Search

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

A large cluster of High-Performance Computing (HPC) equipment at the Lawrence Livermore National Laboratory in California was retrofitted with an Asetek cooling system. The Asetek system is a hybrid scheme with water-cooled cold plates on high-heat-producing components in the information technology (IT) equipment, and with the remainder of the heat being removed by conventional air-cooling systems. In order to determine energy savings of the Asetek system, data were gathered and analyzed two ways: using top-down statistical models, and bottom-up engineering models. The cluster, “Cabernet”, rejected its heat into a facilities cooling water loop which in turn rejected the heat into the same chilled water system serving the computer-room air handlers (CRAHs) that provided the air-based cooling for the room. Because the “before” and “after” cases both reject their heat into the chilled water system, the only savings is due to reduction in CRAH fan power. The top-down analysis showed a 4% overall energy savings for the data center (power usage effectiveness (PUE) —the ratio of total data center energy to IT energy— dropped from 1.60 to 1.53, lower is better); the bottom-up analysis showed a 3% overall energy savings (PUE from 1.70 to 1.66) and an 11% savings for the Cabernet system by itself (partial PUE of 1.51). Greater savings, on the order of 15-20%, would be possible if the chilled water system was not used for rejecting the heat from the Asetek system. About 37% of the heat from the Cab system was rejected to the cooling water, lower than at other installations.

We apply AI‐assisted large language model (LLM) capabilities of GPT‐3 targeting high‐performance computing (HPC) kernels for (i) code generation, and (ii) auto‐parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT‐based OpenAI Codex available in Visual Studio Code given simple + + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto‐parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple "prompt engineering" follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general‐purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. We expect to provide an initial quantifiable point of reference for code generation in each programming model using a state‐of‐the‐art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human‐computer interactions. [ABSTRACT FROM AUTHOR]

In this article, we address the practical needs for the rapid processing and application of aerial photogrammetry remote sensing images, proposing a method based on high-performance computing technology to achieve the real-time, dynamic orthorectified visualization of aerial remote sensing images. This method differs from traditional orthorectification processing and application workflows, utilizing high-performance parallel computing capabilities to directly achieve orthorectification based on raw aerial images. In this paper, we also compare and analyze four different real-time orthorectified visualization schemes to meet various application needs, evaluating the efficiency and accuracy of the proposed method. Finally, experimental results with unmanned aerial vehicle (UAV) flight image data indicate that this method is feasible, with real-time computational results meeting practical application requirements in terms of effect and performance. [ABSTRACT FROM AUTHOR]

This paper identifies and addresses three key challenges in energy systems analysis—varying assumptions, computational limitations, and coverage of a few indicators only. First, results depend strongly on assumptions, i.e., varying input data. Hence, comparisons and robust results are hard to achieve. To address this, we use a broad range of possible inputs through an extensive literature review by scenario experts. Second, we overcome computational limitations using high-performance computing (HPC) and an automated workflow. Third, by coupling models and developing 13 indicators to evaluate the overall quality of energy systems in Germany for 2030, we include many aspects of security of supply, market impact, life cycle analysis and cost optimization. A cluster analysis of scenarios by indicators reveals three recognizable clusters, separating systems with a high share of renewables clearly from more conventional sets. Additionally, scenarios can be identified which perform very positive for many of the 13 indicators. We conclude that an automated, coupled workflow on supercomputers based on a broad parameter space is able to produce robust results for many important aspects of future energy systems. Since all models and software components are released as open-source, all components of a multi-perspective model-chain are now available to the energy system modeling community. [ABSTRACT FROM AUTHOR]

The behavior of turbulent flows remains a significant unsolved problem in physics. Recently, a large quantity of effort has been directed toward understanding the non-linear interactions of the different flow structures in order to address this challenge. In this paper, different implementations of one exact method for identifying these structures are analyzed. This includes two sequential algorithms and a parallelizable one, developed to handle large-scale data efficiently. The new parallel algorithm offers significant advantages in handling the computational demands of large simulations, providing a more scalable solution for future research. [ABSTRACT FROM AUTHOR]

Nuclear forensics relies on different signatures to identify the source of nuclear material. Such signatures include crystalline structure, chemical composition, and particle morphology. One way to quantify morphology in electron microscope imagery is through image segmentation, where pixels are assigned to several partitions (or groups) that correspond to particles, grains, and other objects of interest within the image. Once pixels are assigned to segments, it is relatively straightforward to quantify other quantities of interest, such as grain size, circularity, etc. However, the range and diversity of microscope images make it difficult to obtain an accurate segmentation automatically. The accuracy of segmentation can be improved through supervised learning, but this requires many images to be manually segmented. Another way to improve the accuracy is to use interactive segmentation. Interactive segmentation requires a human to provide image-specific user input to improve performance. However, the amount of user input (effort) is generally far less than is required for supervised learning. In this paper, we investigate several parallelization strategies to automatically explore the user input parameter space of interactive segmentation algorithms across a large number of images. Specifically, we investigate four different parallelization mechanisms in a high-performance computing (HPC) environment and use the Amdahl fraction to evaluate efficiency on multiple processor cores across multiple nodes. Ultimately, the parallelization strategy that was most efficient utilized the message passing interface integrated with the segmentation and quantification code. This strategy had an Amdahl fraction of 0.985 and a performance of about 0.251 s/image. These results indicate that the parameter space of interactive segmentation algorithms can be efficiently explored using HPC. This opens the door to future work where user input is reduced and where interactive image segmentation algorithms are automatically applied to large image sets. [ABSTRACT FROM AUTHOR]

