High Performance Computing Cluster Research Articles

Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications remains challenging. DL algorithms are often computationally expensive, power-hungry, and require large memory to process complex and iterative operations of millions of parameters. Hence, training and inference of DL models are typically performed on high-performance computing (HPC) clusters in the cloud. Data transmission to the cloud results in high latency, round-trip delay, security and privacy concerns, and the inability of real-time decisions. Thus, processing on edge devices can significantly reduce cloud transmission cost. Edge devices are end devices closest to the user, such as mobile phones, cyber–physical systems (CPSs), wearables, the Internet of Things (IoT), embedded and autonomous systems, and intelligent sensors. These devices have limited memory, computing resources, and power-handling capability. Therefore, optimization techniques at both the hardware and software levels have been developed to handle the DL deployment efficiently on the edge. Understanding the existing research, challenges, and opportunities is fundamental to leveraging the next generation of edge devices with artificial intelligence (AI) capability. Mainly, four research directions have been pursued for efficient DL inference on edge devices: 1) novel DL architecture and algorithm design; 2) optimization of existing DL methods; 3) development of algorithm–hardware codesign; and 4) efficient accelerator design for DL deployment. This article focuses on surveying each of the four research directions, providing a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference.

Secure HPC: A workflow providing a secure partition on an HPC system

Driven by the progress of data and compute-intensive methods in various scientific domains, there is an increasing demand from researchers working with highly sensitive data to have access to the necessary computational resources to be able to adapt those methods in their respective fields. To satisfy the computing needs of those researchers cost-effectively, it is an open quest to integrate reliable security measures on existing High Performance Computing (HPC) clusters. The fundamental problem with securely working with sensitive data is, that HPC systems are shared systems that are typically trimmed for the highest performance—not for high security. For instance, there are commonly no additional virtualization techniques employed, thus, users typically have access to the host operating system. Since new vulnerabilities are being continuously discovered, solely relying on the traditional Unix permissions is not secure enough. In this paper, we discuss Secure HPC, a workflow allowing users to transfer, store and analyze data with the highest privacy requirements. Our contributions are the design of a multi-node secure workflow with parallel I/O, a strict security model enforced by the system and network features, and lastly the demonstration of a medical use case. In our experiments, we see an advantage in the asynchronous execution of IO requests in dm_crypt, while reaching 80% of the ideal performance. When comparing eCryptFS with GoCryptFS as two representative filesystem-level encryption stacks, eCryptFS was twice as fast. In a real use case, we observed on average 97% of the native performance.

Massively parallelized interpolated factored Green function method

This paper presents the first parallel implementation of the novel “Interpolated Factored Green Function” (IFGF) method introduced recently for the accelerated evaluation of discrete integral operators arising in wave scattering and other areas (Bauinger and Bruno, Jour. Computat. Phys., 2021). On the basis of the hierarchical IFGF interpolation strategy, the proposed (hybrid MPI-OpenMP) parallel implementation results in efficient data communication, and it scales up to large numbers of cores—without any hard limitations on the number of cores efficiently employed. Moreover, on any given number of cores, the proposed parallel approach preserves the O(Nlog⁡N) computing cost inherent in the sequential version of the IFGF algorithm. Unlike other approaches, the IFGF method does not utilize the Fast Fourier Transform (FFT), and it is thus better suited for efficient parallelization in distributed-memory computer systems. In particular, the IFGF method relies on a “peer-to-peer” strategy wherein, at every level, field propagation is directly enacted via “exchanges” between “peer” polynomials of constant degree, without data accumulation in large-scale “telephone-central” mathematical constructs such as those used in the Fast Multipole Method (FMM) and pure FFT-based approaches. A variety of numerical results presented in this paper illustrate the character of the proposed parallel algorithm, in particular demonstrating scaling from 1 to all 1,680 cores available in the High Performance Computing cluster used, and for problems of up to 4,096 wavelengths in acoustic size.

