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Fortran compilers that provide support for Fortran’s native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while others provide limited or no support at all. This paper introduces a new generalized interface that is both compiler- and runtime-library-agnostic, providing flexibility while fully supporting all of Fortran’s parallel features. The Parallel Runtime Interface for Fortran (PRIF) was developed to be portable across shared- and distributed-memory systems, with varying operating systems, toolchains and architectures. It achieves this by defining a set of Fortran procedures corresponding to each of the parallel features defined in the Fortran standard that may be invoked by a Fortran compiler and implemented by a runtime library. PRIF aims to be used as the solution for LLVM Flang to provide parallel Fortran support. This paper also briefly describes our PRIF prototype implementation: Caffeine.

GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in emerging exascale systems. It provides network-independent, high-performance communication primitives including Remote Memory Access (RMA) and Active Messages (AM). GASNet-EX is an evolution of the popular GASNet communication system, building upon over 20 years of lessons learned, and the primary goals are high performance, interface portability, and expressiveness. The library has been used to implement parallel programming models and libraries such as UPC, UPC++, Fortran coarrays, Legion, Chapel, and many others.This anthology collects together the four separate volumes that currently comprise the GASNet-EX specification, as of the 2024.5.0 release of GASNet-EX.

Background Study Design and Methods Results Discussion Flow cytometry protocols for counting fresh CD34+ cell samples are not ideal for cryopreserved products due to cryoprotectant cytotoxicity. For cryopreserved samples, often large volumes of hypotonic solutions, which can cause cell death, are used to remove the cryoprotectant with a post‐thaw wash. We recently developed a novel multistep dilution method with subsequent flow cytometry analysis to allow for accurate and reproducible results. The previous method involved washing steps which invalidate the ability to enumerate cell recovery, and success had to be gauged solely on viability. The new method allows for assessment of total cell recovery and viable cell recovery.Apheresis products were cryopreserved in 10% DMSO at a target WBC concentration of 300 × 106/mL. Cryovials from these products were thawed at 37°C, and samples were diluted 1:2 by three additions of 1/3 sample volume using 1%‐Human Albumin in Dextran 40 (10% Low Molecular Weight Dextran in 0.9% NaCl) separated by 5 min between each addition. A 1:10 dilution was performed to obtain the correct cell concentration for flow cytometric analysis resulting in a 1:20 dilution. End WBC concentrations were ~15 × 106/mL with DMSO diluted to 0.5%.Fifty‐two samples were tested with this new method. Total and viable cell recoveries were calculated based on pre‐cryopreservation data. Median total cell recoveries for CD34 and CD3 were >85%, while median viable cell recoveries were >75%.Cryopreserved samples can be reliably prepared for flow cytometric testing using a step‐wise dilution to preserve cell integrity and robust recoveries. [ABSTRACT FROM AUTHOR]

Bone is highly vascularized living connective tissue responsible for important metabolic and mechanical functions. Progesterone was first isolated in 1934 by groups investigating the endocrine function of the corpus luteum. Sixty adult male domestic rabbits were selected, with an average weight of 1750 g and an age range of 8 to 10 months. After the rabbit was anesthetized, an incision 4 cm in length was made in the left leg above the femoral bone, followed by dissection of the muscle, fascia, and periosteum to expose the femoral bone. Two holes were made with a round bur of 3 mm, and the space between holes is about 1 cm. Each hole is away from the edge of the femoral bone by 1 cm. In the control negative group, the two holes were lifted without filling with any material, while in the control positive group, the two holes were filled by placement of gel-foam pieces, and in the study group, the holes were filled with gel-foam, which was soaked in hydroxyprogesterone caproate. The study group showed a closed defect faster than the other two groups; in seven days, it showed a narrow defect bone site area between the edge of the old bone under its granulation tissue, connective tissue with high new bone formation, cartilaginous tissue, and well angiogenesis, with a significance of p-value 0.033. In conclusion, HPC accelerates bone healing in different time intervals by activating osteoblasts to form bone. [ABSTRACT FROM AUTHOR]

