bluuebunny/crossref_metadata_2025 huggingface dataset:
Key Findings to Support the Development of a Framework for the Implementation of Product Lifecycle Management in Engineer to Order Products
Authors: McKendry D.A.  , Whitfield R.I.  , Duffy A.H.B.
Published: February 2025

BAE Systems Naval Ships has undertaken a comprehensive overhaul of all aspects of its approach to the engineering, design and manufacture of complex warships. Through a partnership with the University of Strathclyde, research has been completed on the implementation of Product Lifecycle Management (PLM) to meet organisational objectives in the development of Engineer to Order (ETO) products. Engineer to Order (ETO) products include characteristics such as high capital value, large-scale, long-life, no prototype, highly customised and few or one off. An overview of PLM is provided highlighting the challenges specific to the characteristics of ETO products. The paper then provides a summary of the key findings of approximately 30 semi-structured interviews with leading industry personnel, in the UK and internationally, on PLM implementation in ETO products. These participants are engaged with PLM in ETO products either as an implementer or as a key stakeholder with an interest in its successful use within their organisation. The findings have been used to support the development of a framework to ensure that PLM implementation supports the business objectives of ETO product development.
A Review of Technical Challenges of Laser Drilling Manufacturing Process
Authors: Sarfraz Shoaib  , Shehab Essam  , Salonitis Konstantinos
Published: February 2025

Laser drilling is a widely-used manufacturing process especially in high value manufacturing product components such as jet engine and gas turbine. Manufacturing process efficiency and quality as well as operation cost are very important factors for companies to survive in the competitive environment. To create good quality product with minimum cost, optimum process parameters selection is necessary as well as comprehending the process. In this paper, technical challenges of the laser drilling manufacturing processes integrated with the product quality and process parameters has been discussed. This study also reveals some future research direction in the area of laser drilling.
Knowledge Management Yesterday and Tomorrow: Exploring an ‘Intellectual Paradox’
Authors: Maindze Alexslis  , Jennions Ian  , Skaf Zakwan
Published: February 2025

Knowledge management continues to be characterized by strong contextual application with diversity of techniques, tools and applications which practitioners far and wide seem to agree and adopt. However, when it comes to its philosophical distinctness, it is yet to achieve something as seemingly easy as a common definition. There is significant agreement on fluidity and methods of application but limited consensus on philosophical interpretation. Furthermore, that we know what it is, acknowledge its impact, functional relevance and yet cannot articulate a common methodology points to what this paper terms an ‘intellectual paradox’.
Integrating Financial Metrics with Production Simulation Models
Authors: Acheson Clive  , Mackle David  , Murphy Adrian  , Higgins Peter  , Collins Rory  , Higgins Colm  , Butterfield Joe  , Darlington John  , Tame Roger
Published: February 2025

A production system is traditionally considered as a combination of the materials supply, production planning, scheduling, control and material transformation functions. Many production control strategies have been proposed and used to manage production system operations. Such control strategies aim to regulate key metrics such as Work In Progress (WIP), cycle time or throughput, as well as their inter-relationships. Moreover simulation technology is available for developing and optimizing production control. Methods such as Discrete Event Simulation (DES) enable complex process chains to be examined to understand bottlenecks, excess inventory, overproduction, etc. By representing a production system inside a simulation model it is possible to examine in virtual scenarios how a real system might work under predefined conditions. The influence of key characteristics, such as WIP or throughput can be readily examined during such virtual scenarios. A key weakness of the current state-of-the-art in this area is the lack of non-engineering metrics typically modelled. For decision makers the critical metrics are often not solely engineering but also financial, for example, cash-flow, profitability or Return on Investment. However automatically generating financial metrics from simulation output production metrics is a non-trivial task. Moreover the nature of financial and production metrics are typically dissimilar in fidelity and interval and careful attention is required to standardise data for robust decision making. The documented research demonstrates an integrated simulation methodology which represents the calculation of both the critical engineering and financial metrics appropriate for volume production systems. The P&Q production problem and the DES software QUEST are used. In particular the paper investigates how a Design of Experiments approach can be used to systematically create simulation combinations to understand and quantify the critical interactions between engineering and financial metrics.
A Chemical Substance Reporting System for Manufacturing Companies
Authors: Takhar Sukhraj  , Liyange Kapila
Published: February 2025

While striving to mitigate the risk to human health and the environment, chemical substance regulations continue to impose greater legislative burdens on industry, which ultimately creates business continuity risk. Compliance to these regulations requires greater investment which ultimately undermines profits. Furthermore, as regulations vary between countries or politico-economic unions, impact on manufacturers is dependent on which areas of the world that its supply chain is most prevalent. A chemical substance reporting system for manufacturing companies requires information on parts and manufacturing processes that are both defined in-house and within the external supply chain. Without information on chemical substance uses within the downstream supply chain, manufacturers cannot fulfil their legislative obligations or effectively manage business continuity risk. Often the biggest hurdle to collecting this information is supply chain engagement, which is made more difficult with multiple, different industry standards and data exchange formats. As more and more chemical substances become heavily regulated, manufacturers require increased volumes of downstream supply chain information on a routine basis. The aim of this paper is to identify existing good practices which could be utilized to implement chemical substance reporting systems for manufacturing companies.
Effect of Plate Thickness on Shear Fracture Surface Features of Electrical Steel Sheet
Authors: Zhu Yiwei  , Lu Jiabin  , Yan Qiusheng  , Zeng Jun  , Ouyang Bing  , Kuang Zhihui
Published: February 2025

