of AI applications are evolving fast on models and datasets to achieve higher accuracy and be applicable to more scenarios. Due to the lack of scalability on the problem size, static AI benchmarks might be under competent to help understand the performance trend of evolving AI applications on HPC systems, in particular, the scientific AI applications on large-scale systems. In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications. To enable scalability, SAIH builds a set of novel mechanisms for augmenting problem sizes. As the data and model constantly scale, we can investigate the trend and range of AI performance on HPC systems, and further diagnose system bottlenecks. To verify our methodology, we augment a cosmological AI application to evaluate a real HPC system equipped with GPUs as a case study of SAIH. With data and model augment, SAIH can progressively evaluate the AI performance trend of HPC systems, e.g., increasing from 5.2% to 59.6% of the peak theoretical hardware performance. The evaluation results are analyzed and summarized into insight findings on performance issues. For instance, we find that the AI application constantly consumes the I/O bandwidth of the shared parallel file system during its iteratively training model. If I/O contention exists, the shared parallel file system might become a bottleneck. [ABSTRACT FROM AUTHOR]

A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

Abstract Background Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. Results Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a “subpopulation aware” 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). Conclusions This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

Significant progress has occurred in interferometric synthetic aperture radar (InSAR), emerging as a crucial technique for monitoring surface deformation. This evolution is attributed to expanded synthetic aperture radar (SAR) data availability and improved data quality. However, effectively managing and processing SAR big data presents substantial challenges for algorithms and pipelines, especially in large-scale contexts. In this article, we introduce a parallel time-series InSAR processing platform that leverages high-performance computing (HPC) clusters for efficiently managing and processing large-scale SAR data and incorporates graphics processing unit (GPU) acceleration to significantly enhance the speed and efficiency of specific InSAR processing algorithms. Our approach encompasses high-quality data compression, integration of classic InSAR models, and the introduction of a robust distributed scatterer InSAR method for time-series processing. The platform efficiently handles massive data, featuring a parallel optimization tool for acceleration. In addition, it provides web-based two-dimensional (2-D) result visualization and 3-D outcome representation for comprehensive user understanding. To illustrate our platform's capabilities, we applied it to 40 Sentinel-1 SAR data scenes from Tibet (2017–2019). Our data compression technique notably reduces data size, reducing mask data by 87.5% and coherence data to 25% of its original size. Leveraging HPC and GPU, we achieved a 50% reduction in registration computation time. This study offers valuable insights and a comprehensive platform for InSAR practitioners, facilitating calculations and enhancing comprehension of surface deformation processes. Our system's improved processing efficiency, coupled with a variety of InSAR methods, makes it an alternative choice for InSAR data handling and analysis.

Background: Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. Results: Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). Conclusions: This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) environment provides researchers with high level high-performance computing application services with unified access entrance, unified use method and user technical support through shielding the heterogeneity of job management system, access mode, management system and so on. With the development of the environment, more and more supercomputing centers, application communities, and service platforms are connected. Accounts of the supercomputing centers, communities, and service platforms are required to log in to the HPC environment using their original ways. The existing high-performance computing environment supports only grid accounts authenticated by LDAP (lightweight directory access protocol). Application communities and service platforms have their own users and different authentication modes. In order to provide a unified authentication center for the environment, this paper studies the multi-source account authentication technology and develops the multi-source user authentication system. At present, the HPC environment has supported multiple super computing centers. The major communities and platforms in the “high-performance computing” special project also have realized the connection with the national HPC environment through the multi-source account authentication technology. After the connection, accounts of the communities and platforms can log in with the environment grid accounts, at the same time use relevant resources.