OctCarb—A GNU Octave Script for the Analysis and Evaluation of Wide-Angle Scattering Data of Non-Graphitic Carbons

We present a free software script operating in GNU Octave for the refinement of wide-angle X-ray and neutron scattering (WAXS/WANS) data of non-graphitic carbons (NGCs). The refinement script (OctCarb) is based on the evaluation approach of Ruland and Smarsly (2002). As result, up to 14 physically meaningful parameters such as the layer extension La, the stack height Lc, as well as the degree of disorder of the graphenes and their stacking are obtained through a well-established fitting routine. In addition, background scattering based on specific physical phenomena and different correction parameters such as polarization and absorption can be considered. Since the complex mathematical calculations are implemented and performed in the background, with only a few settings to be made, the software was designed to be usable by inexperienced users. As another key feature, Octave and thus OctCarb run on all common operating systems (Windows, MacOS and Linux), and can even be used on high-performance computing clusters (HPCs) to perform multiple calculations at once. In addition to this, the whole refinement can be performed within minutes, and it is possible to tweak and optimize it for special purposes and measuring geometries. These features make OctCarb useful for all scientists dealing with the characterization of NGCs by X-ray or neutron scattering techniques.

High-resolution resonant portraits of a single-planet white dwarf system

ABSTRACT The dynamical excitation of asteroids due to mean motion resonant interactions with planets is enhanced when their parent star leaves the main sequence. However, numerical investigation of resonant outcomes within post-main-sequence simulations is computationally expensive, limiting the extent to which detailed resonant analyses have been performed. Here, we combine the use of a high-performance computer cluster and the general semi-analytical libration width formulation of Gallardo, Beaugé & Giuppone in order to quantify resonant stability, strength, and variation instigated by stellar evolution for a single-planet system containing asteroids on both crossing and non-crossing orbits. We find that resonant instability can be accurately bound with only main-sequence values by computing a maximum libration width as a function of asteroid longitude of pericentre. We also quantify the relative efficiency of mean motion resonances of different orders to stabilize versus destabilize asteroid orbits during both the giant branch and white dwarf phases. The 4:1, 3:1, and 2:1 resonances represent efficient polluters of white dwarfs, and even when in the orbit-crossing regime, both the 4:3 and 3:2 resonances can retain small reservoirs of asteroids in stable orbits throughout giant branch and white dwarf evolution. This investigation represents a preliminary step in characterizing how simplified extrasolar Kirkwood gap structures evolve beyond the main sequence.

A parallel ADMM-based convex clustering method

Convex clustering has received recently an increased interest as a valuable method for unsupervised learning. Unlike conventional clustering methods such as k-means, its formulation corresponds to solving a convex optimization problem and hence, alleviates initialization and local minima problems. However, while several algorithms have been proposed to solve convex clustering formulations, including those based on the alternating direction method of multipliers (ADMM), there is currently a limited body of work on developing scalable parallel and distributed algorithms and solvers for convex clustering. In this paper, we develop a parallel, ADMM-based method, for a modified convex clustering sum-of-norms (SON) formulation for master–worker architectures, where the data to be clustered are partitioned across a number of worker nodes, and we provide its efficient, open-source implementation (available on Parallel ADMM-based convex clustering. https://github.com/lidijaf/Parallel-ADMM-based-convex-clustering. Accessed on 10 June 2022) for high-performance computing (HPC) cluster environments. Extensive numerical evaluations on real and synthetic data sets demonstrate a high degree of scalability and efficiency of the method, when compared with existing alternative solvers for convex clustering.

A container-based workflow for distributed training of deep learning algorithms in HPC clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters.

An online workflow scheduling algorithm considering license limitation in heterogeneous environment

AbstractWith the development of the IC industry, electronic design automation (EDA) tools are also developing. Currently, EDA tools have been migrating to high‐performance computing clusters (HPC) or the cloud to meet increasing computing and storage requirements. EDA tasks are scientific workflows, whose scheduling is a well‐known NP‐hard problem. In this paper, we propose a novel workflow scheduling algorithm HEWS, which achieves better performance through a novel hierarchical sorting method. We conducted a series of simulation experiments in different environments, and the experimental results show that our HEWS scheduling algorithm achieves better scheduling performance compared to several conventional scheduling methods, the waiting time and makespan of workflow are significantly reduced.