In the world of high-performance computing, it is known that it can be beneficial to leave a small number of CPU cores unused, a practice termed undersubscription. However, undersubscription is rarely implemented in scientific applications of high-performance computing. We demonstrate the importance of calibrated undersubscription in Computational Fluid Dynamics simulations through the aggregated results of 1844 benchmarks. These benchmarks measured three hardware configurations and five different CFD models. On average, performance increased by 14% (weighted by node count). Performance improvements were most significant at large node counts, particularly when nearing a regime of negative scalability. We found that undersubscription could increase maximum performance by up to 50%; this advantage diminished as node count decreased but remained as much as 13% with a single node. In some cases, maximum performance was achieved with large numbers of free cores—nearly half of the cores in one case. Producing a regression from our dataset, we universally predict the optimal number of free cores as a function of cells per core. This regression achieves a 15% speed increase on average (again weighted by node count). [ABSTRACT FROM AUTHOR]

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible's powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI). [ABSTRACT FROM AUTHOR]

A new optimization strategy based on system performance optimization life cycle (SPOLC) is introduced for high-performance lattice Boltzmann simulations of three-dimensional decaying isotropic turbulence. This strategy improves the performance of turbulent flow simulations in periodic boxes at different resolutions using the lattice Boltzmann method (LBM). The strategy improves the performance by modifying the lattice Boltzmann model, mathematical representation, computational algorithm, software implementation, and computing hardware utilization. The modifications include: (1) Establishing the slice concept as a logical grouping layer added to the LBM, applying an aggregation–disaggregation mechanism, enabling two-dimensional (2D) operation on the three-dimensional (3D) model, (2) improving lattice data access pattern by using an alternative one-dimensional (1D) array for numerical representation instead of the 3D cubic representation, (3) major reduction in memory access iterations by switching from function-wise iteration method to lattice-wise iteration method by applying code fusion to the streaming, velocity and collision model functions and iterations, (4) applying process parallelization and data vectorization, (5) achieving a much more efficient utilization of modern compute units by increasing the adaption of stream processing model. Furthermore, a correctness validation process has been applied by conducting lattice-wise value comparisons between the proposed solution output and the original implementation output. Simulations of decaying isotropic turbulence at resolutions ranging from 323 to 5123 using the LBM are carried out for these purposes. Calculations are performed on two systems with distinct specifications, to validate the effectiveness and portability of the SPOLC strategy. The calculation times are significantly reduced after applying the SPOLC strategy on S1 with the lattice Boltzmann relaxation time τ=0.503 by over 69% compared to S2’s original time, increasing to 95.47% at higher resolutions. Different features of the flow fields are depicted and their characteristics are discussed. Thin tubes are visualized, and the energy spectra are studied. All fields are initialized by a forced turbulent field simulated in a previous study using the LBM. [ABSTRACT FROM AUTHOR]

In this study, hydroxypropyl cellulose (HPC) was utilized as the raw material, with the addition of β-cyclodextrin (β-CD), citric acid (CA) as the crosslinking agent, and sodium hypophosphite (SHP) as the catalyst to produce hydroxypropyl cellulose/β-CD composite films. The inclusion of β-CD resulted in an increase in the tensile strength of the film, with the maximum value of 13.5 MPa for the 1 % β-CD composite membrane. Additionally, after degradation in soil for 28 days, the degradation ability was significantly enhanced, with the 1.0 % β-CD composite film exhibiting the highest degradation rate of 27.21 %. Furthermore, the water permeability of the composite membrane was improved with the addition of β-CD. Specifically, when the β-CD content was 1.0 %, the water vapor transmission reached its lowest point at 2,445 g* (m 2 * 24 d) − 1 . The findings demonstrated that the 1 % β-cyclodextrin/hydroxypropyl cellulose composite film effectively preserved the freshness of strawberries, reducing the weight loss rate by 1.65 % compared to the control group. In conclusion, this research highlights the potential for preparing composite membranes using HPC and β-CD crosslinking, thereby expanding the application of hydroxypropyl cellulose and β-CD in food preservation. [ABSTRACT FROM AUTHOR]

Building and industrialization-related environmental harm is becoming an increasingly serious concern. In an effort to create an eco-friendly high-performance concrete (HPC), this paper addresses the idea of partially replacing cement with recyclable industrial waste. The current study will experimentally examine the slump, the strengths of compressive (SC) and porosity (P) of fifteen HPC mixtures manufactured from locally available resources. Hence, the effects of utilizing marble powder (MP) as a mineral additive in binary mixes and ternary with cement (PC) and granulated ground blast furnace slag (GGBFS) on the HPCs properties were studied in order to develop statistical models based on mixture design. Highly accurate prediction graphs and models were created for HPC workability, P at 28-day, SC at 7 and 28-day. All responses have satisfactory coefficients of correlation (R2 ≥ 0.76). Replacing cement with GGBFS causes a rise in slump in mixtures. Nevertheless, that only remains relevant when the mixtures have a small MP percentage (≤ 25%). A minor decrease in SC can be attributed by an increase of GGBFS. After 28-day, using GGBFS alone caused a little drop in SC; however, when GGBFS and PC were mixed, SC increased, in comparison with reference composition, and the porosity was reduced. Conversely, SC is superior with lower porosity when a small amount of MP is utilized. The best combination is HPC14, containing 5% GGBFS; it offers an optimal equilibrium among the three qualities; with HPC4's (15%GGBFS+5%MP) qualities, being almost identical to those of reference HPC15, a lower amount of cement may also be utilized. Findings encourage the use of MP and GGBFS to partially replace cement to produce eco-friendly and cost-effective HPC. An extremely high correlation coefficient indicated a strong relationship between P and SC. [ABSTRACT FROM AUTHOR]