Summary: Sequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs. [ABSTRACT FROM AUTHOR]

We present MultiPsi, an open‐source MCSCF program for the calculation of ground and excited states properties of strongly correlated systems. The program currently implements a general MCSCF code with excited states available using either state‐averaging or linear response. It is written in a highly modular fashion using Python/C++ which makes it well suited as a development platform, enabling easy prototyping of novel methods, and as a teaching tool using interactive notebooks. The code is also very efficient and designed for modern high‐performance computing environments using hybrid OpenMP/MPI parallelization. This efficiency is demonstrated with the calculation of the CASSCF energy and linear response of a molecule with more than 700 atoms as well as a fully optimized conventional CI calculation on more than 400 billion determinants. This article is categorized under:Software > Quantum ChemistryElectronic Structure Theory > Ab Initio Electronic Structure MethodsTheoretical and Physical Chemistry > Spectroscopy [ABSTRACT FROM AUTHOR]

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. [ABSTRACT FROM AUTHOR]

The present work assesses the impact - in terms of time to solution, throughput analysis, and hardware scalability - of transferring computationally intensive tasks, found in compressible reacting flow solvers, to the GPU. Attention is focused on outlining the workflow and data transfer penalties associated with "plugging in" a recently developed GPU-based chemistry library into (a) a purely CPU-based solver and (b) a GPU-based solver, where, except for the chemistry, all other variables are computed on the GPU. This comparison allows quantification of host-to-device (and vice versa) data transfer penalties on the overall solver speedup as a function of mesh and reaction mechanism size. To this end, a recently developed GPU-based chemistry library known as UMChemGPU is employed to treat the kinetics in the flow solver KARFS. UMChemGPU replaces conventional CPU-based Cantera routines using a matrix-based formulation. The impact of i) data transfer times, ii) chemistry acceleration, and iii) the hardware architecture is studied in detail in the context of GPU saturation limits. Hydrogen and dimethyl ether (DME) reaction mechanisms are used to assess the impact of the number of species/reactions on overall/chemistry-only speedup. It was found that offloading the source term computation to UMChemGPU results in up to 7X reduction in overall time to solution and four orders of magnitude faster source term computation compared to conventional CPU-based methods. Furthermore, the metrics for achieving maximum performance gain using GPU chemistry with an MPI + CUDA solver are explained using the Roofline model. Integrating the UMChemGPU with an MPI + OpenMP solver does not improve the overall performance due to the associated data copy time between the device (GPU) and host (CPU) memory spaces. The performance portability was demonstrated using three different GPU architectures, and the findings are expected to translate to a wide variety of high-performance codes in the combustion community. [ABSTRACT FROM AUTHOR]

Matrix multiplication is ubiquitous in high-performance applications. It will be a significant part of exascale workloads where power is a big concern. This work experimentally studied the power efficiency of three matrix multiplication algorithms: the definition-based, Strassen's divide-and-conquer, and an optimized divide-and-conquer. The study used reliable on-chip integrated voltage regulators for measuring the power. Interactions with memory, mainly cache misses, were thoroughly investigated. The main result was that the optimized divide-and-conquer algorithm, which is the most time-efficient, was also the most power-efficient, but only for cases that fit in the cache. It consumed drastically less overall energy than the other two methods, regardless of placement in memory. For matrix sizes that caused a spill to the main memory, the definition-based algorithm consumes less power than the divide-and-conquer ones at a high total energy cost. The findings from this study may be of interest when cutting power usage is more vital than running for the shortest possible time or least amount of energy. [ABSTRACT FROM AUTHOR]