I/O performance analysis of machine learning workloads on leadership scale supercomputer

The popularity of machine learning technologies and frameworks has led to an increasingly large number of machine learning workloads running on high-performance computing (HPC) clusters. The ML workflows are readily being adopted in diverse computational fields such as Biology, Physics, Materials, and Computer Science. The I/O behavior of the emerging ML workloads distinctly differs from the traditional HPC workloads, such as simulation or checkpoint/restart-based HPC I/O behavior. Additionally, the ML workloads have also pushed for the utilization of GPUs or a combination of CPUs and GPUs in addition to using only CPUs for computational tasks. The diverse and complex I/O behavior of ML workloads requires extensive study and is critical for the efficient performance of various layers of the I/O stack and the overall performance of HPC workloads. This work aims to fill the gap in understanding the I/O behavior of emerging ML workloads by providing an in-depth analysis of ML jobs running on large-scale leadership HPC systems. In particular, we have analyzed the behavior of jobs based on the scale of the jobs, the science domains, and the processing units used by the ML jobs. The analysis was performed on 23,000 ML jobs collected from one year of Darshan logs running on Summit, which is one of the fastest supercomputers. We also collect the CPU and GPU usage of 15,165 ML jobs by merging the Darshan dataset with the power usage of the processing units on Summit. Therefore, this paper is able to provide a systematic I/O characterization of ML workloads on a leadership scale HPC machine to understand how the I/O behavior differs for workloads across various science domains, the scale of workloads, and processing units and analyze the usage of parallel file system and burst buffer by ML I/O workloads. We have made several observations regarding I/O performances and access patterns through various analytical studies and discuss the important lessons learnt from the perspective of a ML user and a storage architect for emerging ML workloads running on large-scale supercomputers.

Deep Learning Algorithms and Parallel Distributed Computing Techniques for High-Resolution Load Forecasting Applying Hyperparameter Optimization

Electrical load forecasting is one of the critical tasks that helps power utility companies in planning and operation as well as the energy managementsystem (EMS) in controlling and optimizing the power grid’s performance. It guarantees system adequacy and reliability by reducing the gap between supply and demand, and it bolsters cybersecurity by enabling false data detection and system security by predicting system peak load. This article aims to build an accurate long-term load forecasting model based on deep learning (DL) algorithms. Initially, big-data analytics and feature engineering are applied to the power load and weather historical data. Next, three different load forecasting models are developed based on three corresponding deep neural network models (DNN): the simple RNN, the long short term memory (LSTM), and the gated recurrent unit (GRU), respectively. To find an accurate DNN model, a two-step hyperparameter optimization is performed consisting of cross-validation. In the first step, the random search (RS) algorithm is employed to narrow the parameter search space to a smaller local one. In the second step, the grid search (GS) algorithm is used to find the best hyperparameters in the local area located by RS. However, the computational burden of the DNN training process is too high and may lead to out-of-memory (OOM) issues, especially when we are dealing with a big volume of data. To overcome this problem, we developed our Python program based on Tensorflow framework and implemented the framework on a high-performance computer cluster (HPCC). Then, different jobs are defined to employ parallel distributed computing (PDC) capability on the SIU BigDawg HPCC. To this end, all DNN hyperparameters (e.g., number of layers, neurons, dropout, batch size, epoch, learning rate, etc.) can be optimized and result in a comprehensive and accurate load forecasting model. Our proposed method, along with the programming framework, is evaluated on a real-world dataset. Among the three models developed and tested, the GRU, with load and weather as features, has demonstrated excellent performance and outperformed the other two models for long-term forecasting with the best metrics (i.e., MSE, MAPE, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^2$</tex-math></inline-formula> ) and daily error almost less than 1%. Also, our best model (optimized GRU) is compared with the other reported state-of-the-art models such as DBN, SVR, RM-LSTM, DM-GCNN, TCMS-CNN. It is demonstrated that the proposed framework yields a superior performance than the others.