System-on-chip designs for high-performance computing systems widely use network-on-chip (NoC) technology. The critical metrics such as latency, throughput, and total energy directly impact the performance of NoCs. However, a cycle-accurate simulator takes considerable execution time for different values of packet injection rate. This paper proposes a machine learning approach with various regression models to predict the essential NoC performance metrics. The work explores regression models, including decision trees, K-nearest neighbors, random forest, polynomial regression, and support vector regression, alongside baseline linear regression for 2D and 3D NoC-based architectures. Experiment results demonstrate high accuracy, ranging from 96.72 to 99.96% for 2D and up to 99.99% for 3D NoC-based architectures. Root-mean-squared error values vary from 0.2792 to 0.03143 for 2D NoC and 0.1857 to 0.0063 for 3D NoC architecture. The proposed framework achieves impressive speedup, reaching up to 4678.8X and 46152.8X for system sizes 8 × 8 × 4 and 10 × 10 × 20, respectively. [ABSTRACT FROM AUTHOR]

In an era where technology continually reshapes the landscape of professional practice, it has become relevant to equip engineering students with advanced computational skills beyond programming. This article presents a novel discipline‐based framework designed to integrate advanced computational skills into engineering education. Responding to challenges such as the disconnection between computational abilities and domain‐specific knowledge, and student demotivation due to overwhelming technological challenges, this study aims to validate the impact of the framework on domain learning, computational skill acquisition, and perceived future utility. Implementing a case study approach, we explore the development of high‐performance computing skills within a project‐based learning context in Civil Engineering. Results indicate significant improvements in students' understanding of both computational concepts and the engineering domain, evidenced by enhanced self‐perception and positive Technology Acceptance Model outcomes. The framework facilitated a meaningful connection between computational skills and professional applications, as seen in students' project reflections. Despite the promising results, the necessity for instructors to possess and impart computational knowledge is highlighted as an important factor for successful integration. This study contributes to educational computing research by providing a scalable approach to embedding advanced computational skills in engineering curricula, addressing existing educational challenges, and suggesting directions for future research. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) systems are becoming increasingly important as contemporary exascale applications with demand extensive computational and data processing capability. To optimize these systems, efficient scheduling of HPC applications is important. In particular, because I/O is a shared resource among applications and is becoming more important due to the emergence of big data, it is possible to improve performance by considering the architecture of HPC systems and scheduling jobs based on I/O resource requirements. In this paper, we propose a scheduling scheme that prioritizes HPC applications based on their I/O requirements. To accomplish this, our scheme analyzes the IOPS of scheduled applications by examining their execution history. Then, it schedules the applications at pre-configured intervals based on their expected IOPS to maximize the available IOPS across the entire system. Compared to the existing first-come first-served (FCFS) algorithm, experimental results using real-world HPC log data show that our scheme reduces total execution time by 305 h and decreases costs by USD 53 when scheduling 10,000 jobs utilizing public cloud resources. [ABSTRACT FROM AUTHOR]

Motion-compensated image reconstruction enables new clinical applications of Magnetic Resonance Imaging (MRI), but it relies on computationally intensive algorithms. This study focuses on the Generalized Reconstruction by Inversion of Coupled Systems (GRICS) program, applied to the reconstruction of 3D images in cases of non-rigid or rigid motion. It uses hybrid parallelization with the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing). For clinical integration, the GRICS needs to efficiently harness the computational resources of compute nodes. We aim to improve the GRICS's performance without any code modification. This work presents a performance study of GRICS on two CPU architectures: Intel Xeon Gold and AMD EPYC. The roofline model is used to study the software–hardware interaction and quantify the code's performance. For CPU–GPU comparison purposes, we propose a preliminary MATLAB–GPU implementation of the GRICS's reconstruction kernel. We establish the roofline model of the kernel on two NVIDIA GPU architectures: Quadro RTX 5000 and A100. After the performance study, we propose some optimization patterns for the code's execution on CPUs, first considering only the OpenMP implementation using thread binding and affinity and appropriate architecture-compilation flags and then looking for the optimal combination of MPI processes and OpenMP threads in the case of the hybrid MPI–OpenMP implementation. The results show that the GRICS performed well on the AMD EPYC CPUs, with an architectural efficiency of 52%. The kernel's execution was fast on the NVIDIA A100 GPU, but the roofline model reported low architectural efficiency and utilization. [ABSTRACT FROM AUTHOR]

