Search

Summary: We present a fully‐explicit, iteration‐free, weakly‐compressible method to simulate immiscible incompressible two‐phase flows. To update pressure, we circumvent the computationally expensive Poisson equation and use the general pressure equation which is solved explicitly. In addition, a less diffusive algebraic volume‐of‐fluid approach is used as the interface capturing technique and in order to facilitate improved parallel computing scalability, the technique is discretised temporally using the operator‐split methodology. Our method is fully‐explicit and stable with simple local spatial discretization, and hence, it is easy to implement. Several two‐ and three‐dimensional canonical two‐phase flows are simulated. The qualitative and quantitative results prove that our method is capable of accurately handling problems involving a range of density and viscosity ratios and surface tension effects. [ABSTRACT FROM AUTHOR]

Summary: This article introduces a new library that leverages and extends the MPI (message passing interface) standard, capable of aggregating servers and clusters located in multiple domains, as well as cloud resources. The execution platform is created and presented to the MPI application transparently, without the need of recompiling any code. The solution also provides functions for provisioning, adding, and removing nodes at runtime, bringing elasticity to the application. Through benchmarks, its performance was compared with the native execution of the application using the native MPI library. A prototype of an elastic application was also developed, with positive results within the expected range. [ABSTRACT FROM AUTHOR]

Brain‐inspired parallel computing is increasingly considered a solution to overcome memory bottlenecks, driven by the surge in data volume. Extensive research has focused on developing memristor arrays, energy‐efficient computing strategies, and varied operational mechanisms for synaptic devices to enable this. However, to realize truly biologically plausible neuromorphic computing, it is essential to consider temporal and spatial aspects of input signals, particularly for systems based on the leaky integrate‐and‐fire model. This review highlights the significance of neuromorphic computing and outlines the fundamental components of hardware‐based neural networks. Traditionally, neuromorphic computing has relied on two‐terminal devices such as artificial synapses. However, these suffer from significant drawbacks, such as current leakage and the lack of a third terminal for precise synaptic weight adjustment. As alternatives, three‐terminal synaptic devices, including memtransistors, ferroelectric, floating‐gate, and charge‐trapped synaptic devices, as well as optoelectronic options, are explored. For an accurate replication of biological neural networks, it is vital to integrate artificial neurons and synapses, implement neurobiological functions in hardware, and develop sensory neuromorphic computing systems. This study delves into the operational mechanisms of these artificial components and discusses the integration process necessary for realizing biologically plausible neuromorphic computing, paving the way for future brain‐inspired electronic systems. [ABSTRACT FROM AUTHOR]

The seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city‐scale earthquake‐induced elevator passenger entrapment. The model integrates big data and physics‐based approaches. A novel mapping method was developed to estimate city‐scale elevator traffic level based on population heatmap data and deep learning. A process‐based parallel computing scheme was designed to accelerate the assessment. The applicability was demonstrated based on a real‐world urban area comprising 619 buildings. The findings reveal that as the time of the earthquake varies, the risk exhibits significant fluctuations. Additionally, this study highlights that a simplistic correspondence between seismic intensity and passenger entrapment risk can lead to erroneous estimations. [ABSTRACT FROM AUTHOR]

The success of a nanopore experiment relies not only on the quality of the experimental design but also on the performance of the analysis program utilized to decipher the ionic perturbations necessary for understanding the fundamental molecular intricacies. An event extraction framework is developed that leverages parallel computing, efficient memory management, and vectorization, yielding significant performance enhancement. The newly developed abf‐ultra‐simple function extracts key parameters from the header critical for the operation of open‐seek‐read‐close data loading architecture running on multiple cores. This underpins the swift analysis of large files where an ≈ × 18 improvement is found for a 100 min‐long file (≈4.5 GB) compared to the more traditional single (cell) array data loading method. The application is benchmarked against five other analysis platforms showcasing significant performance enhancement (>2 ×–1120 ×). The integrated provisions for batch analysis enable concurrently analyzing multiple files (vital for high‐bandwidth experiments). Furthermore, the application is equipped with multi‐level data fitting based on abrupt changes in the event waveform. The application condenses the extracted events to a single binary file improving data portability (e.g., 16 GB file with 28 182 events reduces to 47.9 MB–343 × size reduction) and enables a multitude of post‐analysis extractions to be done efficiently. [ABSTRACT FROM AUTHOR]

Quantum‐mechanical calculations based on the density functional theory (DFT) enable an effective characterization of a variety of properties of materials at the atomistic level, although at a high computational cost. Here, we fully exploit parallel computing to apply such methods to the study of lattice dynamics and mechanical response of the major oxide constituents of Portland cement clinker: C3S${\rm {C}}_3{\rm {S}}$, C2S${\rm {C}}_{2}{\rm {S}}$, C3A${\rm {C}}_{3}{\rm {A}}$, and C4AF${\rm {C}}_4{\rm {AF}}$. Raman spectra and the evolution of the elastic tensor (and associated mechanical properties) with pressure are predicted for all oxide systems. We devote much attention to the assessment of dynamical and mechanical stability of the many previously proposed polymorphic forms of the most abundant oxide: C3S${\rm {C}}_3{\rm {S}}$. Specifically, five different crystalline models of C3S${\rm {C}}_3{\rm {S}}$ are analyzed: only two turn out to be dynamically stable. The mechanical response of C3S${\rm {C}}_3{\rm {S}}$ is further analyzed as a function of temperature through a quasi‐harmonic description of its lattice dynamics. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used in satellite remote sensing. However, satellites in orbit with limited resources and power consumption cannot meet the storage and computing power requirements of current million‐scale artificial intelligence models. This paper proposes a new generation of high flexibility and intelligent CNNs hardware accelerator for satellite remote sensing in order to make its computing carrier more lightweight and efficient. A data quantization scheme for INT16 or INT8 is designed based on the idea of dynamic fixed point numbers and is applied to different scenarios. The operation mode of the systolic array is divided into channel blocks, and the calculation method is optimized to increase the utilization of on‐chip computing resources and enhance the calculation efficiency. An RTL‐level CNNs field programable gate arrays accelerator with microinstruction sequence scheduling data flow is then designed. The hardware framework is built upon the Xilinx VC709. The results show that, under INT16 or INT8 precision, the system achieves remarkable throughput in most convolutional layers of the network, with an average performance of 153.14 giga operations per second (GOPS) or 301.52 GOPS, which is close to the system's peak performance, taking full advantage of the platform's parallel computing capabilities. [ABSTRACT FROM AUTHOR]