The shear test of 0.20–0.65 mm electrical steel sheet was carried out at the same relative lateral clearance and shear rate, The morphology of the specimen after shearing was detected by SEM and white light interferometer (2D/3D). The results show: Under the same relative backlash, the thinner thickness of the plate, the greater relative height of rollover, the narrower relative height of shear zone, the wider relative height of fracture zone, and the greater fracture angle, the more rough the fracture surface, the more obvious the dimples feature.
Multi-Objective Optimization for Models Sequencing in Mixed-Model Assembly Lines
Authors: Rauf Mudassar  , Sarfraz Shoaib  , Shehab Essam  , Javeed Saima  , Jahanzaib Mirza
Published: February 2025

Since the introduction of assembly lines, they are being used for the mass production. Due to the concept of mass customization, Mixed-Model Assembly Lines (MMAL) are becoming more popular. Various Industries are forced to shift to MMAL because of customer demand of more customized products. Assembly line balancing and model sequencing are the two major problems in MMAL. This paper deals with the Model sequencing problem in MMAL. A modified Multi-Objective NEH (MO-NEH) algorithm was proposed to optimize the conflicting objectives (flow time and make-span). For this purpose, NEH was integrated with PROMTHEE-II, a Multi-Criteria Decision-Making technique (MCDM). A mathematical model was presented for three Objectives; make-span, Idle time and flow time. A case study of hydraulic pumps MMAL was conducted. Results of proposed MO-NEH was compared with existing NEH variants and it showed that proposed algorithm outperformed the existing variants in optimizing the objectives simultaneously.
Ad Hoc IoT Approach for Monitoring Parking Control Process
Authors: Ferreira João C.  , Martins Ana Lúcia
Published: February 2025

The purpose of this research is to develop a collaborative approach to control the parking in a city using IoT (Internet of Things). This approach is based on Bluetooth Low Energy (BLE) beacons to control the parking process without having to investment in sensors. Parking violations can be easily detected through the proposed collaborative process among user's mobile devices. A reward mechanism incentives users' participation. This approach uses an ad hoc network of users who send information to a central system regarding georeferenced beacon information. Comparing with previous payments associated with a vehicle, the approach can identify parking violations, e.g. parking without associated payment.
CAKNA: A Personalized Robot-Based Platform for Anxiety States Therapy
Authors: Aziz Azizi Ab  , Fahad Ali Saad  , Ahmad Faudziah
Published: February 2025

Anxiety can be defined as an unpleasant state of mental uneasiness or concern that causes physical and psychological discomfort. In this paper, we describe an interactive robot-based platform that is designed to be used to support of person with anxiety traits and states using non-medical treatments including mindfulness, relaxation, and muscle relaxation therapy. We describe CAKNA, whose design is based on theories in anxiety traits and states, social robots, and therapies, along with the results of a pilot study (with 24 respondents) exploring the effectiveness of our robot to support individuals with anxiety.
Application of Bluetooth Low Energy Positioning in Long-Term Care Systems
Authors: Lin Jeng-Wei  , Lin Huan-Ting  , Tang Pei-Hua
Published: February 2025

Population aging is a serious problem nowadays in many countries. This creates a huge demand of long-term health care. In many long-term care institutes, a caregiver has to take care of many elderly people at the same time. Some of them have been paralyzed in bed, while others are still able to move in the institutes. It is not uncommon that a caregiver has to spend a lot of time to find an elder to perform daily care operations, such as injections, medications, and blood pressure measuring. Elders may move in dangerous places, such as kitchens, stairs, and bathrooms. In this paper, we present an iBeacon-based indoor positioning system for long-term care institutes. Thus, the staffs of long-term care institutes can easily locate the elders, and be notified when some elders move in dangerous places.
Toward More Reliable Intelligent Environments
Authors: Cho Eun-Sun
Published: February 2025

Thanks to the significant achievement in IoT, artificial intelligence, and other computer technologies, we envision big changes in our everyday lives in the near future. However, before we accept such a new world, we should ensure that having intelligence around us is not harmful to human lives. Here, we focus on dark sides of technological achievement that would harm to our future, and share some technical insights on safety supports for reliable intelligent environments.
An Ontology-Based Approach for an Efficient Selection and Classification of Soils
Authors: Muñoz Andrés  , Soriano-Disla José M.  , Janik Leslie J.
Published: February 2025