As computing systems become increasingly larger, high‐performance computing (HPC) is gaining importance. In particular, as hyperscale artificial intelligence (AI) applications, such as large language models emerge, HPC has become important even in the field of AI. Important operations in hyperscale AI and HPC are mainly linear algebraic operations based on tensors. An AB21 supercomputing AI processor has been proposed to accelerate such applications. This study proposes a XEM accelerator to accelerate linear algebraic operations in an AB21 processor effectively. The XEM accelerator has outer product‐based parallel floating‐point units that can efficiently process tensor operations. We provide hardware details of the XEM architecture and introduce new instructions for controlling the XEM accelerator. Additionally, hardware characteristic analyses based on chip fabrication and simulator‐based functional verification are conducted. In the future, the performance and functionalities of the XEM accelerator will be verified using an AB21 processor. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are recent advancements that permit vehicles to communicate with infrastructure and other vehicles, improving road safety and traffic efficiency. One of the difficulties in constructing and maintaining VANETs deals with the consequences of blockage, it may occur when buildings, trees, or other obstructions block radio signals between vehicles. However, the presence of vehicles as obstacles can severely impact the performance of VANETs. In this paper, an efficient machine learning (ML) model is developed to identify the impact of vehicle obstacles in VANETs. The proposed optimizable tree ML model showed better results in comparison to the other existing models. The results of the proposed model are superior as compared with other existing models in terms of nine performance measures namely, recall, specificity, balanced accuracy, accuracy, error rate, precision, F1 score, FNR and FPR. The values of these nine performance matrices for the proposed optimizable tree ML model are 0.99, 0.99, 0.99, 0.99, 0.01, 0.99, 0.99, 0.01, and 0.01 respectively. [ABSTRACT FROM AUTHOR]

The key factor in reducing the consumed energy when dependent-tasks applications are scheduled on DVFS-enabled computing platforms is task execution time slots. The unique and axiomatic approach to reduce the energy consumption on such platforms involves scaling down the execution frequency of each task within its execution time slot, provided a suitable scaling-down frequency is available. Regrettably, scheduling algorithms often shrink task execution time slots due to minimizing task completion times. This paper presents BlueMoon, a mechanism that reschedules the application tasks to extend the execution time slot of each task while ensuring that the overall completion time of the application tasks remains unaffected. BlueMoon is implemented and tested on numerous schedules of application graphs. The experimental results, conducted through computer simulations, demonstrate that BlueMoon substantially extends the execution time slots of tasks when compared to other mechanisms. [ABSTRACT FROM AUTHOR]

As virtual and actual universes merge inside the creating metaverse, requests have pointedly ascended for continuous, intuitive, and intense encounters. The ability of the metaverse to effectively analyze and render complicated links and information supplied by clients is critical for realizing that goal. These demanding computational demands are starting to be supported by parallel processing, and high-performance computing (HPC) is beyond uncertainty key to this domain. The integrative framework presented in this paper addresses the core challenges of inertness, flexibility, and ease of use while integrating equal registration into the metaverse. The system enables prompt handling of client actions and quick response times by distributing calculations over multiple processors, which is essential for the seamless client experience. It also manages the vast amount of metaverse material and interactions as well as the various data processing needs. The paper looks at intrinsic equal processing difficulties in this unique climate, including creating versatile and energy-effective equal calculations that consider load adjusting and asset designation. It features the need to democratize equal figuring assets to produce metaverse extension while accentuating the significance of information protection and security conventions in multiclient settings. The cooperative energy between metaverse development and equal registering progressions vows to push limits, empowering remarkable degrees of virtual submersion and collaboration. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns. [ABSTRACT FROM AUTHOR]

In this study, a multi-phase-field (MPF) framework for predicting epitaxial grain growth in selective laser melting (SLM) additive manufacturing (AM) with multi-track and multi-layer scanning was developed. The spatiotemporal change in temperature was approximated using the Rosenthal equation, which provides a theoretical solution for the temperature distribution due to a moving point heat source. The powder bed was modeled as a polycrystalline layer. Large-scale MPF simulations for SLM-AM were performed using parallel computing with multiple graphics processing units. Using the MPF framework developed herein, we simulated SLM-AM with four tracks and four layers for 316L stainless steel. By observing the epitaxial grain growth process on two-dimensional cross-sections and in three dimensions, we clarified a typical growth procedure of grains with characteristic 3D shapes. The MPF framework will potentially enable a systematic estimation of the material microstructures formed during SLM-AM. [ABSTRACT FROM AUTHOR]

As computing systems become increasingly larger, high-performance comput-ing (HPC) is gaining importance. In particular, as hyperscale artificial intelli-gence (AI) applications, such as large language models emerge, HPC has become important even in the field of AI. Important operations in hyperscale AI and HPC are mainly linear algebraic operations based on tensors. An AB21 supercomputing AI processor has been proposed to accelerate such applica-tions. This study proposes a XEM accelerator to accelerate linear algebraic operations in an AB21 processor effectively. The XEM accelerator has outer product-based parallel floating-point units that can efficiently process tensor operations. We provide hardware details of the XEM architecture and intro-duce new instructions for controlling the XEM accelerator. Additionally, hard-ware characteristic analyses based on chip fabrication and simulator-based functional verification are conducted. In the future, the performance and func-tionalities of the XEM accelerator will be verified using an AB21 processor.