This paper thoroughly assesses the ARM and RISC-V architectures in the context of high-performance computing (HPC). It includes an analysis of Docker and Kubernetes integration. Our study aims to evaluate and compare these systems' performance, scalability, and practicality in a general context and then assess the impact they might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in working with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM's preparedness and the potential of RISC-V while acknowledging the increased complexity and significant trade-offs involved at this point. [ABSTRACT FROM AUTHOR]

Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. The solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.

This paper investigates the efficient application of half-precision floating-point (FP16) arithmetic on GPUs for boosting LU decompositions in double (FP64) precision. Addressing the motivation to enhance computational efficiency, we introduce two novel algorithms: Pre-Pivoted LU (PRP) and Mixed-precision Panel Factorization (MPF). Deployed in both hybrid CPU-GPU setups and native GPU-only configurations, PRP identifies pivot lists through LU decomposition computed in reduced precision and subsequently reorders matrix rows in FP64 precision before executing LU decomposition without pivoting. Two variants of PRP, namely hPRP and xPRP, are introduced, differing in their computation of pivot lists in full half-precision or mixed half-single precision. The MPF algorithm generates FP64 LU factorization while internally utilizing hPRP for panel factorization, showcasing accuracy on par with standard DGETRF but with superior speed. The study further explores auxiliary functions required for the native mode implementation of PRP variants and MPF. [ABSTRACT FROM AUTHOR]

Evaluating GPU compute instances for High Performance Computing (HPC) and Artificial Intelligence (AI) applications in the cloud involves complex decision-making processes. This research applies the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank various GPU compute instances for HPC and AI from leading cloud providers: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI). The analysis incorporates a sensitivity examination, bootstrapping, and non-parametric tests to ensure robust and reliable rankings. Sensitivity analysis reveals the stability of the TOPSIS framework despite variations in criteria weights, while bootstrap analysis provides confidence intervals for the rankings, highlighting their consistency. The Friedman test confirms that ranking stability persists across different scenarios, indicating minimal impact from weight adjustments. Despite these insights, limitations such as interdependencies among criteria, data accuracy, and generalizability constraints must be acknowledged. This comprehensive approach ensures informed decision-making for selecting optimal GPU instances for cloud-based HPC and AI tasks. [ABSTRACT FROM AUTHOR]

Translation abstract Calculation model for thermal pre‐stressing of subsequently added reinforcementKey technical limitation of many strengthening measures is that they are not effective for the structure‘s self‐weight. As a solution, a targeted tempering of reinforcing bars put into respective slots is proposed for statically determinate load‐bearing structures. The aim is to thermally pre‐stress the bars, which are embedded in a grouting material, and to balance their strain to that of the initial reinforcement. The bond with the grout prevents the bars from re‐forming when cooling after heating is stopped. This creates pre‐stress that neutralizes the bending moment from the self‐weight and relieves the tensile zone of the cross‐section. In the paper, an analytical calculation model for the strengthening approach is presented and derived. This is verified through numerical calculations and a first experimental realization. The main factors influencing the effectiveness of the strengthening measure are analyzed in parameter studies. The results show that the thermal pre‐stressing of subsequently added reinforcing bars is effectively possible. Depending on the actual boundary conditions, the calculation predicts the strengthening effect with an accuracy of 80–97 %. The accuracy increases with the height and a lower initial temperature and a lower thermal conductivity of the concrete in the initial cross‐section. [ABSTRACT FROM AUTHOR]