Soil libraries represent an invaluable resource in terms of research, management or planning. However, the access to such libraries and selection of soils is often tedious and time consuming thus limiting their usefulness. In this study we propose an ontology-based approach for an efficient and intuitive selection and classification of soils. For this test, a soil library of 458 soils from Australia was used. An ontology was then developed to model the fundamentals concepts and relationships found in the data. The basic capabilities of the ontology are shown to select samples with certain values for a number of attributes. In addition, an inference process known as realization is tested to automatically assign individuals to concepts, in our present case to a soil texture class or soil order (the latter known as soil classification). Results show the potential of ontology approaches to select samples from large libraries in an efficient and intuitive way. In addition, and through the use of reasoning processes, we were able to classify soils from different orders and textural classes with accuracy higher than 80% in most cases. This represents an additional application of ontology approaches to produce hidden data from the original data set.
Influence of Multivariate Modeling in the Prediction of Soil Carbon by a Portable Infrared Sensor
Authors: Martínez-España Raquel  , Bueno-Crespo Andrés  , Soto Jesus  , Janik Leslie J.  , Soriano-Disla José M.
Published: February 2025

The determination of carbon is one of the most important in soil analysis. However traditional techniques are costly and time consuming. In this manuscript we propose an alternative predictive approach based on portable mid-infrared spectroscopy data modeled by machine learning techniques. We evaluate the performance of different machine learning models and sample size to predict soil carbon in 457 Australian soils. The results show a good performance of the models. All models are validate by statistical tests. The best performing technique with a 99% of confidence level is the Gaussian Process providing a 98% of accuracy for the prediction of soil carbon. Moreover, this technique is the most robust for the different sample sizes tested. When compared with the commonly used Partial Least Squares Regression technique, the machine learning approaches provide more successful and balanced results.
Responsible Science
Authors: Hay Alastair
Published: February 2025

Responsibility in science is not a new requirement. The very practice of science requires a good deal of responsibility to ensure the integrity of research findings and transmission of knowledge. But there are issues where scientific practice may come into conflict with either individual conscience or a more collective view. These conflicts are considered against a backdrop of increasing discussion in both academic and industrial circles about what it means to practice responsible science.
Defect-Based Testing
Authors: Pretschner Alexander
Published: February 2025

A good test case reveals potential defects with good cost-effectiveness. We provide a generic model of faults and failures, formalize it, and present methodological instantiations for test case generation.
Temporal Logic as Filtering
Authors: Rodionova Alena  , Bartocci Ezio  , Nickovic Dejan  , Grosu Radu
Published: February 2025

We show that metric temporal logic (MTL) can be viewed as linear time-invariant filtering, by interpreting addition, multiplication, and their neutral elements, over the idempotent dioid (max,min,0,1). Moreover, by interpreting these operators over the field of reals (+,×,0,1), one can associate various quantitative semantics to a metric-temporal-logic formula, depending on the filter's kernel used: square, rounded-square, Gaussian, low-pass, band-pass, or high-pass. This remarkable connection between filtering and metric temporal logic allows us to freely navigate between the two, and to regard signal-feature detection as logical inference. To the best of our knowledge, this connection has not been established before. We prove that our qualitative, filtering semantics is identical to the classical MTL semantics. We also provide a quantitative semantics for MTL, which measures the normalized, maximum number of times a formula is satisfied within its associated kernel, by a given signal. We show that this semantics is sound, in the sense that, if its measure is 0, then the formula is not satisfied, and it is satisfied otherwise. We have implemented both of our semantics in Matlab, and illustrate their properties on various formulas and signals, by plotting their computed measures.
Reactive Synthesis: Towards Output-Sensitive Algorithms
Authors: Finkbeiner Bernd  , Klein Felix
Published: February 2025

Reactive synthesis is a technology for the automatic construction of reactive systems from logical specifications. In these lecture notes, we study different algorithms for the reactive synthesis problem of linear-time temporal logic (LTL). The classic game-based synthesis algorithm is input-sensitive in the sense that its performance is asymptotically optimal in the size of the specification, but it produces implementations that may be larger than necessary. We contrast this algorithm with output-sensitive algorithms for reactive synthesis, i.e., algorithms that are optimized towards the size or structural complexity of the synthesized system. We study the bounded synthesis algorithm, which produces an implementation with a minimal number of states, and the bounded cycle synthesis algorithm, which additionally guarantees that the number of cycles of the implementation is minimal.
Good Practice in Change Management: Deploying Telemedicine Services at Large-Scale
Authors: Whitehouse Diane  , Lange Marc
Published: February 2025