Ion Mobility coupled with Mass Spectrometry (IM-MS) stands as a strong analytical method for structurally characterizing complex molecules. In IM-MS, the sample under investigation is ionized and propelled by an electric field into a drift tube, which collides against a buffer gas. The separation of the ion gas phase is then measured through the differences in their rotationally averaged Collision Cross-Section (CCS) values. The effectiveness of the measured Collision Cross-Section (CCS) for structural characterization critically depends on the validation against theoretical calculations. This validation process relies on intensive molecular mechanics simulations, which can be computationally demanding, especially for large systems such as molecular assemblies and viruses. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS experimental data. This work presents the MassCCS software, which considerably increases the CCS simulation performance by implementing a linked-cell-based algorithm, incorporating High-Performance Computing (HPC) techniques. We performed extensive tests regarding the system size, shape, and number of CPU cores. Experimental results reveal speedups up to 3 orders of magnitude faster than Collision Simulator for Ion Mobility Spectrometry (CoSIMS) and High-Performance Collision Cross Section (HPCCS), optimized solutions for CCS simulations, for a single node execution. In addition, we extended MassCCS at the inter-node level by employing OpenMP Cluster (OMPC). OMPC is an innovative programming model designed for the development of HPC applications. It streamlines the development process and simplifies software maintenance using only OpenMP directives. Notably, OMPC delivers a performance level comparable to a pure MPI implementation. This enhancement enabled expensive CCS calculations using nitrogen buffer gas for large systems such as human adenovirus with ∼11 million atoms in just ∼4 min, making MassCCS the most performant software nowadays, to the best of our knowledge. MassCCS is available as free software for Academic use at https://github.com/cces-cepid/massccs. • High-performance Collision Cross-Section software using linked-cell algorithm & HPC techniques. • Our implementation performs up to three orders of magnitude faster than current alternatives in single-node execution for large systems. • Inter-node implementation via OpenMP Cluster, a streamlined programming model for HPC apps, simplifying development and maintenance with OpenMP directives. [ABSTRACT FROM AUTHOR]

High-performance computing-based electromagnetic (EM) emulators are used to simulate real-time complex EM wave interactions between multiple radar targets, transmitters, and receivers. The radar cross section (RCS) of the radar targets are required to be stored as a table; however, the needed storage size increases dramatically with the angle and frequency sampling density. In this article, we present an innovative approach of constructing a concise spherical harmonic-based anisotropic point scatterer model that the emulators can use as part of the computations. The point scatterer model is constructed directly from the precomputed RCS data. First, we use only the monostatic RCS data and compute the spherical harmonic-based monostatic point scatterer model by solving a linear least-squares problem which has a group sparsity constraint. Then, we further compute the spherical harmonic-based bistatic point scatterer model using the full bistatic RCS data. The problem is formulated as a bilinear least-squares problem. The problem is solved using the normalized iterative algorithm, which linearly solves two parameters in a back and forth manner. The results show that the point scatterer model can effectively represent the bistatic RCS data of a radar target. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of molecular docking data, focusing on the binding interactions of the natural compounds apigenin and luteolin with the proteins TP-53, pRb, and APOBEC, in comparison to conventional pharmacological ligands. Advanced bioinformatics techniques were employed to evaluate and contrast binding energies, showing that apigenin and luteolin demonstrate significantly higher affinities for TP-53, pRb, and APOBEC, with binding energies of -6.9 kcal/mol and -6.6 kcal/mol, respectively. These values suggest strong potential for therapeutic intervention against HPV-16. Conventional ligands, by comparison, exhibited lower affinities, with energies ranging from -4.5 to -5.5 kcal/mol. Additionally, protein-protein docking simulations were performed to assess the interaction between HPV-16 E6 oncoprotein and tumor suppressors TP-53 and pRb, which revealed high binding energies around -976.7 kcal/mol, indicative of their complex interaction. A conversion formula was applied to translate these protein-protein interaction energies to a comparable scale for non-protein interactions, further underscoring the superior binding potential of apigenin and luteolin. These findings highlight the therapeutic promise of these natural compounds in preventing HPV-16-induced oncogenesis, warranting further experimental validation for clinical applications.