Background: MCA bifurcation aneurysms pose treatment challenges because of the complex hemodynamics at the bifurcation and the risk of rupture. FDS implantation has been controversial and there are only limited reports. Therefore, the aim of this study was to assess the efficacy and safety of this treatment strategy using p64 MW HPC and p48 MW HPC FDSs for MCA bifurcation aneurysms, compared with the p64 classic FDS. Materials and methods: We retrospectively analyzed our institutional database and identified all patients with saccular, non-ruptured MCA bifurcation aneurysms treated with p64 MW HPC, p48 MW HPC, or p64 classic FDS implantation alone. Aneurysms with implantation of additional devices in the same session, previous treatments, and acutely ruptured and fusiform aneurysms were excluded. Results: A total of 79 aneurysms met the inclusion criteria: 23 receiving a p64 MW HPC, 34 receiving a p48 MW HPC, and 22 receiving a p64 classic FDS. The occlusion rate was highest for the p48 MW HPC 2 mm FDS, at 88.9% at FU2, compared with 72.2% for the p64 MW HPC and 70.6% for the p64 classic. The time to aneurysm occlusion was shortest with the p64 MW HPC, at 178.31 days. The highest retreatment rate was observed with the p48 MW HPC 3 mm. Conclusion: Treatment of MCA bifurcation aneurysms with a p48 MW HPC 2 mm or p64 MW HPC FDS is a safe and reliable strategy achieving high aneurysm occlusion rates - attributable to their lower porosity in relation to the parent vessel diameter as compared to the p48 MW HPC 3 mm FDS-, with reasonable morbidity and mortality. [ABSTRACT FROM AUTHOR]

The effectiveness of copper oxide-modified electrochemical sensors using different polymers is being studied. The commercial powder was sonicated in an isopropyl alcohol solution and distilled water with 5 wt% polymers (chitosan, Nafion, PVP, HPC, α-terpineol). It was observed that the chitosan and Nafion caused degradation of CuO, but Nafion formed a stable mixture when diluted. The modified electrodes were drop-casted and analyzed using cyclic voltammetry in 0.1 M KCl + 3 mM [Fe(CN)6]3−/4− solution to determine the electrochemically active surface area (EASA). The results showed that α-terpineol formed agglomerates, while HPC created uneven distributions, resulting in poor stability. On the other hand, Nafion and PVP formed homogeneous layers, with PVP showing the highest EASA of 0.317 cm2. In phosphate-buffered saline (PBS), HPC and PVP demonstrated stable signals. Nafion remained the most stable in various electrolytes, making it suitable for sensing applications. Testing in 0.1 M NaOH revealed HPC instability, partial dissolution of PVP, and Cu ion reduction. The type of polymer used significantly impacts the performance of CuO sensors. Nafion and PVP show the most promise due to their stability and effective dispersion of CuO. Further optimization of polymer–CuO combinations is necessary for enhanced sensor functionality. [ABSTRACT FROM AUTHOR]

Summary: Los Alamos National Laboratory has deployed (over the last year and a half) a pair of Cray Shasta machines—a development testbed named Guaje and and production machine named Chicoma, which will soon comprise the bulk of LANL's open science research computing portfolio. In the process, we have encountered a number of problems and challenges in several realms—authentication and authorization, cluster health management, image management, and configuration management. Both independently and in collaboration with Cray/HPE, we have found solutions and brought the system into stable production. The presentation will discuss the solutions and how they came about, and issues we are working to resolve in the near future. [ABSTRACT FROM AUTHOR]

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model's performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R 2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery. [ABSTRACT FROM AUTHOR]

The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25–26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used by various code teams, and consider the adoption of industry-standard benchmark problems to better facilitate code-to-code performance comparisons. Participants reported that identifying and implementing suitable Monte Carlo algorithms for GPUs continues to be a sticking point. They also report significant difficulty porting existing algorithms between GPU APIs (specifically Nvidia CUDA to AMD ROCm). To better compare code-to-code performance, participants decided to design a C5G7-like benchmark problem with a defined figure of merit, with the expectation of adding more benchmarks in the future. The participants also identified the need to explore the intermediate and long-term future of the Monte Carlo neutron transport community and how best to modernize and contextualize Monte Carlo as a useful tool in modern industry. Overall the summit was considered to be a success by the organizers and participants, and the group shared a strong desire for future, potentially larger, Monte Carlo summits. [ABSTRACT FROM AUTHOR]