Scaling-up a telemedicine service involves careful preparation and planning for managing change. Telemedicine “doers” worked together actively in Momentum, a co-financed European Commission project, on how to manage changes to telemedicine services. In 2015, they came up with 18 critical success factors that are best used to deploy a telemedicine service, looking specifically at the importance of change management. Translated into a workshop and accompanying survey, now called the Momentum process, the European Health Telematics Association (EHTEL) offers training in these success factors. The factors have already been used by several sites in Europe to move telemedicine services forward in a practical way. This workshop, can help telemedicine practioners, in Europe and globally, make progress on the expansion of this useful technology.
Parallel Computing: A Paradigm to Unimaginable Computing Speed and Efficiency
Authors: Jafar Sana  , Rajnish Ranjana  , Kumar Pankaj
Published: February 2025

Parallel computing finds its place in the world of computation long back since 1842 with the first ever design of Analytical Engine by English mathematician and computer pioneer Charles Babbage. It took lots of brainstorming by the great researchers like John Cocke, Daniel Slotnick, Gene Amdahl and the big organizations like IBM, Burroughs Corporation and Honeywell for the next century, to coin various laws and set a background for today's parallel computing platform. Since then, parallel computing has successfully replaced the notion of sequential computing in terms of instruction level, task level, thread level parallelism. From the use of SISD programs to MIMD programs, the evolution of parallelism in computing has been phenomenon. When a job is divided into several parts where each part is capable of running with others in parallel, then this not only increases the overall speed of execution of job, but also ascertains the optimal utilization of computer resources like memory, processor time and the like. On this basis, the computers can be categorized on hardware level at which they can support parallelism which include multi core/ many core computers, multi processor computers, clusters, grids and massively parallel supercomputers. With the concept of parallelization concepts like concurrency control, deadlocks, synchronization of tasks have been there. Lot of work is going on since years in addressing these concepts in virtue of parallel computing. This chapter is focusing on the history, background, types of parallel computing, the memory architectures, message passing, concurrency control, deadlocks and their possible solution in parallel computing.
Spark SQL with Hive Context or SQL Context
Authors: Sunami Karuna Yogesh  , Sunami Yogesh
Published: February 2025

The recent advances in Big data made attempts to analyze huge dumps of readily available transactional data to predict patterns and trends. Hadoop framework was developed based on MapReduce to exploit parallelism to the fullest. And, indeed it has enabled the computing mechanisms to be more robust, flexible, scalable and efficient. At the same time, this has unearthed many new limitations of existing databases and computational algorithms such as processing speed versus waiting times and parallelizability of a query. In this chapter, we will focus on understanding the need, features and applications of Spark SQL. It will also include Spark SQL code snippets to enhance the coding abilities of the readers.
Real Time Location Tracking Map Matching Approaches for Road Navigation Applications
Authors: Sharma Kanta Prasad  , Poonia Ramesh C.  , Sunda Surendra
Published: February 2025

Prediction of accurate vehicle location is essential for various (Intelligent Transport System) ITS applications. Existing vehicle location systems are work on different position sensor and smart computing devices to execute map matching algorithm. There exist necessary needs for enhancing the performance of receivers in terms of position towards the error characteristics of (Global Position System) GPS signals. Simple map matching algorithm faces difficulties in testing on road trial. The proposed algorithm and approach overcome many of the drawbacks of existing methods and is capable of achieving higher accuracy, precise navigation in critical conditions for high carrier frequency signaling on road network.
Parallel Computing Contrive Optimized NFB Through QEEG & LENS Approach
Authors: Zafar Sherin
Published: February 2025

Neuro-feedback (NFB) has been considered as the cognitive science to retrain brainwave patterns. The occurrence of various brain waves take place at different frequencies, some fast, some quite slow called as delta (0.5–3.5 Hz very slow high amplitude experienced in deep restorative sleep), theta (4–8 Hz represents daydream correlated with mental inefficiency a twilight zone between waking and sleep), alpha (8–12 Hz, slower and larger correlated with a state of relaxation), beta (13–30 Hz, fast brainwave correlated with intellectual activity) and gamma (above 30 Hz, represents very fast EEG activity), specified as classic EEG bands. The proposed work focuses upon utilizing parallel computing based neurofeedback (PCBNFB) as rehabilitation means that will directly retrain the electrical activity (waves) in human brain. The concept of brain ability for making sense of doing different stimuli at the same time (simultaneously) is referred as Parallel Computing so the proposed research study will be directed upon utilizing parallel computing based neuro-feedback (PCBNFB) as a major therapeutic role in difficult areas like ADD (Adult Attention Deficit)/ADHD (Attention Deficit Hyperactivity Disorder), anxiety, obsessive compulsive disorder, learning disabilities, head injuries, Obsessive-Compulsive Disorder (OCD), reduces pain, quality of life for cancer patients suffering from chemotherapy etc. In a developing country like ours citing an ADD/ADHD as a reason for underperformance, underachievement is not taken seriously as its still unheard and unaware off and ADD/ADHD, compulsive and obsessive disorder patients spend their whole life considered as lazy, unorganized and duffers. The PCBNF unit will provide for each modality a real-time processing pipeline that handles signal acquisition and all the necessary methods/algorithms required for NFB calculation through effective and easier QEEG & LENS approach. Both QEEG and LENS have their advantages and limitations so the proposed research study explores in parallel fashion both QEES & LENS for better analysis of various neural disorders. Furthermore, it should provide the flexibility of using multimodal or uni-modal NFB.
Blockchain Innovation and Its Impact on Business Banking Operations
Authors: Li Larry  , Sy Malick  , Mcmurray Adela
Published: February 2025