Summary: The Oak Ridge Leadership Computing Facility (OLCF) has been preparing the nation's first exascale system, Frontier, for production and end users. Frontier is based on HPE Cray's new EX architecture and Slingshot interconnect and features 74 cabinets of optimized 3rd Gen AMD EPYC CPUs for HPC and AI and AMD Instinct 250X accelerators. As a part of this preparation, "real‐world" user codes have been selected to help assess the functionality, performance, and usability of the system. This article describes early experiences using the system in collaboration with the Hamburg Observatory for two selected codes, which have since been adopted in the OLCF test harness. Experiences discussed include efforts to resolve performance variability and per‐cycle slowdowns. Results are shown for a performance portable astrophysical magnetohydronamics code, AthenaPK, and a mini‐application stressing the core functionality of a performance portable block‐structured adaptive mesh refinement framework, Parthenon‐Hydro. These results show good scaling characteristics to the full system. At the largest scale, the Parthenon‐Hydro miniapp reaches a total of 1.7×1013 zone‐cycles/s on 9216 nodes (73,728 logical GPUs) at ≈92% weak scaling parallel efficiency (starting from a single node using a second‐order, finite‐volume method). [ABSTRACT FROM AUTHOR]

Summary: We present a simple recurrence for the surface area of the (n,k)$$ \left(n,k\right) $$‐star graph, 0

Summary: Voronoi diagram is a versatile geometric structure used in solving many computational geometry problems in the field of image analysis, computer vision, engineering, medicine, molecular biology and various other disciplines. As a result it is a popular research domain. The literature boasts of many elegant and efficient methods for the construction of the Voronoi diagram in the real space. But these algorithms often fail to perform well when applied to the discrete domain. We present in this paper a novel algorithm for the construction of discrete Voronoi diagram using digital geometric concepts that takes care of the geometric perspective of the Voronoi diagram in the discrete domain. Although our technique is more suited for the CPU but by careful use of some cut‐off heuristics we are able to design a parallel programming approach for the construction of the Voronoi diagram making use of the GPU computational power. The speedup of our algorithm is compatible with the popular GPU technique for Voronoi diagram construction, namely, Jump flood algorithm. But since our algorithm uses the geometric perspective for the construction of Voronoi diagram it gives more accurate output and hence requires no correction step as is the case for the Jump flood algorithm. Furthermore, our approach focuses on extensibility, allowing for easy amenability for construction of higher dimensional Voronoi diagram. This ensures that the Voronoi diagram generation process can be adapted and expanded upon to meet specific requirements or incorporate future enhancements. We present an implementation of the proposed algorithm on a GPU using TAICHI language and describe its performance benefits on multiple input sites. [ABSTRACT FROM AUTHOR]

Heterogeneous platforms composed of multiple different types of computing devices (such as CPUs, GPUs, and Intel MICs) have been widely used recently. However, most of parallel applications developed in such a heterogeneous platform usually only utilize a certain kind of computing device due to the lack of easy‐to‐use heterogeneous cooperative parallel programming models. To reduce the difficulty of heterogeneous cooperative parallel programming, a directive‐based heterogeneous cooperative parallel programming framework called HeteroPP is proposed. HeteroPP provides an easier way for programmers to fully exploit multiple different types of computing devices to concurrently and cooperatively perform data‐parallel applications on heterogeneous platforms. An extension to OpenMP directives and clauses is proposed to make it possible for programmers to easily offload a data‐parallel compute kernel to multiple different types of computing devices. A source‐to‐source compiler is designed to help programmers to automatically generate multiple device‐specific compute kernels that can be concurrently and cooperatively performed on heterogeneous platforms. Many experiments are conducted with 12 typical data‐parallel applications implemented with HeteroPP on a heterogeneous CPU‐GPU‐MIC platform. The results show that HeteroPP not only greatly simplifies the heterogeneous cooperative parallel programming, but also can fully utilize the CPUs, GPU, and MIC to efficiently perform these applications. [ABSTRACT FROM AUTHOR]

Introducing soft materials with deformability and flexibility into mechanical switches may help to develop new types of soft mechanical switches with special properties such as reconfigurable characteristics and parallel computing. However, recently developed soft mechanical switches still have low on/off ratio, slow response time, and insufficient durability. Here, a soft mechanical switch based on liquid metal and a zigzag structure is constructed through strain engineering strategy with high on/off ratio (R/R0 > 107), short response time (~0.2 s), long‐term durability (> 2000 times), and self‐healing performance which is outstanding than the reported ones. With the switch, a mechanical unit is designed and all types of logic gates can be constructed by programming switches. Such multiple switches can also be used to perform parallel computing and achieve intelligent information processing. [ABSTRACT FROM AUTHOR]

Quantum computing shows promise for 6G networks due to its parallel computing capabilities. In the context of the Noisy Intermediate‐Scale Quantum era, the introduction of hybrid quantum‐classical algorithms like Quantum Approximate Optimization Algorithm (QAOA) offer powerful solutions to many combinatorial optimization problems in 6G. This paper focuses on Low‐Density Parity‐Check (LDPC) channel decoding and proposes an improved QAOA algorithm assisted by the learning‐to‐learn strategy. We also investigate the parameter concentration phenomenon in QAOA‐based LDPC decoding to assess the rationality. To evaluate effectiveness, a comprehensive numerical expression for the energy expectation of single‐layer QAOA and propose indicators for transfer performance evaluation is provided. Based on simulation results, the similarity in parameter distribution across specific LDPC configurations is investigated. This similarity facilitates the transfer of training outcomes from smaller to larger‐scale problems for optimization initialization, thereby avoiding the need for retraining. This approach offers insights and potential solutions for rapid, large‐scale channel decoding in 6G networks, despite the current limitations of quantum hardware. [ABSTRACT FROM AUTHOR]