We present ichor, an open‐source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post‐processing. Raw data can be directly processed by ichor to create machine learning‐ready datasets. In addition to powerful data‐related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple‐to‐use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine‐learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end‐to‐end data procurement, data management, and analysis solution for machine‐learning force‐field development. [ABSTRACT FROM AUTHOR]

When you perceive or remember something, other related things come to mind, affecting how these competing items are subsequently perceived and remembered. Such behavioural consequences are believed to result from changes in the overlap of neural representations of these items, especially in the hippocampus. According to multiple theories, hippocampal overlap should increase (integration) when there is high coactivation between cortical representations. However, prior studies used indirect proxies for coactivation by manipulating stimulus similarity or task demands. Here, we induce coactivation in visual cortex more directly using closed-loop neurofeedback from real-time functional magnetic resonance imaging (fMRI). While viewing one object, participants were rewarded for activating the representation of another object as strongly as possible. Across multiple real-time fMRI sessions, participants succeeded in using this neurofeedback to increase coactivation. Compared with a baseline of untrained objects, this protocol led to memory integration in behaviour and the brain: the trained objects became harder for participants to discriminate behaviourally in a categorical perception task and harder to discriminate neurally from patterns of fMRI activity in their hippocampus as a result of losing unique features. These findings demonstrate that neurofeedback can be used to alter and combine memories. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'. [ABSTRACT FROM AUTHOR]

The International Journal of Networked and Distributed Computing has been pioneering research that advances our understanding of networked and distributed computing. As the newly appointed Editor-in-Chief, in this editorial, I articulate my vision for the future of the journal, emphasizing its commitment to maintaining rigorous standards while embracing technological advancements. Key areas of focus will be extended to include the Quantum Internet, Serverless Computing, Distributed Intelligence, the convergence of HPC and Cloud Continuum, and sustainable computing practices. Innovative initiatives, such as enhancing the editorial board, forging strategic research partnerships, and, possibly, expanding article types, are introduced to elevate the journal's impact and relevance. The feasibility of establishing an ad hoc periodic series of works realized in collaboration with key researchers in the different fields, focused on recent trends, findings, and technological roadmaps will be investigated. The rigorous editorial process that characterizes the journal, aimed at ensuring academic integrity and transparency, will not be affected. [ABSTRACT FROM AUTHOR]

Gaussian Processes have become an indispensable part of the spatial statistician’s toolbox but are unsuitable for analyzing large datasets because of the significant time and memory needed to fit the associated model exactly. Vecchia Approximation is widely used to reduce the computational complexity and can be calculated with embarrassingly parallel algorithms. While multi-core software has been developed for Vecchia Approximation, software designed to run on graphics processing units (GPUs) is lacking, despite the tremendous success GPUs have had in statistics and machine learning. We compare three different ways to implement Vecchia Approximation on a GPU: two of which are similar to methods used for other Gaussian Process approximations and one that is new. Our new method exploits the properties of Vecchia Approximation to nearly eliminate thread synchronization and reduce memory access times. We show that our new method outperforms the other two and then compare it to existing multi-core and GPU-accelerated software by fitting Gaussian Process models on various datasets, including a large spatial-temporal dataset of n > 10 6 points collected from an Earth-observing satellite. Our method works on larger datasets and provides higher predictive accuracy than existing GPU methods, and it runs up to 20 times faster than a single-core CPU implementation of Vecchia Approximation. [ABSTRACT FROM AUTHOR]

Abstract Artificial Intelligence (AI) is already a reality in health systems, bringing benefits to patients, healthcare providers, and other stakeholders in the health care. To further leverage AI in health, Belgium is advised to make policy-level decisions about how to fund, design and undertake actions focussing on data access and inclusion, IT-infrastructure, legal and ethical frameworks, public and professional trust, in addition to education and interpretation. EU initiatives, such as European Health data space (EHDS), the Genomics Data Infrastructure (GDI) and the EU Cancer Imaging Infrastructure (EUCAIM) are building EU data infrastructures. To continue these positive developments, Belgium should continue to invest and support existing European data infrastructures. At the national level, a clear vision and strategy need to be developed and infrastructures need to be harmonized at the European level.

Abstract Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation’s effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ . Scientific contribution This work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Abstract After the 2011 Tohoku-oki earthquake, a seafloor observation system observed rapid landward deformation. These seafloor observation data are potentially explicable via modeling of a low-viscosity zone under the subducting plate, called the lithosphere–asthenosphere boundary (LAB). However, the effect of a low-viscosity LAB has not been empirically examined in past earthquakes because of the absence of seafloor observation data, which are expected to have high sensitivity in the detection of a low-viscosity LAB. The Nankai Trough, southwest Japan, is one location where seafloor geodetic stations are in operation that could detect a low-viscosity LAB after a future earthquake. Therefore, we quantitatively model how the low-viscosity of the LAB is expected to affect postseismic crustal deformation following an anticipated future large earthquake under the Nankai Trough. Calculating viscoelastic deformation using a model that takes into account the realistic crustal structure in the southwestern Japanese Archipelago, we examine the effects of LAB viscosity on surface deformation patterns. The results show for very low LAB viscosities ( $$2.5 \times 10^{17}$$ 2.5 × 10 17 Pa s), rapid landward displacement as large as 38 cm/yr occurs in the offshore area, with uplift and subsidence patterns highly dependent on the viscosity structure. Especially in the first year after the earthquake, the difference in deformation between the cases with and without a low-viscosity LAB was much larger than the observational error level of the current seafloor observation system. For such low LAB viscosities, the probability of detection is therefore high. On the other hand, for moderately low LAB viscosities ( $$2.5 \times 10^{18}$$ 2.5 × 10 18 Pa s), the deformation patterns in the cases with and without a low-viscosity LAB are similar. Indeed, the difference in displacement lies within the observational error level of the current observational network, so detection is expected to be difficult for a LAB of only moderately low-viscosity. However, such a LAB may be detected by improving observational accuracy in the Global Navigation Satellite System–acoustic observation technique by performing more frequent measurements. Additional detection potential lies in expected future accuracy improvements in vertical displacement estimates at the DONET stations. Graphical Abstract