Blockchain has become one of the hottest technologies in the market, and has immense potential to reshape current business operations in multiple industries, particularly the banking sector. Its impact comes through the facilitation of fund transaction, securities trading, data analytics, and risk management. This study identifies the key characteristics of blockchain that cultivates competitive advantages applied in the banking sector. We discuss how blockchain's capabilities cultivate a new form of data monitoring, analysis and storage. We identify the nine key challenges to be mindful of, before considering a strategic shift onto an entire infrastructure based on such new blockchain technology.
Big Iron, Big Data, and Big Identity
Authors: Lee Craig A.  , Assis Marcio  , Bittencourt Luiz F.  , Nativi Stefano  , Tolosana-Calasanz Rafael
Published: February 2025

While High-Performance Computing (HPC) typically focuses on very large, parallel machines, i.e., Big Iron, running massive numerical codes, the importance of extracting knowledge from massive amounts of information, i.e., Big Data, has been clearly recognized. While many massive data sets can be produced within a single administrative domain, many more massive data sets can be, and must be, assembled from multiple sources. Aggregating data from multiple sources can be a tedious task. First, the locations of the desired data must be known. Second, access to the data sets must be allowed. For publicly accessible data, this may not pose a serious problem. However, many application domains and user groups may wish to facilitate, and have some degree of control over, how their resources are discovered and shared. Such collaboration requirements are addressed by federation management technologies. In this paper, we argue that effective, widely-adopted federation management tools, i.e., Big Identity, are critical for enabling many Big Data applications, and will be central to how the Internet of Things is managed. To this end, we re-visit the NIST cloud deployment models to extract and identify the fundamental aspects of federation management: crossing trust boundaries, trust topologies, and deployment topologies. We then review possible barriers to adoption and relevant, existing tooling and standards to facilitate the emergence of a common practice for Big Identity.
Adaptive Data Placement in Multi-Tiered Data Staging Runtime
Authors: Jin Tong  , Sun Qian  , Romanus Melissa  , Parashar Manish
Published: February 2025

As applications approach extreme scales, data staging and in-situ/in-transit processing have been proposed to address the data challenges and improve scientific discovery. However, further research is necessary in order to understand how growing data sizes from data intensive simulations coupled with the limited DRAM capacity in High End Computing systems will impact the effectiveness of this approach. Moreover, the complex and dynamic data exchange patterns exhibited by the workflows coupled with the varied data access behaviors make efficient data placement within the staging area challenging. In this paper, we explore how we can use deep memory levels for data staging and develop a multi-tiered data staging method that spans both DRAM and solid state disks (SSD). This approach allows us to support both code coupling and data management for data-intensive simulation workflows. We also show how adaptive application-aware data placement mechanisms can dynamically manage and optimize data placement vertically across the DRAM and SSD storage levels and horizontally across different staging nodes in this multi-tiered data staging method. We present an experimental evaluation of our approach using two OLCF resources: an Infiniband cluster (Sith) and a Cray XK7 system (Titan), and using combustion (S3D) and fusion (XGC1) simulations. The evaluation results demonstrate that our approach can effectively improve data access performance and overall efficiency of coupled scientific workflows.
Parallelizing Big Data Machine Learning Applications with Model Rotation
Authors: Zhang Bingjing  , Peng Bo  , Qiu Judy
Published: February 2025

This paper proposes model rotation as a general approach to parallelize big data machine learning applications. To solve the big model problem in parallelization, we distribute the model parameters to inter-node workers and rotate different model parts in a ring topology. The advantage of model rotation comes from maximizing the effect of parallel model updates for algorithm convergence while minimizing the overhead of communication. We formulate a solution using computation models, programming interfaces, and system implementations as design principles and derive a machine learning framework with three algorithms built on top of it: Latent Dirichlet Allocation using Collapsed Gibbs Sampling, Matrix Factorization using Stochastic Gradient Descent and Cyclic Coordinate Descent. The performance results on an Intel Haswell cluster with max 60 nodes show that our solution achieves faster model convergence speed and higher scalability than previous work by others.
Optimizing Science Applications for the Cori, Knights Landing, System at NERSC
Authors: Deslippe Jack  , Doerfler Doug  , Cook Brandon  , Malas Tareq  , Williams Samuel  , Dosanjh Sudip
Published: February 2025

In this chapter, we present the optimization strategy developed at NERSC for moving user applications from traditional x86 CPU based HPC systems to the many-core Xeon-Phi powered Cori system. We target the Xeon-Phi many-cores, large vector units and high-bandwidth memory. The developed strategy is general, intended to be applicable to NERSC's 6,000 users. We present three application case studies from different science areas to illustrate the Cori optimization process.
Asymptotic Computing – Undoing the Damage
Authors: Sterling Thomas  , Brodowicz Maciej  , Kogler Danny  , Anderson Matthew
Published: February 2025