The plethora of complex Artificial Intelligence (AI) algorithms and available High-Performance Computing (HPC) power stimulates the expeditious development of AI components with heterogeneous designs. Consequently, the need for cross-stack performance benchmarking of AI-HPC systems has rapidly emerged. In particular, the de facto HPC benchmark, LINPACK, cannot reflect the AI computing power and input/output performance without a representative workload. Current popular AI benchmarks, such as MLPerf, have a fixed problem size and therefore limited scalability. To address these issues, we propose an end-to-end benchmark suite utilizing automated machine learning, which not only represents real AI scenarios, but also is auto-adaptively scalable to various scales of machines. We implement the algorithms in a highly parallel and flexible way to ensure the efficiency and optimization potential on diverse systems with customizable configurations. We utilize Operations Per Second (OPS), which is measured in an analytical and systematic approach, as a major metric to quantify the AI performance. We perform evaluations on various systems to ensure the benchmark’s stability and scalability, from 4 nodes with 32 NVIDIA Tesla T4 (56.1 Tera-OPS measured) up to 512 nodes with 4096 Huawei Ascend 910 (194.53 Peta-OPS measured), and the results show near-linear weak scalability. With a flexible workload and single metric, AIPerf can easily scale on and rank AI-HPC, providing a powerful benchmark suite for the coming supercomputing era.

This short review shows how innovative processing units—including graphical processing units (GPUs)—are used in high-performance computing (HPC) in meteorology, introduces current scientific studies relevant to HPC, and discusses the latest topics in meteorology accelerated by HPC computers. The current status surrounding HPC is distinctly complicated in both hardware and software terms, and flows similar to fast cascades. It is difficult to understand and follow the status for beginners; they need to overcome the obstacle of catching up on the information on HPC and connecting it to their studies. HPC systems have accelerated weather forecasts with physical-based models since Richardson's dream in 1922. Meteorological scientists and model developers have written the codes of the models by making the most of the latest HPC technologies available at the time. Several of the leading HPC systems used for weather forecast models are introduced. Each institute chose an HPC system from many possible alternatives to best match its purposes. Six of the selected latest topics in high-performance computing in meteorology are also reviewed: floating points; spectral transform in global weather models; heterogeneous computing; exascale computing; co-design; and data-driven weather forecasts. [ABSTRACT FROM AUTHOR]

High-Performance Computing (HPC) has evolved into a tool that is essential to every researcher's work. The vast majority of issues that arise in contemporary research may be simulated, explained, or put to the test through the use of computer simulations. Researchers often struggle with computational issues while concentrating on the issues that arise from the study. Because the majority of researchers have a minimal or nonexistent understanding of low-level computer science, it tends to view computer programs as extensions of the thoughts and bodies rather than as fully independent systems. As a result of the fact that computers do not function in the same manner as people do, the typical outcome is low-performance computing in situations where high-performance computing would be expected. [ABSTRACT FROM AUTHOR]

Reliability analysis and performance evaluation are complementary methods to quantify nonfunctional aspects of a system. However, a range of factors such as concurrency and heterogeneity quickly exacerbate the state-space explosion problem when attempting detailed system-level modeling and simulation of high-performance computing (HPC) systems. To overcome these impediments to modeling and analysis, this article develops a hierarchical model of an application that implements checkpointing running in an HPC environment subject to application, network, and system-wide outages. The modeling approach ensures that the number of states is linear in the number of checkpoints and possesses a low constant factor for the number of recovery states most relevant to the external influences contributing to degraded application performance. We illustrate the types of analysis enabled by the model through a series of examples with parameters determined empirically from data logs of the Blue Waters supercomputer located at the University of Illinois at Urbana–Champaign. A comprehensive comparative analysis of the model parameters indicates that lowering the failure rate of network nodes would most significantly reduce application downtime. We also discuss how the modeling approach can be used to objectively assess both current and hypothetical future systems to identify competitive designs and enhancements. [ABSTRACT FROM AUTHOR]

Virtual materials design requires not only the simulation of a huge number of systems, but also of systems with ever larger sizes and through increasingly accurate models of the electronic structure. These can be provided by density functional theory (DFT) using not only simple local approximations to the unknown exchange and correlation functional, but also more complex approaches such as hybrid functionals, which include some part of Hartree–Fock exact exchange. While hybrid functionals allow many properties such as lattice constants, bond lengths, magnetic moments and band gaps, to be calculated with improved accuracy, they require the calculation of a nonlocal potential, resulting in high computational costs, that scale rapidly with the system size. This limits their wide application. Here, we present a new highly-scalable implementation of the nonlocal Hartree-Fock-type potential into FLEUR—an all-electron electronic structure code that implements the full-potential linearized augmented plane-wave (FLAPW) method. This implementation enables the use of hybrid functionals for systems with several hundred atoms. By porting this algorithm to GPU accelerators, we can leverage future exascale supercomputers which we demonstrate by reporting scaling results for up to 64 GPUs and up to 12,000 CPU cores for a single k-point. As proof of principle, we apply the algorithm to large and complex iron garnet materials (YIG, GdIG, TmIG) that are used in several spintronic applications.