We present updates to the Cray Graph Engine, a high performance in‐memory semantic graph database, which enable performant execution across multiple architectures as well as deployment in a container to support cloud and as‐a‐service graph analytics. This paper discusses the changes required to port and optimize CGE to target multiple architectures, including Cray Shasta systems, large shared‐memory machines such as SuperDome Flex (SDF), and cluster environments such as Apollo systems. The porting effort focused primarily on removing dependences on XPMEM and Cray PGAS and replacing these with a simplified PGAS library based upon POSIX shared memory and one‐sided MPI, while preserving the existing Coarray‐C++ CGE code base. We also discuss the containerization of CGE using Singularity and the techniques required to enable container performance matching native execution. We present early benchmarking results for running CGE on the SDF, Infiniband clusters and Slingshot interconnect‐based Shasta systems. [ABSTRACT FROM AUTHOR]

Summary: We are concerned with the mapping on high performance hybrid architectures of a parallel software implementing a two level overlapping domain decomposition, that is, along space and time directions, of the four dimensional variational data assimilation model. The reference architecture belongs to the SCoPE (Sistema Cooperativo Per Elaborazioni scientifiche multidisciplinari) data center, located at University of Naples Federico II. We consider the initial boundary problem of the shallow water equation and analyse both strong and weak scaling. Keeping the efficiency always greater than 60%$$ 60\% $$ and about 90%$$ 90\% $$ in most cases, we experimentally find that the isoefficiency function grows a little more than linearly with respect to the number of processes. Results, obtained by using the parallel computing toolbox of MATLABR2013a, are in agreement with the algorithm's performance prevision based on the scale up factor, confirming the appropriate mapping of the algorithm on the hybrid architecture. [ABSTRACT FROM AUTHOR]

This paper introduces a modified sequential version method for optimizing a linear function over an integer efficient set, as well as a new exact parallel algorithm. The performance of parallel programming in this context is clear and shown through different instances with different sizes. Each procedure builds a finite monotonous sequence of values for the main criterion to be optimized, in a reasonable amount of CPU execution time. This latter remains much better. For the first time, the Algerian IBNBADIS cluster—CERIST—was used with this type of problem. Significant results are obtained by both proposed techniques, particularly with the parallel one. [ABSTRACT FROM AUTHOR]

The hierarchical finite element method (FEM)–discrete element method (DEM) multiscale approach is a powerful tool for solving geotechnical boundary value problems. However, despite parallel computing can be resorted to, the high computational cost remains an insurmountable barrier to its practical application in engineering‐scale problems. As an alternative, a deep learning model (DLM) is employed to replace the representative volume element (RVE) in the DEM. The complex constitutive responses of sand under various loading paths can be reproduced by leveraging the powerful learning capacity of the DLM. During the numerical computations, the DLM is integrated into the Gauss integration points of the FEM mesh, where it receives strains as input and returns the prediction of stresses to advance the calculation. The applicability and accuracy of the approach were examined with three BVPs, in which the sources of error for both global and local performances were systematically analyzed. The feasibility of the approach in accounting for the inherent anisotropy of sand was also investigated. The results demonstrated that the incorporation of the DLM can achieve a significant computational acceleration of up to two orders of magnitude while maintaining a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has witnessed a growing integration into numerous domains of production and daily life. Such powerful AI has emerged from electronic simulations of human neurons. In the post‐Moore era, Von‐Neumann architecture and high energy consumption have posed challenges for the advancement of electronic AI. In light of these limitations, a new concept of photonic meta‐neurons is proposed, which holds the potential to address these issues by optically emulating biological neurons through the utilization of metasurfaces. Meta‐neurons could enable flexible modulation of photonic signals through hierarchical and high‐dimensional manipulation of light fields. The flat and thin design of the meta‐neuron reduces the spatial constraints. In comparison to electronic AI, meta‐neurons offer a range of desirable attributes, including the potential for achieving light‐speed processing, parallel computing, clean energy utilization, high energy efficiency, and an in‐memory computing architecture. The meta‐neuron concept can serve as a promising starting point for photonic AI. With its advantages of high‐speed computing and environmental friendliness, meta‐neurons will have broad applications in various fields, particularly for latency‐critical tasks. [ABSTRACT FROM AUTHOR]

Coupled simulations of surface and variably saturated subsurface flow, termed integrated hydrologic models (IHMs), can provide powerful insights into the complex dynamics of watersheds. The system of governing equations solved by an IHM is non‐linear, making them a significant computational burden and challenging to accurately parameterize. Consequently, a large fraction of the IHM studies to date have been "numerical hypothesis testing" studies, but, as parallel computing continues to improve, IHMs are approaching the point where they might also be useful as predictive tools. For this to become reality, the predictive uncertainty of such highly parameterized simulations must be considered. However, uncertainty is seldom considered in the IHM literature, likely due to the long runtimes of the complex simulations. The questions considered herein are how much uncertainty is there in an IHM for a common watershed simulation scenario, and how likely is it that any one realization of a system will give the same relative change as any other due to a perturbation in recharge? A stochastic ensemble of 250 permeability field realizations was used to show that uncertainty in a high‐mountain headwaters systems is dominated by the subsurface. Recharge perturbation scenarios echo these results, but the uncertainty of changes in streamflow or groundwater pressure heads were significantly smaller than the uncertainty in their base‐case values. The main finding is that IHMs do provide confident, predictive estimates of relative changes in watersheds, even when uncertainty in specific simulation outputs may be high. Plain Language Summary: Watershed simulations that consider stream‐groundwater interactions require the specification of lots of parameters to define the model but there is often little data to base those parameters on. Consequently, simulation results have uncertainty and if it is not quantified then any predictions made by these models have no "plus‐or‐minus" factors, giving an illusion of high confidence. The catch is that quantifying uncertainty is computationally expensive, which is why it is seldom done, yet at some point the uncertainty must be explored if these models are to be used for predictions. The work in this paper assesses the variability of predictions for a "typical" watershed simulation under three different rainfall scenarios using an ensemble of 250 random versions of the system. The results show that groundwater has higher variability than surface flows, meaning the latter is simulated with higher confidence. However, an important finding is that the relative changes due to the changes in the rainfall were generally consistent across the ensemble. The main point is that even with limited confidence in what an output will be, the relative change of that output (e.g., a 10% increase in recharge increases streamflow by 10%) can likely be inferred with much greater confidence. Key Points: Ensemble simulations of coupled surface‐subsurface flows in a high mountain watershed were performed using the numerical model ParFlowUncertainty throughout the simulation domain was significant and was dominated by the subsurface domainRelative changes in the surface‐ and groundwater values due to a perturbation had smaller confidence intervals than the base‐case values [ABSTRACT FROM AUTHOR]