While the very far future well beyond exaflops computing may encompass such paradigm shifts as quantum computing or neuromorphic computing, a critical window of change exists within the domain of semiconductor digital logic technology but beyond conventional practices of architecture, system software, and programming. As key parameters such as Dennard scaling, nano-scale component densities, clock rates, pin I/O, and voltage represent asymptotic operational regimes, one major area of untapped opportunity is computer architecture which has been severely limited by conventional practices of organization and control semantics. Mainstream computer architecture in HPC has been inhibited in innovation by the original von Neumann architecture of seven decades ago. Although notably diverse in form of parallelism exploited, six major epochs of computer architecture through to the present are all von Neumann derivatives. At their core is the use of single instruction issue and the prioritization of Floating Point ALU (FPU) utilization. However, in the modern age, FPUs consume only a small part of die real estate while the plethora of mechanisms to achieve maximum floating point efficiency take up the majority of the chip. The von Neumann bottleneck, the separation of memory and processor, is also retained. A revolution in computer architecture design is possible by undoing the damage of the von Neumann heritage and emphasizing the key challenges of data movement latency and bandwidth which are the true precious resources along with operation/instruction issue control. This paper discusses key tradeoffs that should drive computer architecture in what might be called the “Neo-Digital Age”.
Fast Models Dedicated to Simulation of Eddy Current Thermography
Authors: Ratsakou Almpion  , Reboud Christophe  , Skarlatos Anastasios  , Lesselier Dominique
Published: February 2025

This communication presents the first development aiming at efficiently simulating configurations of eddy current thermography for nondestructive evaluation. The numerical method proposed here is based on the Finite Integration Technique for both electromagnetic and thermal problems. Simulation results obtained using two different materials, steel and aluminum, are compared and discussed with respect to the presence of a flaw affecting the piece under test.
Reconstruction of Stress Corrosion Cracking Based on a Regularization Method Using Multi-Frequency Eddy Current Signals
Authors: Wang Li  , Chen Zhenmao
Published: February 2025

Stress corrosion cracking (SCC) is a prolong breakdown phenomena that occurs in several metal materials and may cause catastrophic accidents. Quantitative nondestructive testing of SCC is important in guaranteeing safety and highly efficient operation in large mechanical systems. However, reconstruction of SCC using eddy current testing (ECT) signals remains a problem given the complicated microstructure of SCC and the ill-posedness of the inverse problem. In this study, the relation between crack parameters and multi-frequency ECT signals is investigated; a regularization method that uses multi-frequency ECT signals is used to reduce the ill-posedness of SCC reconstruction. Conclusions are verified with simulated ECT signals from different conductive crack models.
Effect of Heat Treatment on Barkhausen Noise Behaviours of Deformed Low Carbon Steel
Authors: Kikuchi Hiroaki  , Sodekawa Hirotoshi  , Takekawa Kohei  , Ito Fumiya
Published: February 2025

We investigated the Barkhausen noise profiles for the undeformed and deformed low carbon steel when the specimens were heat treated with modifications of microstructures of specimen: recovery, recrystallization process and stress relief. The Barkhausen noise parameter is almost constant in heat treatment at a lower temperature, while it shows a rapid decrease with heat treatment time at higher temperatures. The recovery process contributes to the decreases of Barkhausen noise signal in heat treatment at higher temperatures.
Spectral Acceleration of Parallel Iterative Eigensolvers for Large Scale Scientific Computing
Authors: Bergamaschi Luca  , Martínez Ángeles
Published: February 2025

The computation of a number of the smallest eigenvalues of large and sparse matrices is crucial in various scientific applications, as the Finite Element solution of PDEs, electronic structure calculations or Laplacian of graphs, to mention a few. We propose in this contribution a parallel algorithm which is based on the spectral low-rank modification of a factorized sparse inverse preconditioner (RFSAI) to accelerate Newton-based iterative eigensolvers. Numerical results onto matrices arising from various realistic problems with size up to 5 million unknowns and 2.2×108nonzero elements account for the efficiency and the scalability of the proposed RFSAI–updated preconditioner.
Scalable Block-Tridiagonal Eigensolvers in the Context of Electronic Structure Calculations
Authors: Lamas Daviña Alejandro  , Cartoixà Xavier  , Román José E.
Published: February 2025

In computer-based numerical simulations, some methods to determine the electronic and optical properties of semiconductor nanostructures, require computing the energies that correspond to the interior eigenvalues of a Hamiltonian matrix. We study the case in which the Schrödinger equation is expanded into a matrix that has a block-tridiagonal structure. Additionally, the matrix can have two extra non zero blocks in the corners due to periodic boundary conditions. Given that not the whole eigenspectrum is required, we choose to use projection methods to compute the necessary set of eigenvalues. The shift-and-invert Lanczos method requires to solve a linear system at each iteration. We have developed a parallel code that improves the scalability of this step by exploiting the block structure. Results show that, to solve these specific cases, this method offers better scalability when compared to a general-purpose solver such as MUMPS.
A Parallel Scheduling Algorithm to Solve Triangular Band Systems on Multicore Machine
Authors: Marrakchi Sirine  , Jemni Mohamed
Published: February 2025