Monte Carlo performance study of virtual high performance computing cluster over cloud

ObjectiveAs a high computation cost discipline, nuclear science and engineering still relies heavily on traditional high performance computing (HPC) clusters. However, the usage of traditional HPC for nuclear science and engineering has been limited due to the poor flexibility, the software compatibility and the poor user interfaces. Virtualized/virtual HPC (vHPC) can mimic an HPC by using a cloud computing platform. In this work, we designed and developed a vHPC system for employment in nuclear engineering. MethodsThe system is tested using the computation of the number π by Monte Carlo and an X-ray digital imaging system simulation. The performance of the vHPC system is compared with that of the traditional HPCs. ResultsAs the number of the simulated particles increases, the virtual cluster computing time grows proportionally. The time used for the simulation of the X-ray imaging was about 21.1 ​h over a 12 kernels virtual server. Experimental results show that the performance of virtual cluster computing and the actual physical machine is almost the same. ConclusionsFrom these tests, it is concluded that vHPC is a good alternative for employing in nuclear engineering. The proposed vHPC in this paper will make HPC flexible and easy to deploy.

A DFT+U Study of the Electrochemical Oxidation of H2 and CO on SrLaFeO4-Δ

INTRODUCTION Solid oxide fuel cell (SOFC) anodes are commonly composed of cermet materials, typically Ni-YSZ. Despite success in electrochemical performance, cermet materials suffer from sulfur poisoning, coke formation under low operating temperatures, and metal coarsening. Due to success of material design focused on the cathode such as LSCF, single-phase mixed ionic-electronic conducting (MIEC) materials have displayed promising oxidation activity [1]. The Sr-based Ruddlesden-Popper (RP) family has found recent use as a base stoichiometry in anodes where experimentalists have used both A and B-site doping and nanoparticle exsolvation strategies in order to tune activity [2,3]. In order to understand the fundamental electrochemical activity, we want to elaborate on the mechanism of H2 and CO oxidation on the promising material SrLaFeO4- \U0001d6ff (SLF). In the present study, we used ab initio methods to model H2 and CO oxidation on two FeO2-based models of SLF with and without B-site doping with Co and Ni. COMPUTATIONAL DETAILS All calculations presented in this work were performed using the spin-polarized DFT+U method with periodic boundary conditions as implemented in the VASP 5.4.4 code. We choose the PBE functional to describe exchange and correlation effects. Dudarev’s approach for DFT+U calculations is used with a U-J value of 4.0, 3.32, and 6.2 eV, respectively. We used the projector-augmented wave (PAW) method to represent the inner core potentials. The kinetic energy cutoff was set for all calculations to 700 eV, and integration over the Brillouin zone was performed with 3 × 3 × 1 Monkhorst-Point k-point mesh and the Gaussian smearing method with a sigma value of 0.05 eV. Slab models utilized a 15 Å vacuum layer and the bottom two layers were fixed to bulk position values. The DFT-derived parameters were employed in a microkinetic model to calculate the surface coverage of all adsorbed species for a given reaction condition. RESULTS AND DISCUSSION To begin our study, we tested all non-symmetrical conformers of SLF for the lowest relative energy on a model 2 × 2 × 1 supercell, as displayed in Figure 1a. A total of 3 structures were tested – P4/mmm, P4/nmm, and I4mm. We confirm that the lowest energy structure (I4mm) is equivalent to the experimentally reported structure. Next, we develop two 1.5 × 1.5 × 1 non-identical (001) FeO2-based slab models – so-called FeO2-LaO and FeO2-SrO (Figure 2b). The oxidation mechanism of H2 suggests oxidation begins with a dissociative adsorption step. Subsequent steps include formation of surface H2O, a surface vacancy, and a subsurface vacancy. To complete the catalytic cycle, we computed the bulk vacancy migration of SLF and assumed fast cathode kinetics for the overall cell model. At an operating voltage of 0.7 V, we predict that the highest rate occurs on the FeO2-SrO surface. We predict that H2 dissociative adsorption step is the rate-determining process based on Campbell’s degree of rate control. With the application of a single B-site dopant (Co and Ni), Co and Ni both decrease the barrier of the H2 dissociative adsorption step. Ni causes a slight decrease in energy for the formation energy of H2O. The oxidation mechanism of CO suggests oxidation begins with adsorption of the CO molecule on a reduced Fe site. The CO migrates to a neighboring Fe-O complex in order to form surface CO2. Based on degree of rate control, CO2 formation is likely the rate-determining step for both surface models.In conclusion, this is one of the first mechanism investigations on the anodic oxidation reactions of H2 and CO on RP-based oxides. We find that H2 has promising catalytic performance similar to the experimental evidence. The inclusion of B-site dopants displays that catalytic performance can be tuned with the introduction of different transition metals. Future work will examine the role of exsolved particles for RP-based oxides. ACKNOWLEDGMENTS This work has been funded by the Division of Materials Research, a National Science Foundation organization under Award Number 1832809. Computing resources provided by the San Diego Supercomputer Center (SDSC) and Texas Advanced Computing Center (TACC) under XSEDE grant number TG-CTS090100 and USC High-Performance Computing clusters are gratefully acknowledged. REFERENCES L. Xu, Y. Yin, N. Wang, Z. Ma, Electrochem. Commun., 76, 51–54 (2017)N. Wu, W. Wang, Y. Zhong, G. Yang, J. Qu, Z. Shao, ChemElectroChem, 4, 2378–2384 (2017)H. Chong, H. Chen, Z. Shao, J. Shi, J. Bai, S. Li, J. Mater. Chem. A, 4, 13997–14007 (2016) FIGURE 1a: 2×2×1 supercell model of I4mm SrLaFeO4 FIGURE 1b: Slab models of (001) FeO2-LaO and FeO2-SrO Figure 1