Summary: The development of computational fluid dynamics (CFD) highly depends on high‐performance computers. Computer hardware has evolved rapidly, yet scalable CFD parallel software remains scarce. In this article, we design a highly scalable CFD parallel paradigm for both homogeneous and heterogeneous supercomputers. The paradigm achieves the separation of communication and computation and automatically adapts to various solvers and hardware environments, thus reducing programming difficulties and increasing automatic parallelization. Meanwhile, the number of communications is greatly reduced and the scalability of the program is improved through implementing centralized communication and two‐level partitioning techniques. Complex flow problems for real aircraft were then computed on different hardware platforms with a grid size of ten billion. The homogeneous computer hardware includes Intel Xeon Gold 6258R and Phytium 2000+ processors, and the heterogeneous computer platforms include NVIDIA Tesla V100 and SW26010 processors. High parallel efficiency was obtained on all computer platforms, verifying that the paradigm has good automatic parallelization, scalability, and stability. The paradigm in this article has an important reference value for CFD massively parallel computing and can promote the development and application of CFD technology. [ABSTRACT FROM AUTHOR]

Analog and digital switching performances in a Ta/HfO2/RuO2 (THR) memristor are studied to implement a density‐based spatial clustering of applications with noise (DBSCAN) algorithm in a low‐power, parallel‐computing memristor crossbar structure. In the analog mode THR memristor, more than 256 states can be stored through a fine‐tuning process with a denoising scheme. The analog mode crossbar array facilitates Euclidean distance calculation between any points in the given graphic dataset. In the digital mode, the on/off ratio of more than three orders of magnitude between the binary states is achieved, providing functionality to cluster the data points with a reduced number of operations. The parallel computing capacity of the adopted crossbar decreases the time complexity of the original DBSCAN from O(n2) to O(n). Through array‐level simulations, the effectiveness of hardware functionality is validated using representative synthetic datasets and single‐cell RNA sequences datasets. [ABSTRACT FROM AUTHOR]

The real‐time tracking process of dim targets in space is mainly achieved through the correlation and prediction of dots after the detection and calculation process. The on‐board calculation of the tracking needs to be completed in milliseconds, and it needs to reach the microsecond level at high frame rates. For real‐time tracking of dim targets in space, it is necessary to achieve universal tracking calculation acceleration in response to different space regions and complex backgrounds, which poses high requirements for engineering implementation architecture. This paper designs a Kalman filter calculation based on digital logic parallel acceleration architecture for real‐time solution of dim target tracking on‐board. A unified architecture of Vector Processing Element (VPE) was established for the calculation of Kalman filtering matrix, and an array computing structure based on VPE was designed to decompose the entire filtering process and form a parallel pipelined data stream. The prediction errors under different fixed point bit widths were analyzed and deduced, and the guidance methods for selecting the optimal bit width based on the statistical results were provided. The entire design was engineered based on Xilinx's XC7K325T, resulting in an energy efficiency improvement compared to previous designs. The single iteration calculation time does not exceed 0.7 microseconds, which can meet the current high frame rate target tracking requirements. The effectiveness of this design has been verified through simulation of random trajectory data, which is consistent with the theoretical calculation error. [ABSTRACT FROM AUTHOR]

Summary: The efficiency of metaheuristic algorithms depends significantly on the number of fitness value evaluations performed on candidate solutions. In addition to various intelligent techniques used to obtain better results, parallelization of calculations can substantially improve the solutions in cases where the problem is NP‐hard and requires many evaluations. This study proposes a new parallel tabu search method for solving the Maximum Vertex Weight Clique Problem (MVWCP) on the Non‐Uniform Memory Access (NUMA) architectures using the OpenMP parallel programming paradigm. Achieving scalability in the NUMA architectures presents significant challenges due to the high complexity of their memory systems, which can lead to performance loss. However, our proposed Tabu‐NUMA algorithm provides up to 18×$$ 18\times $$ speed‐up with 64 cores for ten basic problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. And it improves the performance of the serial Multi Neighborhood Tabu Search (MN/TS) algorithm for 38 problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. We further evaluate our algorithm on larger datasets with thousands of edges and vertices from Network Data Repository benchmark problem instances, and we report significant improvements in terms of speed up. Our results confirm that the Tabu‐NUMA algorithm is among the best recent algorithms for solving MVWCP on the NUMA architectures. [ABSTRACT FROM AUTHOR]

Reconfigurable logic‐in‐memory device, albeit a promising hardware platform for constructing parallel computing architectures, still suffers from high power‐consumption and low area‐efficiency arising from the circuit redundancy in silicon (Si) based technical path. 2D materials are identified as potential building blocks to significantly reduce the circuit footprints with outstanding power‐efficiency, owing to their atomically thin nature and unique electronic properties. However, the present 2D logic devices are primarily based on single channel materials with homogeneous transport polarities, limiting the device reconfigurability for multi‐functional applications. Here, 2D p–n junction based dual‐gate Gaussian‐type transistors for simplified reconfigurable logic circuits are reported. All fundamental Boolean logic operators are implemented in a single device with electrically driven reconfigurability, giving rise to an ultra‐low transistor consumption down to 13% of the traditional circuits. The device is also demonstrated as a reliable image processing unit for various computing tasks (pixel processing, comparing, and ciphering) by executing the corresponding logic operations. Moreover, in situ memory of the Boolean logics is achieved by engineering the dielectric properties of the transistor without compromising its reconfigurability. These findings provide a potential approach to achieving reconfigurable logic‐in‐memory operations at the hardware level, with significant implications for the advancement of parallel computing. [ABSTRACT FROM AUTHOR]