Summary: With the rise of exascale systems and large, data‐centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service‐based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application‐specific diagnostic data and performance data addresses these needs. We present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi‐GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring. [ABSTRACT FROM AUTHOR]

Summary: Burst‐buffering is a promising storage solution that introduces an intermediate high‐throughput storage buffer layer to mitigate the I/O bottleneck problem that the current high‐performance computing (HPC) platforms suffer. The existing Markov‐Chain based probabilistic I/O scheduling utilizes the load state of burst‐buffers and the periodic characteristics of applications to reduce I/O congestion due to the limited capacity of burst‐buffers. However, this probabilistic approach requires consistent I/O characteristics of applications, including similar I/O duration and long application length, in order to obtain an accurate I/O load estimation. These consistency conditions do not often hold in realistic situations. In this paper, we propose a generic framework of dynamic probabilistic I/O scheduling based on application clustering (DPSAC) to make applications meet the consistency requirements. According to the I/O phase length of each application, our scheme first deploys a one‐dimensional K‐means clustering algorithm to cluster the applications into clusters. Next, it calculates the expected workload of each cluster through the probabilistic model of applications and then partitions the burst‐buffers proportionally. Then, to handle dynamic changes (join and exit) of applications, it updates the clusters based on a heuristic strategy. Finally, it applies the probabilistic I/O scheduling, which is based on the distribution of application workload and the state of burst‐buffers, to schedule I/O for all the concurrent applications to mitigate I/O congestion. The simulation results on synthetic data show that our DPSAC is effective and efficient. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is already a reality in health systems, bringing benefits to patients, healthcare providers, and other stakeholders in the health care. To further leverage AI in health, Belgium is advised to make policy-level decisions about how to fund, design and undertake actions focussing on data access and inclusion, IT-infrastructure, legal and ethical frameworks, public and professional trust, in addition to education and interpretation. EU initiatives, such as European Health data space (EHDS), the Genomics Data Infrastructure (GDI) and the EU Cancer Imaging Infrastructure (EUCAIM) are building EU data infrastructures. To continue these positive developments, Belgium should continue to invest and support existing European data infrastructures. At the national level, a clear vision and strategy need to be developed and infrastructures need to be harmonized at the European level. [ABSTRACT FROM AUTHOR]

Physical mechanisms that contribute to the generation of fracture waves in condensed media under intensive dynamic impacts have not been fully studied. One of the hypotheses is that this process is associated with the blocky structure of a material. As the loading wave passes, the compliant interlayers between blocks are fractured, releasing the energy of self-balanced initial stresses in the blocks, which supports the motion of the fracture wave. We propose a new efficient numerical method for the analysis of the wave nature of the propagation of a system of cracks in thin interlayers of a blocky medium with complex rheological properties. The method is based on a variational formulation of the constitutive relations for the deformation of elastic–plastic materials, as well as the conditions for contact interaction of blocks through interlayers. We have developed a parallel computational algorithm that implements this method for supercomputers with cluster architecture. The results of the numerical simulation of the fracture wave propagation in tempered glass under the action of distributed pulse disturbances are presented. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'. [ABSTRACT FROM AUTHOR]

Background: Cardiac pacemaking remains an unsolved matter from many perspectives. Extensive experimental and computational studies have been performed to describe the sinoatrial physiology across different scales, from the molecular to clinical levels. Nevertheless, the mechanism by which a heartbeat is generated inside the sinoatrial node and propagated to the working myocardium is not fully understood at present. This work aims to provide quantitative information about this fascinating phenomenon, especially regarding the contributions of cellular heterogeneity and fibroblasts to sinoatrial node automaticity and atrial driving. Methods: We developed a bidimensional computational model of the human right atrial tissue, including the sinoatrial node. State-of-the-art knowledge of the anatomical and physiological aspects was adopted during the design of the baseline tissue model. The novelty of this study is the consideration of cellular heterogeneity and fibroblasts inside the sinoatrial node for investigating the manner by which they tune the robustness of stimulus formation and conduction under different conditions (baseline, ionic current blocks, autonomic modulation, and external high-frequency pacing). Results: The simulations show that both heterogeneity and fibroblasts significantly increase the safety factor for conduction by more than 10% in almost all the conditions tested and shorten the sinus node recovery time after overdrive suppression by up to 60%. In the human model, especially under challenging conditions, the fibroblasts help the heterogeneous myocytes to synchronise their rate (e.g. -82% in sCL under 25 nM of acetylcholine administration) and capture the atrium (with 25% L-type calcium current block). However, the anatomical and gap junctional coupling aspects remain the most important model parameters that allow effective atrial excitations. Conclusion: Despite the limitations to the proposed model, this work suggests a quantitative explanation to the astonishing overall heterogeneity shown by the sinoatrial node. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are valuable tools for discovering biologically relevant information within gene expression data. However, analysing large datasets presents challenges due to the identification of nonlinear gene–gene associations and the need to process an ever-growing number of gene pairs and their potential network connections. These challenges mean that some experiments are discarded because the techniques do not support these intense workloads. This paper presents pyEnGNet, a Python library that can generate gene co-expression networks in High-performance computing environments. To do this, pyEnGNet harnesses CPU and multi-GPU parallel computing resources, efficiently handling large datasets. These implementations have optimised memory management and processing, delivering timely results. We have used synthetic datasets to prove the runtime and intensive workload improvements. In addition, pyEnGNet was used in a real-life study of patients after allogeneic stem cell transplantation with invasive aspergillosis and was able to detect biological perspectives in the study. [ABSTRACT FROM AUTHOR]