There is an emerging need to design configurable accelerators for the high-performance computing (HPC) and artificial intelligence (AI) applications in different precisions. Thus, the floating-point (FP) processing element (PE), which is the key basic unit of the accelerators, is necessary to meet multiple-precision requirements with energy-efficient operations. However, the existing structures by using high-precision-split (HPS) and low-precision-combination (LPC) methods result in low utilization rate of the multiplication array and long multiterm processing period, respectively. In this article, a configurable FP multiple-precision PE design is proposed with the LPC structure. Half precision, single precision, and double precision are supported. The 100% multiplier utilization rate of the multiplication array for all precisions is achieved with improved speed in the comparison and summation process. The proposed design is realized in a 28-nm process with 1.429-GHz clock frequency. Compared with the existing multiple-precision FP methods, the proposed structure achieves 63% and 88% area-saving performance for FP16 and FP32 operations, respectively. The $4\times $ and $20\times $ maximum throughput rates are obtained when compared with fixed FP32 and FP64 operations. Compared with the previous multiple-precision PEs, the proposed one achieves the best energy-efficiency performance with 975.13 GFLOPS/W. [ABSTRACT FROM AUTHOR]

Controlled-source electromagnetic (CSEM) measurements are complementary data for magnetotelluric (MT) characterization although its methodology on land is not sufficiently developed and tested as in marine environments. Acquiring expertise in CSEM is crucial for surveys in places where MT cannot be performed due to high levels of cultural noise. To acquire that expertise, we perform CSEM experiments in the Vallès fault [Northeast (NE), Spain], where MT results have been satisfactory and allow us to verify the CSEM results. The Vallès basin is relevant for potential heat generation because of the presence of several geothermal anomalies and its nearby location in urban areas. In this article, we present the experimental setup for that region, a 2-D joint MT+CSEM inverse model, several 3-D CSEM simulations in the presence of metallic casing, and its comparison with real data measurements. We employ a parallel and high-order vector finite element algorithm to discretize the governing equations. By using an adapted meshing strategy, different scenarios are simulated to study the influence of the source position/direction and the conductivity model in a metallic casing presence. An excellent agreement between the simulated data and analytical/real field data demonstrates the feasibility of study metallic structures in realistic configurations. Our numerical results confirm that metallic casing strongly influences electromagnetic (EM) responses, making surface measurements more sensitive to resistivity variations near the metallic structure. It could be beneficial getting higher signal-to-noise ratios and sensitivity to deep targets. However, such a casing effect depends on the input model (e.g., conductivity contrasts, frequency, and geometry). [ABSTRACT FROM AUTHOR]

In this article, a large-scale electromagnetic-thermal-mechanical co-simulation solver is implemented, to simulate complex radio frequency components by using a high performance computing framework. The proposed solver integrates the frequency-domain electric field simulation, with time-domain thermal and thermal-induced stress simulations, via a multiphysics coupling iterative process. In order to speed up the cosimulation, a Krylov subspace method with a domain decomposition method (DDM) preconditioner is used. First, the developed multiphysics solver is verified with the commercial software COMSOL Multiphysics. Then, the parallel performance of our cosimulation solver, with different ghost mesh thicknesses, is tested on a supercomputer. Finally, some multiphysics results of filters and a real-world system-in-package (SiP) are obtained with our proposed solver. [ABSTRACT FROM AUTHOR]