Summary: This article evaluates the current support for heterogeneous OpenMP 5.2 applications regarding the simultaneous activation of host and device computing units (e.g., CPUs, GPUs, or FPGAs). The article identifies limitations in the current OpenMP specification and describes the design and implementation of novel OpenMP extensions and runtime support for heterogeneous parallel programming. The Compute Unit (CUs) abstraction is introduced in the OpenMP programming model. The Compute Unit abstraction is defined in terms of an aggregation of computing elements (e.g., CPUs, GPUs, FPGAs). On top of CUs, the article describes dynamic work sharing constructs and schedulers that address the inherent differences in compute power of host and device CUs. New constructs and the corresponding runtime support are described for the new abstractions. The article evaluates the case of a hybrid multilevel parallelization of the NPB‐MZ benchmark suite. The implementation exploits both coarse‐grain and fine‐grain parallelism, mapped to CUs of different nature (GPUs and CPUs). All CUs are activated using the new extensions and runtime support. We compare hybrid and nonhybrid executions under two state‐of‐the‐art work‐distribution schemes (Static and Dynamic Task schedulers). On a computing node composed of one AMD EPYC 7742 @ 2.250GHz (64 cores and 2 threads/core, totalling 128 threads per node) and 2×$$ \times $$ GPU AMD Radeon Instinct MI50 with 32GB, hybrid executions present speedups from 1.08×$$ \times $$ up to 3.18×$$ \times $$ with respect to a nonhybrid GPU implementation, depending on the number of activated CUs. [ABSTRACT FROM AUTHOR]

SpMV (Sparse matrix‐vector multiplication) is an important computing core in traditional high‐performance computing and also one of the emerging data‐intensive applications. For diagonal sparse matrices, it is frequently necessary to fill in a large number of zeros to maintain the diagonal structure as for using DIA (Diagonal) storage format. The fact that filling with zeros may consume additional computing and memory resources, will certainly lead to degradation of the parallel computing performance of SpMV, further causing computing and storage redundancy. To solve the deficiencies of the DIA format, a Two‐stage parallel SpMV method is presented in this paper, which can reasonably distribute the data of diagonal matrix and irregular matrix to different CUDA kernels. As different corresponding compression methods are particularly designed for different matrix forms, a partition‐based hybrid format of DIA and CSR (HPDC) is therefore adopted in the two‐stage method to ensure load balancing among computing resources and continuity of data access on the diagonal. Simultaneously, a standard deviation among blocks is used as a criterion to obtain the optimal number of blocks and distribution of data. The experimental data were implemented in the Florida data set. Compared to DIA, cuSPARSE‐CSR, HDC, and BRCSD, the execution time of the Two‐stage method is shortened by 4×$$ \times $$, 3.4×$$ \times $$, 1.9×$$ \times $$, and 1.15×$$ \times $$, respectively. [ABSTRACT FROM AUTHOR]

As a key component of high‐performance switches and routers, the packet forwarding engine (PFE) is mainly responsible for selecting the appropriate output port for tens of thousands of packets within an extremely short time frame. However, the performance of PFE is determined by the selected group membership algorithm. This paper puts forth a hybrid strategy–caching scalar‐pair and vectors routing and forwarding (CSVRF), consisting of virtual output port bitmap caching (VOPBC) and fractional‐N SVRF to address major multicast forwarding issues such as scalability by using content addressable memory. In CSVRF, a virtual output port bitmap cache is introduced, which includes the most popular combinations of output port bitmap and divides the big scalar‐pair into N sub‐groups to achieve parallel compute and the reusability of less bit‐length prime. The results demonstrate that the memory space and the forwarding latency are effectively reduced compared with previous work. In space efficiency, it only required 10% memory space compared with the original SVRF/fractional‐N SVRF, decreased 10% memory usage compared with pure VOPBC and nearly improved 1 to 4 orders of magnitude of packet processing time compared with the original SVRF and the fractional‐N SVRF respectively. [ABSTRACT FROM AUTHOR]

Summary: The wide adoption of SYCL as an open‐standard API for accelerating C++ software in domains such as HPC, automotive, artificial intelligence, machine learning, and other areas necessitates efficient compiler and runtime support for a growing number of different platforms. Existing SYCL implementations provide support for various devices like CPUs, GPUs, DSPs, FPGAs and so forth, typically via OpenCL or CUDA backends. While accelerators have increased the performance of user applications significantly, employing CPU devices for further performance improvement is beneficial due to the significant presence of CPUs in existing data‐centers. SYCL applications on CPUs, currently go through an OpenCL backend. Though an OpenCL backend is valuable in supporting accelerators, it may introduce additional overhead for CPUs since the host and device are the same. Overheads like a run‐time compilation of the kernel, transferring of input/output memory to/from the OpenCL device, invoking the OpenCL kernel and so forth, may not be necessary when running on the CPU. While some of these overheads (such as data transfer) can be avoided by modifying the application, it can introduce disparity in the SYCL application's ability to achieve performance portability on other devices. In this article, we propose an alternate approach to running SYCL applications on CPUs. We bypass OpenCL and use a CPU‐directed compilation flow, along with the integration of whole function vectorization to generate optimized host and device code together in the same translation unit. We compare the performance of our approach—the CPU‐directed compilation flow, with an OpenCL backend for existing SYCL‐based applications, with no code modification for BabelStream benchmark, Matmul from the ComputeCpp SDK, N‐body simulation benchmarks and SYCL‐BLAS (Aliaga et al. Proceedings of the 5th International Workshop on OpenCL; 2017.), on CPUs from different vendors and architectures. We report a performance improvement of up to 72%$$ 72\% $$ on BabelStream benchmarks, up to 63%$$ 63\% $$ on Matmul, up to 21%$$ 21\% $$ on the N‐body simulation benchmark and up to 16% on SYCL‐BLAS. [ABSTRACT FROM AUTHOR]