Presented and discussed here is the implementation of a software solution that provides prompt X‐ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high‐performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X‐ray diffraction techniques, e.g. large‐volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high‐resolution diffraction etc. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) plays a crucial role in scientific computing, and the efficient utilization of HPC to accomplish computational tasks remains a focal point of research. This study addresses the issue of parameter tuning for Earth system models by proposing a comprehensive solution based on the concept of scientific workflows. This solution encompasses detailed methods from sensitivity analysis to parameter tuning and incorporates various approaches to enhance result accuracy. We validated the reliability of our methods using five cases in the Single Column Atmosphere Model (SCAM). Specifically, we investigated the influence of fluctuations of 11 typical parameters on 10 output variables. The experimental results show that the magnitude of the impact on the results varies significantly when different parameters are perturbed. These findings will help researchers develop more reasonable parameterization schemes for different regions and seasons. [ABSTRACT FROM AUTHOR]

The demand for wireless connectivity has grown tremendously over the last few decades. A new perspective of wireless communication will soon be positioned worldwide. In the years beyond, the millimeter-wave spectrum for the mobile communication infrastructure has improved spectral efficiency. The problem of the technical challenges may be detected, identified, and resolved in different designs as traditional solutions involve the software tricks that prevent the modes of operation that trigger the trouble. In this research, we carry out the proposed Millimetre assisted wave technologies using 6G federated learning and high-performance computing for 6G assisted wireless communication systems for voice data optimization. Millimeter-wave technology is a specific part of the radio frequency spectrum, between 24 and 100 GHz. The outcome of this proposed technique will be voice data optimization in the 6G cellular networks for next-generation networks to improve the 6G assisted networks federated learning from existing cellular network technology generation, established on the tool of Flight stack or Autopilot. The observational results express to assess the performance of the device, we acquire theMillimetre assisted wave technologies and federated learning and high-performance computing at various data transmission rates and modulations, including real-time data transfer up to 96 Gbit/s at 420 GHz, with a low bit error rate and good signal-to-noise ratio. With 18 dBm emitted power at 0.4 THz, this method creates and transmits a signal with a 160 Gbits per second net rate across a 15 m distance. This monolithic dual-DFB PIC-based THz generating technique represents an important advancement toward completely integrated, economically viable, and energy-efficient THz transmitters. [ABSTRACT FROM AUTHOR]

Large simulations with many independent sub-simulations are common in scientific computing. There are numerous challenges, however, associated with performing such simulations in shared computing environments. For example, sub-simulations may have wildly varying completion times or not complete at all, leading to unpredictable runtimes as well as unbalanced and inefficient use of human and computational resources. In this study, we use the actor model of concurrent computation to improve both the resource utilization and fault tolerance for large-scale scientific computing simulations. More specifically, we use actors in the SUMMA model to manage a large-scale hydrological simulation over the North American continent with over 500,000 independent sub-simulations. We find that the actors implementation outperforms a standard array job submission as well as the job submission tool GNU Parallel by better balancing the computational load across processors. The actors implementation also improves fault tolerance and can eliminate the user intervention required to detect and re-submit failed jobs. [ABSTRACT FROM AUTHOR]

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/. Scientific contribution This work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application. [ABSTRACT FROM AUTHOR]

Summary: This article presents the development of a new wind turbine simulation software to study wake flow physics. To this end, the design and development of waLBerla‐wind, a new simulator based on the lattice‐Boltzmann method that is known for its excellent performance and scaling properties, will be presented. Here it will be used for large eddy simulations (LES) coupled with actuator wind turbine models. Due to its modular software design, waLBerla‐wind is flexible and extensible with regard to turbine configurations. Additionally it is performance portable across different hardware architectures, another critical design goal. The new solver is validated by presenting force distributions and velocity profiles and comparing them with experimental data and a vortex solver. Furthermore, waLBerla‐wind's performance is compared to a theoretical peak performance, and analyzed with weak and strong scaling benchmarks on CPU and GPU systems. This analysis demonstrates the suitability for large‐scale applications and future cost‐effective full wind farm simulations. [ABSTRACT FROM AUTHOR]