Rapidly changing heterogeneous supercomputer architectures pose a great challenge to many scientific communities trying to leverage the latest technology in high-performance computing. Many existing projects with a long development history have resulted in a large amount of code that is not directly compatible with the latest accelerator architectures. Furthermore, due to limited resources of scientific institutions, developing and maintaining architecture-specific ports is generally unsustainable. In order to adapt to modern accelerator architectures, many projects rely on directive-based programming models or build the codebase tightly around a third-party domain-specific language or library. This introduces external dependencies out of control of the project. The presented paper tackles the issue by proposing a lightweight application-side adaptor layer for compute kernels and memory management resulting in a versatile and inexpensive adaptation of new accelerator architectures with little draw backs. A widely used hydrologic model demonstrates that such an approach pursued more than 20 years ago is still paying off with modern accelerator architectures as demonstrated by a very significant performance gain from NVIDIA A100 GPUs, high developer productivity, and minimally invasive implementation; all while the codebase is kept well maintainable in the long-term. [ABSTRACT FROM AUTHOR]

Computing capacity has always been on the upward climb due to the constant technological improvements in semiconductor manufacturing and packaging industry. This growth in computing capability is usually accompanied by a steep rise in heat flux density associated with the electronic component (CPUs or GPUs, for example). High-performance computing (HPC) data centers often employ several of these high-performance devices for crunching their enormous artificial intelligence (AI) or scientific computing workloads. State-of-the-art air cooling technologies throttle after a certain heat flux level and would require bigger heat sinks driving enormous airflow through it, which may not be desirable from a data center operation standpoint. Hence, a lookout for advanced thermal management techniques is quite imperative. In this article, a commercially available intel overclockable CPU i9-9900k was exercised with high performance workloads while employing and evaluating three different cooling technologies namely air-cooled, cold plates, and two-phase (2P) immersion. The high heat carrying capacity associated with cold plates and 2P immersion techniques outperformed the air-cooled solution by constantly yielding higher CPU clock rates up to 41% utilizing cold plates and 51% employing 2P immersion. The performance of cold plates and 2P immersion was evaluated at different functional points (different coolant operating temperatures and 2P coolant types for example) to understand how the technologies would respond in a legacy or modern data center operation. It was again observed that both 2P immersion technology and cold plates provided the least thermal resistance (as low as 0.247 °C/W) path to heat transfer and therefore provided higher computational performance and efficiency compared to air cooling. Further improvement in performance is not limited by cooling but by packaging constraints. Advanced packaging techniques coupled with minimizing the interfacial resistance can reap enhanced performance and energy efficiency gains using these modes of cooling. This article demonstrates the potential increase in virtual machine (VM) performance that can be attained using cold plates or 2P immersion. Authors recommend adoption of liquid cooling in early chip design and architecture phases. Finally, the article provides a generic overclocking methodology that the industry can use to qualify postturbo server class chip performance. [ABSTRACT FROM AUTHOR]

Summary: Training models on large‐scale GPUs‐accelerated clusters are becoming a commonplace due to the increase in complexity and size in deep learning models. One of the main challenges for distributed training is the collective communication overhead for large message sizes: up to hundreds of MB. In this paper, we propose two hierarchical distributed memory multileader AllReduce algorithms optimized for GPU‐accelerated clusters (named lr_lr and lr_rab), in which GPUs inside a computing node perform an intra‐node communication phase to gather and store results of local reduced values to designated GPUs (known as node leaders). Node leaders then keep a role as an inter‐node communicator. Each leader exchanges one part of reduced values to the leaders of the other nodes in parallel. Hence, we are capable of significantly reducing the time for injecting data into the inter‐node network. We also overlap the inter‐node and intra‐node communication by implementing our proposal in a pipelined manner. We evaluate those algorithms on the discrete‐event simulation Simgrid. We show that our algorithms, lr_lr and lr_rab, can cut down the execution time of an AllReduce microbenchmark that uses the logical ring algorithm (lr) by up to 45% and 51%, respectively. With the pipelined implementation, our lr_lr_pipe achieves 15% performance improvement when compared with lr_lr. In addition, the simulation result also projects power savings for the network devices of up to 23% and 32%. [ABSTRACT FROM AUTHOR]