Performance Evaluation of Open-Source Serverless Platforms for Kubernetes

Serverless computing has grown massively in popularity over the last few years, and has provided developers with a way to deploy function-sized code units without having to take care of the actual servers or deal with logging, monitoring, and scaling of their code. High-performance computing (HPC) clusters can profit from improved serverless resource sharing capabilities compared to reservation-based systems such as Slurm. However, before running self-hosted serverless platforms in HPC becomes a viable option, serverless platforms must be able to deliver a decent level of performance. Other researchers have already pointed out that there is a distinct lack of studies in the area of comparative benchmarks on serverless platforms, especially for open-source self-hosted platforms. This study takes a step towards filling this gap by systematically benchmarking two promising self-hosted Kubernetes-based serverless platforms in comparison. While the resulting benchmarks signal potential, they demonstrate that many opportunities for performance improvements in serverless computing are being left on the table.

Understanding hot interconnects with an extensive benchmark survey

Understanding the designs and performance characterizations of hot interconnects on modern data center and high-performance computing (HPC) clusters is a fruitful research topic in recent years. The rapid and continuous growth of high-bandwidth and low-latency communication requirements for various types of data center and HPC applications (such as big data, deep learning, and microservices) has been pushing the envelope of advanced interconnect designs. We believe this is high time to investigate the performance characterizations of representative hot interconnects with different benchmarks. Hence, this paper presents an extensive survey of state-of-the-art hot interconnects on data center and HPC clusters and the associated representative benchmarks to help the community to better understand modern interconnects. In addition, we characterize these interconnects by the related benchmarks under different application scenarios. We provide our perspectives on benchmarking data center interconnects based on our survey, experiments, and results.

Generalised network architectures for environmental sensing: Case studies for a digitally enabled environment

A digitally enabled environment is a setting which incorporates sensors coupled with reporting and analytics tools for understanding, observing or managing that environment. Large scale data collection and analysis are a part of the emerging digitally enabled approach for the characterisation and understanding of our environment. It is recognised as offering an effective methodology for addressing a range of complex and interrelated social, economic and environmental concerns. The development and construction of the approach requires advances in analytics control linked with a clear definition of the issues pertaining to the interaction between elements of these systems. This paper presents an analysis of selected issues in the field of analytics control. It also discusses areas of progress, and areas in need of further investigation as sensing networks evolve. Three case studies are described to illustrate these points. The first is a physical analytics test kit developed as a part of the “Reinvent the Toilet Challenge” (RTTC) for process control in a range of environments. The second case study is the Cranfield Urban Observatory that builds on elements of the RTTC and is designed to allow users to develop user interfaces to monitor, characterise and compare a variety of environmental and infrastructure systems plus behaviours (e.g., water distribution, power grids). The third is the Data and Analytics Facility for National Infrastructure, a cloud-based high-performance computing cluster, developed to receive, store and present such data to advanced analytical and visualisation tools. • Studying a generalised network architecture framework for environmental sensing. • Discussing digital enabled environment approaches in context of analytics control. • Case study 1: a physical analytics test kit in “Reinvent the Toilet Challenge”. • Case study 2: the Cranfield Urban Observatory and Living Laboratory. • Case study 3: the Data and Analytics Facility for National Infrastructure.