Summary: The breadth‐first search procedure is an algorithm that traverses the vertices of a graph, determining the distance from each vertex to the initial vertex. The distance is infinite for a non‐reachable vertex from the starting vertex. Despite having an efficient serial version, this important algorithm is irregular, making its effective parallel implementation a daunting task. This paper shows the results of an OpenMP‐based implementation of the breadth‐first search procedure using the bag data structure. Furthermore, the code relied on the C++ programming language. This paper reimplements an existing proposal coded using the Cilk++ programming language. The experiments relied on 32 strongly connected graphs and 31 disconnected graphs in executions performed on two machines. The first machine contained 28 cores and two threads per core. The second machine comprised 48 processing cores, with hyperthreading disabled. Regarding the serial version, the parallel implementation yielded a speedup of up to 20× when using 28 processing cores and up to 25× when using 56 threads in tests performed on a machine with the first generation of Intel® Xeon® Scalable processors. Furthermore, the new parallel implementation yielded speedups of up to 45× when using 48 cores in experiments performed on a machine with the second generation of Intel® Xeon® Scalable processors. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) bioprinting belongs to the wide family of additive manufacturing techniques and employs cell-laden biomaterials. In particular, these materials, named "bioink", are based on cytocompatible hydrogel compositions. To be printable, a bioink must have certain characteristics before, during, and after the printing process. These characteristics include achievable structural resolution, shape fidelity, and cell survival. In previous centuries, scientists have created mathematical models to understand how physical systems function. Only recently, with the quick progress of computational capabilities, high-fidelity and high-efficiency "computational simulation" tools have been developed based on such models and used as a proxy for real-world learning. Computational science, or "in silico" experimentation, is the term for this novel strategy that supplements pure theory and experiment. Moreover, a certain level of complexity characterizes the architecture of contemporary powerful computational resources, known as high-performance computing (HPC) resources, also due to the great heterogeneity of its structure. Lately, scientists and engineers have begun to develop and use computational models more extensively to also better understand the bioprinting process, rather than solely relying on experimental research, due to the large number of possible combinations of geometrical parameters and material properties, as well as the abundance of available bioprinting methods. This requires a new effort in designing and implementing computational tools capable of efficiently and effectively exploiting the potential of new HPC computing systems available in the Exascale Era. The final goal of this work is to offer an overview of the models, methods, and techniques that can be used for "in silico" experimentation of the physicochemical processes underlying the process of 3D bioprinting of cell-laden materials thanks to the use of up-to-date HPC resources. [ABSTRACT FROM AUTHOR]

Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatiotemporal modeling, where high-dimensional latent parameter spaces are ubiquitous. The methodology of integrated nested Laplace approximations (INLA) provides a framework for performing Bayesian inference applicable to a large subclass of additive Bayesian hierarchical models. In combination with the stochastic partial differential equation (SPDE) approach, it gives rise to an efficient method for spatiotemporal modeling. In this work, we build on the INLA-SPDE approach by putting forward a performant distributed memory variant, INLADIST, for large-scale applications. To perform the arising computational kernel operations, consisting of Cholesky factorizations, solving linear systems, and selected matrix inversions, we present two numerical solver options: a sparse CPU-based library and a novel blocked GPU-accelerated approach which we propose. We leverage the recurring nonzero block structure in the arising precision (inverse covariance) matrices, which allows us to employ dense subroutines within a sparse setting. Both versions of INLADIST are highly scalable, capable of performing inference on models with millions of latent parameters. We demonstrate their accuracy and performance on synthetic as well as real-world climate dataset applications. [ABSTRACT FROM AUTHOR]

The development vector of modern central processing units, which increasingly involves using a more significant number of cores and prioritizing parallelism over the high power of a single computational unit, presents new challenges for the existing software design. This work investigates and addresses the problem of access to shared memory in multithreaded environments, such as operating systems, interactive distributed computing systems, and high-performance simulation systems. Thus, the object of study is a non-blocking approach to organizing access to memory and performing basic operations with it through non-blocking synchronization. The research methods include developing an approach to organizing access to shared memory using the doubleword compare-and-swap algorithm, followed by a theoretical and practical comparison of the resulting outcome with the standard blocking access algorithm to shared memory for different configurations of the number of threads and the number of simultaneous memory access attempts. Additionally, testing was conducted within the framework of an unnamed closed-source project by integrating the solution into it, followed by A/B testing. The results showed that using non-blocking approaches is advisable, especially in comparison with locking approaches, which demonstrated a performance degradation relative to the standard allocation algorithm by more than 300 %, while non-blocking approaches provided an improvement of 40–90 %. It was also found that using hybrid approaches to the organization of shared memory systems at the software level can lead to more stable results and mitigate application performance degradation compared to classical approaches such as buddy algorithms or free lists. Despite the results obtained, the author remains cautious about the idea of memory management and pool organization at the software level and does not recommend using specialized allocation algorithms without an urgent need to speed up memory allocation itself. The purpose of these structures is still not to improve software performance directly but to enhance and speed up access to the data stored in them. [ABSTRACT FROM AUTHOR]

The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of ∼ 10 8 stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture. [ABSTRACT FROM AUTHOR]

In the era of heterogeneous computing, a new paradigm called accelerator level parallelism (ALP) has emerged. In ALP, accelerators are used concurrently to provide unprecedented levels of performance and energy efficiency. To reach that there are many problems to be solved, one of the most challenging being co-execution. In this paper, we present a new scheduling framework called POAS, a general method for providing co-execution to applications. Our proposal consists of four steps: predict, optimize, adapt and schedule. With POAS, an unseen application can be executed concurrently in ALP with little effort. We evaluate POAS on a heterogeneous environment consisting of CPUs, GPUs (CUDA cores), and XPUs (Tensor cores) on two different fields, namely linear algebra (matrix multiplication benchmark) and deep learning (convolution benchmark). Our experiments prove that POAS provides excellent performance and completes the tasks within a time very close to the optimal time for the hardware and applications used, with a negligible execution time overhead. Moreover, the POAS predictor performed exceptionally well, achieving very low RMSE values for both use cases. Therefore, POAS can be a valuable tool for fully exploiting ALP and improving overall performance over offloading in heterogeneous settings. [ABSTRACT FROM AUTHOR]