This short review shows how innovative processing units—including graphical processing units (GPUs)—are used in high-performance computing (HPC) in meteorology, introduces current scientific studies relevant to HPC, and discusses the latest topics in meteorology accelerated by HPC computers. The current status surrounding HPC is distinctly complicated in both hardware and software terms, and flows similar to fast cascades. It is difficult to understand and follow the status for beginners; they need to overcome the obstacle of catching up on the information on HPC and connecting it to their studies. HPC systems have accelerated weather forecasts with physical-based models since Richardson’s dream in 1922. Meteorological scientists and model developers have written the codes of the models by making the most of the latest HPC technologies available at the time. Several of the leading HPC systems used for weather forecast models are introduced. Each institute chose an HPC system from many possible alternatives to best match its purposes. Six of the selected latest topics in high-performance computing in meteorology are also reviewed: floating points; spectral transform in global weather models; heterogeneous computing; exascale computing; co-design; and data-driven weather forecasts.

The NAND flash memories have endurance issues in a high-performance computing (HPC) environment because of their blocks' limited lifetime. Hence, the NAND flash block allocation and reclamation policies have a significant impact on its lifetime improvement. To address this problem, the scoring-based method is proposed in this paper, which employs priority-based victim block and active block selection methods. The priority of a block is estimated based on both of the score and a time factor. The comprehensive experiments using both realistic and synthetic benchmark traces are performed. The SSDsim simulation environment is extended to implement this method. The results reveal that in comparison with other methods, the proposed method improves endurance by at least 38%. [ABSTRACT FROM AUTHOR]

We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System. ADIOS 2 addresses scientific data management needs ranging from scalable I/O in supercomputers, to data analysis in personal computer and cloud systems. Version 2 introduces a unified application programming interface (API) that enables seamless data movement through files, wide-area-networks, and direct memory access, as well as high-level APIs for data analysis. The internal architecture provides a set of reusable and extendable components for managing data presentation and transport mechanisms for new applications. ADIOS 2 bindings are available in C++11, C, Fortran, Python, and Matlab and are currently used across different scientific communities. ADIOS 2 provides a communal framework to tackle data management challenges as we approach the exascale era of supercomputing.

An open‐source program named VeloxChem has been developed for the calculation of electronic real and complex linear response functions at the levels of Hartree–Fock and Kohn–Sham density functional theories. With an object‐oriented program structure written in a Python/C++ layered fashion, VeloxChem enables time‐efficient prototyping of novel scientific approaches without sacrificing computational efficiency, so that molecular systems involving up to and beyond 500 second‐row atoms (or some 10,000 contracted and in part diffuse Gaussian basis functions) can be routinely addressed. In addition, VeloxChem is equipped with a polarizable embedding scheme for the treatment of the classical electrostatic interactions with an environment that in turn is modeled by atomic site charges and polarizabilities. The underlying hybrid message passing interface (MPI)/open multiprocessing (OpenMP) parallelization scheme makes VeloxChem suitable for execution in high‐performance computing cluster environments, showing even slightly beyond linear scaling for the Fock matrix construction with use of up to 16,384 central processing unit (CPU) cores. An efficient—with respect to convergence rate and overall computational cost—multifrequency/gradient complex linear response equation solver enables calculations not only of conventional spectra, such as visible/ultraviolet/X‐ray electronic absorption and circular dichroism spectra, but also time‐resolved linear response signals as due to ultra‐short weak laser pulses. VeloxChem distributed under the GNU Lesser General Public License version 2.1 (LGPLv2.1) license and made available for download from the homepage https://veloxchem.org. This article is categorized under:Software > Quantum ChemistryElectronic Structure Theory > Density Functional TheoryTheoretical and Physical Chemistry > Spectroscopy [ABSTRACT FROM AUTHOR]