Solving triangular systems with band structure is considered as one of the main issues in computational linear algebra. This study describes a new approach in order to improve the parallel performance of the resolution problem. The experiments are performed on a multicore machine using OpenMP interface. The analysis and evaluation indicate that the proposed method minimizes effectively the execution time in comparison of the related works.
On Architecture for the Future Petascale Computing
Authors: Kučera Luděk
Published: February 2025

The supercomputers listed in Top500 and running the HPCG benchmark are usually not able to use more than 1–5 % of their peak computing power, and no system is able to achieve 1 PFlop/s. The paper shows that the Conjugate Gradient algorithm without preconditioning requires only weak bandwidth of interconnect links, but, being a problem of low arithmetic intensity (flop/byte ratio), it would need the memory bandwidth about 20 times greater than available.
Popularity-Based Caching of CMS Datasets
Authors: Meoni Marco  , Perego Raffaele  , Tonellotto Nicola
Published: February 2025

The distributed monitoring infrastructure of the Compact Muon Solenoid (CMS) experiment at the European Organization for Nuclear Research (CERN) records on a Hadoop infrastructures a broad variety of computing and storage logs. They represent a valuable source of information for system tuning and capacity planning. In this paper we analyze machine learning (ML) techniques on large amount of traces to discover patterns and correlations useful to classify the popularity of experiment-related datasets. We implement a scalable pipeline of Spark components which collect the dataset access logs from heterogeneous monitoring sources and group them into weekly snapshots organized by CMS sites. Predictive models are trained on these snapshots and forecast which dataset will become popular over time. Dataset popularity predictions are then used to experiment a novel strategy of data caching, called Popularity Prediction Caching (PPC). We compare the hit rates of PPC with those produced by well known caching policies. We demonstrate how the performance improvement is as high as 20% in some sites.
Self-Scheduling for a Heterogeneous Distributed Platform
Authors: García-González Luis A.  , García-Jacas César R.  , Acevedo-Martinez Liesner  , Trujillo-Rasúa Rafael A.  , Roose Dirk
Published: February 2025

We discuss schedulers for a heterogeneous distributed platform, designed to execute a variety of tasks in a non-dedicated environment. The platform uses and controls a large number of non-dedicated heterogeneous computational resources in a local network. Several self-scheduling algorithms have been adapted to take into account the computational capacity of each workstation of the network. To evaluate the schedulers we use the platform to execute a software tool for molecular docking. We analyze the performance of the self-scheduling algorithms and their impact on the execution time of the application.
Smart Systems, the Fourth Industrial Revolution and New Challenges in Distributed Computing
Authors: El Baz Didier  , Zhu Li
Published: February 2025

Smart systems and the smart world concept are addressed in the framework of the fourth industrial revolution. New challenges in distributed autonomous robots and computing are considered. An illustration of a new kind of smart and reconfigurable distributed modular robot system is given. A prototype is also presented as well as the associated distributed algorithm.
A Fast Implementation of a Spectral Finite Elements Method on CPU and GPU Applied to Ultrasound Propagation
Authors: Carrascal-Manzanares Carlos  , Imperiale Alexandre  , Rougeron Gilles  , Bergeaud Vincent  , Lacassagne Lionel
Published: February 2025

In this paper we present an optimization of a spectral finite element method implementation. The improvements consisted in the modification of the memory layout of the main algorithmic kernels and in the augmentation of the arithmetic intensity via loop transformations. The code has been deployed on multi-core SIMD machines and GPU. Compared to our starting point, i.e. the original scalar sequential code, we achieved a speed up of ×228 on CPU. We present comparisons with the SPECFEM2D code that prove the good performances of our implementation on similar cases. On GPU, a hybrid solution is investigated.
Strategies for Forward Modelling of Infrared Radiative Transfer on GPUs
Authors: Baumeister P.F.  , Rombach B.  , Hater T.  , Griessbach S.  , Hoffmann L.  , Bühler M.  , Pleiter D.
Published: February 2025

Satellite-based remote sensing in the mid-infrared spectral region can deliver a wealth of information on pressure, temperature, clouds and aerosols, and trace gas concentrations in the atmosphere. Interpreting the satellite measurements requires to solve an inverse modelling problem based on variational methods and a forward model evaluating the radiative transfer equations. As state-of-the-art satellite measurement campaigns require Petascale systems to process the data in due time, graphical processing units are employed for the high-throughput problem of computing the forward model for a given atmospheric state. We explore features of the considered architecture as well as relevant performance signatures of the different implementations to improve our understanding on opportunities for efficient exploitation of GPU-accelerated architectures based on the POWER2processor for this class of applications. Scalability is a key aspect as the application is known to scale well on massively-parallel architectures.
Using Complex-Network Properties for Efficient Graph Analysis
Authors: Messi Nguélé Thomas  , Tchuente Maurice  , Méhaut Jean-François
Published: February 2025