Optimal control musculoskeletal simulation is a valuable approach for studying fundamental and clinical aspects of human movement. However, the high computational demand has long presented a substantial challenge, creating a need to improve simulation performance. The OpenSim Moco software package permits musculoskeletal simulation problems to be solved in parallel on multicore processors using the CasADi optimal control library, potentially reducing the computational demand. However, the computational performance of this framework has not been thoroughly examined. Thus, we aimed to investigate the computational speed‐up obtained via multicore parallel computing relative to solving problems serially (i.e., using a single core) in optimal control simulations of human movement in OpenSim Moco. Simulations were solved using up to 18 cores with a variety of temporal mesh interval densities and using two different initial guess strategies. We examined a range of musculoskeletal models and movements that included two‐ and three‐dimensional models, tracking and predictive simulations, and walking and reaching tasks. The maximum overall parallel speed‐up was problem specific and ranged from 1.7 to 7.7 times faster than serial, with most of the speed‐up achieved by about 6 processor cores. Parallel speed‐up was generally greater on finer temporal meshes, while the initial guess strategy had minimal impact on speed‐up. Considerable speed‐up can be achieved for some optimal control simulation problems in OpenSim Moco by leveraging the multicore processors often available in modern computers. However, since improvements are problem specific, achieving optimal computational performance will require some degree of exploration by the end user. [ABSTRACT FROM AUTHOR]

Although atomicity plays a key role in data operations of shared variables in parallel computation, researchers haven't treated atomicity in Python in much detail. This study provides a novel approach to integrate the CPU‐based atomic C APIs into Python shared variables by C Foreign Function Interface for Python (CFFI) on all major platforms and utilises Cython to optimise calculation in CPython. Evidence shows that the resulting product, Shared Atomic Enterprise (SAE), could accelerate data operations on shared data types to a large extent. These findings provide a solid evidence base for the massive utilisation of Python atomic operations in parallel computation and concurrent programming. [ABSTRACT FROM AUTHOR]

Summary: In the article, we have proposed a framework that allows programming a parallel application for a multi‐node system, with one or more graphical processing units (GPUs) per node, using an OpenMP+extended CUDA API. OpenMP is used for launching threads responsible for management of particular GPUs and extended CUDA calls allow to transfer data and launch kernels on local and remote GPUs. The framework hides inter‐node MPI communication from the programmer. For optimization, the implementation takes advantage of the MPI_THREAD_MULTIPLE mode allowing: multiple threads handling distinct GPUs as well as overlapping communication and computations transparently using multiple CUDA streams. The solution allows data parallelization across available GPUs in order to minimize execution time and supports a power‐aware mode in which GPUs are automatically selected for computations using a greedy approach in order not to exceed an imposed power limit. We have implemented and benchmarked three parallel applications including: finding the largest divisors; verification of the Collatz conjecture; finding patterns in vectors. These were tested on three various systems: a GPU cluster with 16 nodes, each with NVIDIA GTX 1060 GPU; a powerful 2‐node system—one node with 8×$$ \times $$ NVIDIA Quadro RTX 6000 GPUs, the second with 4×$$ \times $$ NVIDIA Quadro RTX 5000 GPUs; a heterogeneous environment with one node with 2×$$ \times $$ NVIDIA RTX 2080 and 2 nodes with NVIDIA GTX 1060 GPUs. We demonstrated effectiveness of the framework through execution times versus power caps within ranges of 100–1400 W, 250–3000 W, and 125–600 W for these systems respectively as well as gains from using two versus one CUDA streams per GPU. Finally, we have shown that for the testbed applications the solution allows to obtain high speed‐ups between 89.3% and 97.4% of the theoretically assessed ideal ones, for 16 nodes and 2 CUDA streams, demonstrating very good parallel efficiency. [ABSTRACT FROM AUTHOR]

Summary: While heterogeneous computing has emerged as a dominant trend in current and future High‐Performance Computing (HPC) systems, it is also widely recognized that this shift has led to increased software complexity due to a proliferation of programming systems for different heterogeneous processors. One such example is the Heterogeneous‐Compute Interface for Portability from AMD (HIP), which is composed of a C Runtime API and C++ Kernel Language. Many HPC applications will likely use HIP on future exascale systems (e.g., Frontier and El Capitan), but HIP currently only targets AMD and NVIDIA processors. This limitation creates challenges for users who would also like to run their applications on exascale systems based on other architectures (e.g., Aurora, which is based on Intel hardware) that are currently not targeted by HIP. In this paper, we introduce the design and implementation of HIPLZ , a compiler and runtime system that uses the Intel Level Zero API to support HIP on Intel GPU architectures. We discuss the design of HIPLZ , derived from HIPCL (an implementation of HIP on top of OpenCL), and portability issues that occur from using the Level Zero runtime as a backend. We evaluate our implementation by running several performance benchmarks and mini‐apps written in HIP on Intel architectures using HIPLZ. Our results show that this approach provides competitive performance relative to Intel's OpenCL implementations on Intel Gen9 and UHD Graphics 770 GPUs, while providing good coverage of features needed by HPC applications. Overall, this approach is a promising demonstration of enabling performance portability for exascale systems. [ABSTRACT FROM AUTHOR]

Summary: Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present a novel graph transformation strategy to increase the parallelism degree of a sparse matrix and compare it to our previous strategy. It is seen that our transformation strategy can provide a speedup as high as 1.42x$$ 1.42x $$. [ABSTRACT FROM AUTHOR]