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping; plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots; second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845; and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping; the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft’s simple encrypted arithmetic library (SEAL). [ABSTRACT FROM AUTHOR]

Summary: This paper presents feasibility studies in utilizing graphics processing units (GPUs) as high‐performance computing hardware with front‐end electronics in high‐scale magnetic confinement thermal fusion experiments. The objective of the research is to provide scalable, high‐throughput, and low‐latency measurements for the runtime tokamak metallic impurities X‐ray diagnostic for the Tungsten Environment in Steady‐State Tokamak (WEST) reactor. The heterogeneous system of front‐end with field‐programmable gate arrays and the back‐end server was introduced to decompose workloads efficiently. It allows the comprehensive evaluation of CPUs and accelerators. In particular, a novel implementation of the back‐end algorithm for GPU with the performance analysis are presented. [ABSTRACT FROM AUTHOR]

With the convergence of high-performance computing (HPC), big data and artificial intelligence (AI), the HPC community is pushing for "triple use" systems to expedite scientific discoveries. However, supporting these converged applications on HPC systems presents formidable challenges in terms of storage and data management due to the explosive growth of scientific data and the fundamental differences in I/O characteristics among HPC, big data and AI workloads. In this paper, we discuss the driving force behind the converging trend, highlight three data management challenges, and summarize our efforts in addressing these data management challenges on a typical HPC system at the parallel file system, data management middleware, and user application levels. As HPC systems are approaching the border of exascale computing, this paper sheds light on how to enable application-driven data management as a preliminary step toward the deep convergence of exascale computing ecosystems, big data, and AI. [ABSTRACT FROM AUTHOR]

Many scientific fields increasingly use high-performance computing (HPC) to process and analyze massive amounts of experimental data while storage systems in today's HPC environments have to cope with new access patterns. These patterns include many metadata operations, small I/O requests, or randomized file I/O, while general-purpose parallel file systems have been optimized for sequential shared access to large files. Burst buffer file systems create a separate file system that applications can use to store temporary data. They aggregate node-local storage available within the compute nodes or use dedicated SSD clusters and offer a peak bandwidth higher than that of the backend parallel file system without interfering with it. However, burst buffer file systems typically offer many features that a scientific application, running in isolation for a limited amount of time, does not require. We present GekkoFS, a temporary, highly-scalable file system which has been specifically optimized for the aforementioned use cases. GekkoFS provides relaxed POSIX semantics which only offers features which are actually required by most (not all) applications. GekkoFS is, therefore, able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of common parallel file systems. [ABSTRACT FROM AUTHOR]

GEYSER, an acronym for Graphic processing units (GPU) cluster computing for Enhanced hYdrothermal SystEms with Reactive transport, is a 3D simulator that includes porosity and permeability evolution for mass and heat transport processes in fractured geological media. The simulator also includes mass porosity and permeability evolution in response to dehydration reactions of hydrous minerals. GEYSER utilizes a finite difference scheme to solve the governing PDEs associated with 3D large-scale hydrothermal systems or geothermal reservoirs. This tool is a high performance code using GPU workstations or cluster technology. The physical processes implemented into the code are those associated with deep hydrogeological complexes where high fluid pressures generated by dehydration reactions can be sufficient to induce hydrofractures that significantly influence the porosity and permeability structures within geological formation. The governing equations are described and implemented and applied to a simplified 3D model of a magmatic intrusion at depth underlying a deep sedimentary cover. Close to ideal, weak scaling is demonstrated on GPU clusters with up to 128 GPUs. The numerical model can be used to investigate and understand coupled and time-dependent hydromechanical and thermodynamic processes at high resolution of the 3D computational domain. Applications include the hydrogeology of volcanic environments or exploitation of sediment-hosted geothermal resources. The code can also be suited for porosity and permeability evolution regarding pressure and temperature reaction rate to rock decarbonization for CO2 sequestration in deep sedimentary formations. [ABSTRACT FROM AUTHOR]

Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master–slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets. [ABSTRACT FROM AUTHOR]