Distributed Computing in a Pandemic

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.

Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications

Distributed supercomputing is becoming common in different companies and academia. Most of the parallel computing researchers focused on harnessing the power of commodity processors and even internet computers to aggregate their computation powers to solve computationally complex problems. Using flexible commodity cluster computers for supercomputing workloads over a dedicated supercomputer and expensive high-performance computing (HPC) infrastructure is cost-effective. Its scalable nature can make it better employed to the available organizational resources, which can benefit researchers who aim to conduct numerous repetitive calculations on small to large volumes of data to obtain valid results in a reasonable time. In this paper, we design and implement an HPC-based supercomputing facility from commodity computers at an organizational level to provide two separate implementations for cluster-based supercomputing using Hadoop and Spark-based HPC clusters, primarily for data-intensive jobs and Torque-based clusters for Multiple Instruction Multiple Data (MIMD) workloads. The performance of these clusters is measured through extensive experimentation. With the implementation of the message passing interface, the performance of the Spark and Torque clusters is increased by 16.6% for repetitive applications and by 73.68% for computation-intensive applications with a speedup of 1.79 and 2.47 respectively on the HPDA cluster. We conclude that the specific application or job could be chosen to run based on the computation parameters on the implemented clusters.

Hamiltonian open quantum system toolkit

We present an open-source software package called “Hamiltonian Open Quantum System Toolkit" (HOQST), a collection of tools for the investigation of open quantum system dynamics in Hamiltonian quantum computing, including both quantum annealing and the gate-model of quantum computing. It features the key master equations (MEs) used in the field, suitable for describing the reduced system dynamics of an arbitrary time-dependent Hamiltonian with either weak or strong coupling to infinite-dimensional quantum baths. We present an overview of the theories behind the various MEs and provide examples to illustrate typical workflows in HOQST. We present an example that shows that HOQST can provide order of magnitude speedups compared to “Quantum Toolbox in Python" (QuTiP), for problems with time-dependent Hamiltonians. The package is ready to be deployed on high performance computing (HPC) clusters and is aimed at providing reliable open-system analysis tools for noisy intermediate-scale quantum (NISQ) devices.

Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System

High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors’ information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.

Scalability of Nek5000 on High-Performance Computing Clusters Toward Direct Numerical Simulation of Molten Pool Convection

In a postulated severe accident, a molten pool with decay heat can form in the lower head of a reactor pressure vessel, threatening the vessel’s structural integrity. Natural convection in molten pools with extremely high Rayleigh (Ra) number is not yet fully understood as accurate simulation of the intense turbulence remains an outstanding challenge. Various models have been implemented in many studies, such as RANS (Reynolds-averaged Navier–Stokes), LES (large-eddy simulation), and DNS (direct numerical simulation). DNS can provide the most accurate results but at the expense of large computational resources. As the significant development of the HPC (high-performance computing) technology emerges, DNS becomes a more feasible method in molten pool simulations. Nek5000 is an open-source code for the simulation of incompressible flows, which is based on a high-order SEM (spectral element method) discretization strategy. Nek5000 has been performed on many supercomputing clusters, and the parallel performance of benchmarks can be useful for the estimation of computation budgets. In this work, we conducted scalability tests of Nek5000 on four different HPC clusters, namely, JUWELS (Atos Bullsquana X1000), Hawk (HPE Apollo 9000), ARCHER2 (HPE Cray EX), and Beskow (Cray XC40). The reference case is a DNS of molten pool convection in a hemispherical configuration with Ra = 1011, where the computational domain consisted of 391 million grid points. The objectives are (i) to determine if there is strong scalability of Nek5000 for the specific problem on the currently available systems and (ii) to explore the feasibility of obtaining DNS data for much higher Ra. We found super-linear speed-up up to 65536 MPI-rank on Hawk and ARCHER2 systems and around 8000 MPI-rank on JUWELS and Beskow systems. We achieved the best performance with the Hawk system with reasonably good results up to 131072 MPI-rank, which is attributed to the hypercube technique on its interconnection. Given the current HPC technology, it is feasible to obtain DNS data for Ra = 1012, but for cases higher than this, significant improvement in hardware and software HPC technology is necessary.