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework's performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers. [ABSTRACT FROM AUTHOR]

This paper introduces a novel formulation of the clustering problem, namely, the minimum sum-of-squares clustering of infinitely tall data (MSSC-ITD), and presents HPClust, an innovative set of hybrid parallel approaches for its effective solution. By utilizing modern high-performance computing techniques, HPClust enhances key clustering metrics: effectiveness, computational efficiency, and scalability. In contrast to vanilla data parallelism, which only accelerates processing time through the MapReduce framework, our approach unlocks superior performance by leveraging the multi-strategy competitive–cooperative parallelism and intricate properties of the objective function landscape. Unlike other available algorithms that struggle to scale, our algorithm is inherently parallel in nature, improving solution quality through increased scalability and parallelism and outperforming even advanced algorithms designed for small- and medium-sized datasets. Our evaluation of HPClust, featuring four parallel strategies, demonstrates its superiority over traditional and cutting-edge methods by offering better performance in the key metrics. These results also show that parallel processing not only enhances the clustering efficiency, but the accuracy as well. Additionally, we explore the balance between computational efficiency and clustering quality, providing insights into optimal parallel strategies based on dataset specifics and resource availability. This research advances our understanding of parallelism in clustering algorithms, demonstrating that a judicious hybridization of advanced parallel approaches yields optimal results for MSSC-ITD. Experiments on the synthetic data further confirm HPClust's exceptional scalability and robustness to noise. [ABSTRACT FROM AUTHOR]

Laser Powder Bed Fusion (L-PBF) has immense potential for the production of complex, lightweight, and high-performance components. The traditional optimization of process parameters is costly and time-intensive, due to reliance on experimental approaches. Current numerical analyses often model single-line scans, while it is necessary to model multiple fully scanned layers to optimize for bulk material quality. Here, we introduce a novel approach utilizing discrete element simulations with a ray tracing-modeled laser heat source. Our approach significantly reduces the cost and time consumption compared to conventional optimization methods. GPU acceleration enables efficient simulation of multiple layers, resulting in parameters optimized for bulk material. In a case study, parameters were optimized for AlSi10Mg in just 5 days, a process that would have taken over 8 months without GPU acceleration. Experimental validation affirms the quality of the optimized process parameters, achieving an optical density of 99.91%. Optimization using the accelerated simulation yielded an optimized parameter set within 5 days. This resulted in a part with an optical density of 99.91%. [ABSTRACT FROM AUTHOR]

We present the algorithm of exoplanets transit search in MASTER global robotic telescope network database, that has been created with the involvement and use of artificial intelligence methods. MASTER's 2003–2023 years archive images are stored at data storage and analysis center in Lomonosov supercomputer. For exoplanets transit search we used the images of SWIFT gamma-ray bursts alerts (GRB), received by MASTER telescopes during several hours at target nights. The algorithm includes cross-correlation of GRB error-boxes coordinates and TESS exoplanetary transits candidates inside 4 square degrees of each field. Our analyses is based on wide-field images obtained on MASTER-Amur, -Tunka, -Kislovodsk, -Tavrida, MASTER-IAC, MASTER-OAGH, MASTER-OAFA and MASTER-SAAO robotic telescopes. We used data storage and analysis center in Lomonosov supercomputer with MASTER images archive, calculated light curves for target stars and approximate them to find eclipse by exoplanets. The centralized storage of this data allows us to simplify and significantly speed up access to the huge archive of images formed during the entire operation of the robotic network for automatic processing and facilitate the manual search for the necessary information by the researcher. From this storage we compared the program with unique long homogeneous series of Swift GRBs alert and inspection observations, which we analyzed. The main result is reduction of unique light curves of exoplanetary transits of TIC 127115861.01 TESS candidate, obtained by MASTER long before the TESS space observatory. [ABSTRACT FROM AUTHOR]

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. [ABSTRACT FROM AUTHOR]

In the last decades, technological advances have led to a considerable increase in computing power constraints to simulate complex phenomena in various application fields, among which are climate, physics, genomics and medical diagnosis. Often, accurate results in real time, or quasi real time, are needed, especially if related to a process requiring rapid interventions. To deal with such demands, more sophisticated approaches have been designed, including GPUs, multicore processors and hardware accelerators. Supercomputers manage high amounts of data at a very high speed; however, despite their considerable performance, their limitations are due to maintenance costs, rapid obsolescence and notable energy consumption. New processing architectures and GPUs in the medical field can provide diagnostic and therapeutic support whenever the patient is subject to risk. In this context, image processing as an aid to diagnosis, in particular pulmonary ultrasound to detect COVID-19, represents a promising diagnostic tool with the ability to discriminate between different degrees of disease. This technique has several advantages, such as no radiation exposure, low costs, the availability of follow-up tests and the ease of use even with limited resources. This work aims to identify the best approach to optimize and parallelize the selection of the most significant frames of a video which is given as the input to the classification network that will differentiate between healthy and COVID patients. Three approaches have been evaluated: histogram, entropy and ResNet-50, followed by a K-means clustering. Results highlight the third approach as the most accurate, simultaneously showing GPUs significantly lowering all processing times. [ABSTRACT FROM AUTHOR]