The solution of reactive computational fluid dynamics (CFD) simulations is accelerated by the implementation of a hybrid central processing unit/graphics processing units (CPU/GPU) Finite Volume solver based on the operator‐splitting strategy, where the chemistry integration is treated independently of the flow solution. The integration of ordinary differential equations (ODEs) describing the finite‐rate chemical kinetics is solved by an adaptive multi‐block explicit solver on GPUs, while the load of the fluid solution is distributed on a multicore CPU algorithm. The resulting speed‐up for reactive CFD simulations is up to 10×$$ \times $$; the performance gain increases with the size of the mechanism. The proposed implementation is general and can be applied to any CFD problem where the governing equations for the fluid transport are coupled with an ODE system. Code validation is performed against reference solutions on a selection of test cases involving reacting flows. [ABSTRACT FROM AUTHOR]

To predict the favorable thermodynamical conditions and characterize cryogenic pellet formations for applications in nuclear fusion reactors, a high–throughput molecular dynamics study based on a unified framework to simulate the growth process of cryogenic solids (molecular deuterium, neon, argon) under gas pressure have been designed. These elements are used in fusion nuclear plants as fuel materials and to reduce the damage risks for the plasma-facing components in case of a plasma disruption. The unified framework is based on the use of workflows that permit management in HPC facilities, the submission of a massive number of molecular dynamics simulations, and handle huge amounts of data. This simplifies a variety of operations for the user, allowing for significant time savings and efficient organization of the generated data. This approach permits the use of large-scale parallel simulations on supercomputers to reproduce the solid–gas equilibrium curves of cryogenic solids like molecular deuterium, neon, and argon, and to analyze and characterize the reconstructed solid phase in terms of the separation between initial and reconstructed solid slabs, the smoothness of the free surfaces and type of the crystal structure. These properties represent good indicators for the quality of the final materials and provide effective indications regarding the optimal thermodynamical conditions of the growing process. [ABSTRACT FROM AUTHOR]

The harsh geological conditions in the northwest region of China, characterized by widespread saline-alkali soil rich in alkali ions, pose a high risk of Alkali-Silica reaction (ASR) in concrete, particularly due to the presence of ASR-active natural river sands. To address ASR hazards, locally applied concrete often employs High-Performance concrete (HPC) prepared with high proportions of mineral admixtures. In this paper, the alkali content is controlled by adding mixed water with NaOH to the initial configuration of concrete, and three different alkali content states are set up. A 1 mol/L NaOH solution was used to simulate alkaline conditions, and HPC specimens were immersed for an extended period to investigate the effects of equivalent alkali content, immersion time, concrete strength, and admixture on the flexural mechanical properties of HPC under the condition of long-term alkali immersion. Results indicate that, the strength grade was positively correlated with the flexural strength of HPC, but the alkali content was negatively. Initial immersion significantly enhances strength, followed by a gradual decline after long-term immersion. Among three types of admixture addition methods, the impact on flexural strength of HPC immersed in alkaline solution for 10 years follows the order: Double doped air entraining agent and rust inhibitor is greater than single doped air entraining agent is greater than single doped rust inhibitor. In the process of macroscopic test, it is difficult to observe the variation rule of stress and strain in detail, only the final aggregate failure mode can be analyzed. In order to analyze the strain change of the specimen and the failure process of the aggregates more accurately, a three-dimensional random aggregate concrete mesoscopic model was established, and equations relating microhardness to the mechanical properties of concrete components were derived from statistical analysis, providing a basis for parameter selection in the model. Results demonstrate that with increasing strength, the occurrence time of initial cracks is delayed, and the ratio of cracks bypassing aggregates (cracks develop along the ITZ between aggregate and mortar until complete failure) decreases, and the ratio of cracks penetrating aggregates (cracks develop directly through aggregates in an almost vertical direction) increases. [ABSTRACT FROM AUTHOR]

The ability to accurately predict when a job on a supercomputer will leave the queue and start to run is not only beneficial for providing insights to users, but can also help enable non‐traditional HPC workloads that are not necessarily suited to the batch queue style‐approach that is ubiquitous on production HPC machines. However there are numerous challenges in achieving such a prediction with high accuracy, not least because the queue's state can change rapidly and depend upon many factors. In this work, we explore a novel machine learning approach for predicting queue wait times, hypothesising that such a model can capture the complex behavior resulting from the queue policy and other interactions to generate accurate job start times. For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600), and 4‐cabinet (HPE Cray EX) we explore how different machine learning approaches and techniques improve the accuracy of our predictions, comparing against the estimation generated by Slurm. By combining categorization and regression models, we demonstrate that our approach delivers the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within 1 min of the actual start time for around 65% of jobs on ARCHER2 and 4‐cabinet, and 76% of jobs on Cirrus. When compared against what Slurm can deliver, via the backfill plugin, this represents around 3.8 times better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our approach can accurately predicting the start time for three quarters of all job within 10 min of the actual start time on ARCHER2 and 4‐cabinet, and for 90% of jobs on Cirrus. Whilst the initial driver of this work was to better facilitate non‐traditional, interactive and urgent, workloads on HPC machines, the insights gained can also be used to provide wider benefits to users, enrich existing batch queue systems, and inform supercomputing center policy also. [ABSTRACT FROM AUTHOR]