A three‐way coupled thermo‐mechanical fracture model is presented to predict the damage of brittle ceramics, in particular α‐SiC, over a wide range of temperatures (20–1400°C). Predicting damage over such a range of temperatures is crucial for thermal protection systems for many systems such as spacecraft. The model, which has been implemented in MOOSE, is divided into three modules: elasticity, damage phase field, and heat conduction. Analytical approaches for determining crack lengthscales are presented for both simple tension and simple shear. Validation tests are conducted for both flexural strength and fracture toughness over the specified range of temperatures. Flexural strength simulation results fall within the uncertainty region of the experimental data, and mode I fracture toughness simulation results are also in agreement with the experimental data. Mode II and mixed mode fracture toughness simulations results are presented with the modified G‐criterion. Finally, the parallel computing capabilities of the model are considered in various scalability tests. [ABSTRACT FROM AUTHOR]

In the seismic wave exploration field, first break picking is an important underlying task. With the emergence of massive seismic data, it is urgent to develop automatic and accurate picking algorithms to relieve the huge workload of manual picking. Although many traditional and machine learning based techniques have been developed, it is still a challenging task to obtain satisfactory picking results in practice because of complex subsurface structures and low signal‐to‐noise ratio. Ensemble learning has been demonstrated to be a powerful tool to produce good prediction results in many fields. Its core idea is to create multiple models with some special techniques and then to combine the prediction results of each model with a fusion strategy. Structured random forests , a type of ensemble learning method, have been proven to be quite effective in implementing structured learning tasks. Based on the observation that adjacent traces can provide useful information to pick the first arrival time for a specific trace, we propose in this paper a novel structured random forest based first break picking framework. In particular, a multi‐scale normalization technique is presented to make full use of the amplitude information at different scales. To capture local features of first breaks, an enhanced feature map based on the short‐term/long‐term average ratio method is also computed. By extracting patches from two channel feature maps, we construct a structured random forest to predict the locations of the first breaks. On the basis of the probability score map produced by the structured random forest, an effective post‐processing strategy is proposed to further deal with the detected results. By conducting experiments with synthetic and field data, the proposed method is shown to be effective in identifying first breaks. It is significantly superior to the short‐term/long‐term average ratio method and support vector machine in terms of picking accuracy. With the advantage of parallel computing, the computational cost of structured random forest is acceptable, that is, it is much lower than the support vector machine while being higher than the short‐term/long‐term average ratio method. In addition, the experiments also confirm that the multi‐scale normalization plays an important role in improving the picking performance of the structured random forest. [ABSTRACT FROM AUTHOR]

Biodiversity has typically been quantified using species richness, but this ignores evolutionary history. Due to the increasing availability of robust phylogenies, methods have been developed that incorporate phylogenetic relationships into quantification of biodiversity. CANAPE (categorical analysis of neo‐ and paleo‐endemism) is one such method that can provide insight into the evolutionary processes generating biodiversity. The only currently available software implementing CANAPE is Biodiverse, which is written in Perl and can be used either through a graphical user interface (GUI) or user‐developed scripts. However, many researchers, particularly in the fields of ecology and evolutionary biology, use the R programming language to conduct their analyses. Here, we present canaper, a new R package (www.r‐project.org) that provides functions to conduct CANAPE in R. canaper implements methods for efficient computation, including parallelization and encoding of community data as sparse matrices. The interface is designed for maximum simplicity and reproducibility; CANAPE can be conducted with two functions, and parallel computing can be enabled with one line of code. Our case study shows that canaper produces equivalent results to Biodiverse and can complete computations on moderately sized datasets quickly (<10 min to reproduce a canonical study). canaper allows researchers to conduct all analyses from data import and cleaning through CANAPE within R, thereby obviating the need to manually import and export data and analysis results between programs. We anticipate canaper will become a part of the toolkit for analyzing biodiversity in R. [ABSTRACT FROM AUTHOR]

Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual‐level data. A variety of implementations exist for such models, including integrated likelihood, parameter‐expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species. [ABSTRACT FROM AUTHOR]

Modern diffraction experiments (e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies (i.e. the parameter turn‐on‐off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi‐histogram datasets such as from multi‐bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram‐specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction (MAUD), the MAUD Interface Language Kit (MILK) is developed along with an updated text batch interface for MAUD. The open‐source software MILK is computer‐platform independent and is packaged as a Python library that interfaces with MAUD. Using MILK, model selection (e.g. various texture or peak‐broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high‐level Python interface. A high‐level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD. Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer, a tool for visualizing and inspecting the results of multi‐parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer. [ABSTRACT FROM AUTHOR]

Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process. [ABSTRACT FROM AUTHOR]

Anomaly detection of sequences is a hot topic in data mining. Anomaly detection using piecewise aggregate approximation in the amplitude domain (called ADPAAD) is one of the widely used methods in anomaly detection of sequences. The core step in the classical algorithm for performing ADPAAD is to construct an approximate representation of the subsequence, where the elements of each subsequence are divided into several subsections according to the amplitude domain, and then the average of the subsections is computed. It is computationally expensive when processing large‐scale sequences. In this paper, a quantum algorithm for ADPAAD is proposed, which can divide the subsequence elements and compute the average in parallel. The quantum algorithm can achieve polynomial speedups on the number of subsequences and the length of subsequences over its classical counterpart. [ABSTRACT FROM AUTHOR]

Lightweight manycore processors deliver high performance and energy efficiency by bundling hundreds of low‐power cores, a distributed memory architecture with small local memories and Networks‐on‐Chip in a single die. However, the lack of rich and portable programming models for these processors makes software development a challenging task. Currently, two approaches are employed to address programmability in lightweight manycores: Operating Systems (OSes) and baremetal runtime libraries. The former provides portability but exposes complex Operating System (OS)‐level programming interfaces to developers. The latter focuses on providing rich and high performance interfaces, which are vendor‐specific and yield to non‐portable software. In this work, we address these programmability and portability challenges by combining a rich OS with a well‐known standard for parallel programming. We propose a portable and lightweight Message Passing Interface (MPI) library (LWMPI) designed from scratch to cope with restrictions and intricacies of lightweight manycores. We integrated LWMPI into Nanvix, an open‐source distributed OS that runs on silicon lightweight manycores. The results obtained with a synthetic benchmark and a subset of the CAP Bench applications running on Kalray MPPA‐256 unveil that LWMPI not only delivers a lightweight and richer programming interface but also presents good performance and scalability results. [ABSTRACT FROM AUTHOR]