A complex network is a set of entities in a relationship, modeled by a graph where nodes represent entities and edges between nodes represent relationships. Graph algorithms have inherent characteristics, including data-driven computations and poor locality. These characteristics expose graph algorithms to several challenges, because most well studied (parallel) abstractions and implementation are not suitable for them. This work shows how to use some complex-network properties, including community structure and heterogeneity of node degree, to tackle one of the main challenges in graph analysis applications: improving performance, by a proper memory management and an appropriate thread scheduling. In this paper, we first proposed Cn-order, a heuristic that combines advantages of the most recent algorithms (Gorder, Rabbit and NumBaCo) to reduce cache misses in graph algorithms. Second, we proposed deg-scheduling, a degree-aware scheduling to ensure load balancing in parallel graph applications. Then we proposed commdeg-scheduling, an improved version of deg-scheduling that uses Cn-order to take into account graph order in scheduling. Experimental results on a 32 cores NUMA machine (NUMA4) (with Pagerank and livejournal for example) showed that Cn-order used with deg-scheduling (comm-deg-scheduling) outperforms the recent orders: with 32 threads, we reduce time by 26.81% compared to Gorder, 17.28% compared to Numbaco and 11.53% compared to Rabbit.
An Efficient SIMD Implementation of Pseudo-Verlet Lists for Neighbour Interactions in Particle-Based Codes
Authors: Willis James S.  , Schaller Matthieu  , Gonnet Pedro  , Bower Richard G.  , Draper Peter W.
Published: February 2025

In particle-based simulations, neighbour finding (i.e finding pairs of particles to interact within a given range) is the most time consuming part of the computation. One of the best such algorithms, which can be used for both Molecular Dynamics (MD) and Smoothed Particle Hydrodynamics (SPH) simulations, is the pseudo-Verlet list algorithm. This algorithm, however, does not vectorize trivially, and hence makes it difficult to exploit SIMD-parallel architectures. In this paper, we present several novel modifications as well as a vectorization strategy for the algorithm which lead to overall speed-ups over the scalar version of the algorithm of 2.24x for the AVX instruction set (SIMD width of 8), 2.43x for AVX2, and 4.07x for AVX-512 (SIMD width of 16).
Exploiting Hierarchical Parallelism in an Astrophysical Equation of State Using OpenACC and OpenMP
Authors: Messer O.E. Bronson  , Papatheodore Thomas
Published: February 2025

Modeling thermonuclear supernovae is a premier application for leadership-class supercomputers and requires multi-physics simulation codes to capture hydrodynamics, nuclear burning, gravitational forces, etc. As a nuclear detonation burns through the stellar material, it also increases the temperature. An equation of state (EOS) is then required to determine, for example, the new pressure associated with this temperature increase. In fact, an EOS is needed after thermodynamic conditions are changed by any physics routines. This means it is called many times throughout a simulation, requiring the need for a fast EOS implementation. Fortunately, these calculations can be performed independently during each time step, so the work can be offloaded to GPUs. Using results from the IBM/NVIDIA early test system (Summitdev, a precursor to the upcoming Summit supercomputer) at Oak Ridge National Laboratory, we describe a hybrid OpenMP implementation with offloaded work to GPUs. We compare performance results between the two implementations, with a discussion of some of the currently available features of OpenACC and OpenMP 4.5.
A Performance Study of Machine and Deep Learning Frameworks on Cineca HPC Systems
Authors: Zanella Riccardo  , Fiameni Giuseppe  , Rorro Marco
Published: February 2025

With the data doubling every year, data intensive applications are increasing as well as the demand of high-end resource capacity to analyse collected data sets. The explosion of analysis applications have become a major driver for revising system architecture and tools leading to the proliferation of software components and frameworks which may require multi-node and multi-core systems to scale-up and provide good performance. In this context, Machine learning and Deep learning are steadily proving to be successful methods for a variety of use cases, and their popularity has resulted in numerous open-source software tools becoming accessible to the public and popular across different scientific disciplines. But with the growth of applications and tools, it is becoming difficult for researchers to estimate how much resource is needed to run their analyses and select appropriate software and hardware components. The goal of this paper is to present the results of a preliminary comparative study of state-of-the-art machine and deep learning tools and benchmark them on Cineca HPC systems. The comparison has been done taking in consideration different factors including the impossibility to benchmark all tools available on the market, the existence of tools supporting hardware accelerators, such as GPU, and the availability of precedent studies [1,2]. Our preliminary results show that tested tools are able to leverage underneath system capabilities to achieve significant performance and that no single software exists that outperforms others opening space to further optimisation.