Filesystems are essential components in contemporary computer systems that organize and manage data. Their performance is crucial in various applications, from web servers to data storage systems. This paper helps to pick the suitable filesystem by comparing btrfs with ZFS by considering multiple situations and applications, ranging from sequential and random performance in the most common use cases to extreme use cases like high-performance computing (HPC). It showcases each option's benefits and drawbacks, considering different usage scenarios. The performance of btrfs and ZFS will be evaluated through rigorous testing. They will assess their capabilities in handling huge files, managing numerous small files, and the speed of data read and write across varied usage levels. The analysis indicates no definitive answer; the selection of the optimal filesystem is contingent upon individual data-access requirements. [ABSTRACT FROM AUTHOR]

In regions characterized by the challenging combination of brine corrosion in the salt lakes and river sand with alkali silica reaction (ASR) activity in areas of the Northwest, high-performance concrete (HPC) formulated with high-volume composite mineral admixtures as ASR suppression measures has been preferred for civil engineering structures in the region. This study investigates the splitting tensile strength, corrosion products, microscopic structure characteristics, and mesoscopic mechanical mechanisms of splitting failure of such HPC under 10-year corrosion from salt lake brine. The relationship between mechanical properties and corrosion damage, as well as the characteristics of internal crack propagation paths and failure mechanisms of HPC under splitting load, are explored. The findings reveal that as the alkali content within HPC rises, corrosion damage intensifies, resulting in a reduction in splitting tensile strength. Moreover, a linear association between mechanical properties and corrosion damage is observed. Microscopic structural analysis and numerical simulation of the splitting failure process of HPC elucidate that while the substantial presence of mineral admixtures effectively suppresses the ASR risk associated with alkali-reactive aggregates in concrete, uneven ASR gel products persist. These discontinuous micro-fine interface cracks induced by the gel products and the cracks induced by the gel products around the selective alkali-active aggregate particles distributed in the local area are the initiation sources of mortar cracks in HPC splitting failure. In terms of the overall failure state observed during the concrete splitting process, mortar cracks manifest two distinct extension paths: along the coarse aggregate interface and directly through the aggregates themselves. Notably, a greater proportion of coarse aggregates are directly penetrated by mortar cracks, as opposed to the number of interface failures bypassing coarse aggregates. More importantly, the above work establishes a theoretical reference in three dimensions: macroscopic, mesoscopic, and microscopic, for studying concrete corrosion damage in complex environments such as salt lake brine corrosion and ASR inhibition. [ABSTRACT FROM AUTHOR]

We introduce OpenRAND, a C++17 library aimed at facilitating reproducible scientific research by generating statistically robust yet replicable random numbers in as little as two lines of code, overcoming some of the unnecessary complexities of existing RNG libraries. OpenRAND accommodates single and multi-threaded applications on CPUs and GPUs and offers a simplified, user-friendly API that complies with the C++ standard’s random number engine interface. It is lightweight; provided as a portable, header-only library. It is statistically robust: a suite of built-in tests ensures no pattern exists within single or multiple streams. Despite its simplicity and portability, it remains performant—matching and sometimes outperforming native libraries. Our tests, including a Brownian walk simulation, affirm its reproducibility and ease-of-use while highlight its computational efficiency, outperforming CUDA’s cuRAND by up to 1.8 times.

In short, the current status of the HPC ecosystem in Bulgaria includes diverse modern hardware (including two supercomputers from the TOP500 list – Discoverer and HEMUS, as well as multiple HPC clusters), and extensive expertise in HPC and the adjacent fields. The competencies and expertise were acquired during a multitude of successful EU projects, as well as leadership and participation in high-profile national programmes and projects with industry/SMEs. Experience in negotiating and managing contracts with different sources of funding and for the use of both equipment and expertise has been gathered. In this paper, we discuss the new developments in the HPC Ecosystem, supported by the acquisition of the HEMUS supercomputer in 2023, as well as our design choices and optimization approaches.