Inverse eigenstrain (inherent strain) analysis methods are shown to be effective for the reconstruction of residual stresses in plane eigenstrain problems (continuously processed bodies) while conversely residual stress reconstruction in discontinuously processed bodies is extremely challenging and necessitates the use of complex regularizing assumptions. Herein, a new generic inverse eigenstrain method suitable for the reconstruction of residual stresses along with residual elastic strains and displacements in discontinuously processed bodies is introduced. The proposed method uses the superposition of eigenstrain radial basis functions together with a set of limited experimental data for model‐free (unconstrained) determination of unknown eigenstrain fields. This approach eliminates the limitations introduced by global basis functions such as polynomials. The novel point of this method is the ability to account for all six components of strain in an isotropic body without using regularizing assumptions. By lifting complex guiding formulation, the fidelity of full‐field eigenstrain reconstruction becomes directly related to the quality of experimental data and proper discretisation of the model domain. The FEniCS implementation has been validated using the experimental data of pointwise high‐energy synchrotron X‐ray diffraction measurements from a bent titanium alloy bar. A hybrid high throughput computing approach is also introduced for effective parallel computing. [ABSTRACT FROM AUTHOR]

Large‐scale computing and data processing with cloud resources is gaining popularity. However, the usage of the cloud differs from traditional high‐performance computing (HPC) systems and both algorithms and codes have to be adjusted. This work is often time‐consuming and performance is not guaranteed. To address this problem we have developed the PCJ library (parallel computing in Java), a novel tool for scalable HPC and big data processing in Java. In this article, we present a performance evaluation of parallel applications implemented in Java using the PCJ library. The performance evaluation is based on the examples of highly scalable applications of different characteristics focusing on CPU, communication or I/O. They run on the traditional HPC system and Amazon web services Cloud as well as Linaro Developer Cloud. For the clouds, we have used Intel x86 and ARM processors for running Java codes without changing any line of the program code and without the need for time‐consuming recompilation. Presented applications have been parallelized using the partitioned global address space programming model and its realization in the PCJ library. Our results prove that the PCJ library, due to its performance and ability to create simple portable code, has great promise to be successful for the parallelization of various applications and run them on the cloud with a performance close to HPC systems. [ABSTRACT FROM AUTHOR]

Certain workloads such as in‐memory databases are inherently hard to scale‐out and rely on cache‐coherent scale‐up non‐uniform memory access (NUMA) systems to keep up with the ever‐increasing demand for compute resources. However, many parallel programming frameworks such as OpenMP do not make efficient use of large scale‐up NUMA systems as they do not consider data locality sufficiently. In this work, we present PGASUS, a C++ framework for NUMA‐aware application development that provides integrated facilities for NUMA‐aware task parallelism and data placement. The framework is based on an extensive review of parallel programming languages and frameworks to incorporate the best practices of the field. In a comprehensive evaluation, we demonstrate that PGASUS provides average performance improvements of 1.56× and peak performance improvements of up to 4.67×across a wide range of workloads. [ABSTRACT FROM AUTHOR]

Understanding the nexus between food, energy, and water systems (FEW) is critical for basins with intensive agricultural water use as they face significant challenges under changing climate and regional development. We investigate the food, energy, and water nexus through a regional hydroeconomic optimization (RHEO) modeling framework. The crop production in RHEO is estimated through a hierarchical regression model developed using a biophysical model, AquaCropOS, forced with daily climatic inputs. Incorporating the hierarchical model within the RHEO also reduces the computation time by enabling parallel programming within the AquaCropOS and facilitates mixed irrigation—rainfed, fully irrigated and deficit irrigation—strategies. To demonstrate the RHEO framework, we considered a groundwater‐dominated basin, South Flint River Basin, Georgia, for developing mixed irrigation strategies over 31 years. Our analyses show that optimal deficit irrigation is economically better than full irrigation, which increases the groundwater pumping cost. Thus, considering deficit irrigation in a groundwater‐dominated basin reduces the water, carbon, and energy footprints, thereby reducing FEW vulnerability. The RHEO also could be employed for analyzing FEW nexus under potential climate change and future regional development scenarios. Key Points: A new regional hydroeconomic optimization modeling framework is proposed for mixed irrigation regimes using AquaCropOSOptimal deficit irrigation strategy is economically better than full irrigation by reducing the cost and energy for groundwater pumpingCrop simulation module and optimization module are linked using Bayesian Hierarchical Model to reduce computation time [ABSTRACT FROM AUTHOR]

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single‐cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in‐depth and high‐quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well‐performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights. [ABSTRACT FROM AUTHOR]

Summary: The Floyd‐Warshall algorithm is a well‐known algorithm to compute the distance of all pairs of nodes of a graph. The Blocked Floyd‐Warshall algorithm, a variant of the Floyd‐Warshall has been proposed to accelerate the Floyd‐Warshall algorithm by means of a graphics processing unit (GPU) architecture. The previously published GPU implementations for the Blocked Floyd‐Warshall algorithm perform many separated kernel calls for costly barrier synchronization. The main contribution of this article is to present efficient implementations of the Blocked Floyd‐Warshall algorithm, which performs no barrier synchronization and invokes only one kernel call. Experimental results using NVIDIA Tesla V100 show that our implementation runs 1.05‐1.31 times faster than the previously published one. Our implementation with SIMD functions also runs 1.00‐1.28 times faster than it. Second, we propose efficient GPU implementations to execute the Blocked Floyd‐Warshall algorithm for many graphs at the same time. From the experimental results, our single kernel implementation runs 1.03‐1.60 times faster than multiple kernel one. In terms of implementations with SIMD functions, our single kernel implementation runs 1.01‐1.89 times faster than it. We also propose the low‐latency implementations for many graphs. Finally, we implemented the parallel Floyd‐Warshall algorithm on the multicore processors. [ABSTRACT FROM AUTHOR]

Background: Voxel-Based (VB) analysis embraces a multifaceted ensemble of sophisticated techniques, lying at the boundary between image processing and statistical modeling, that allow for a frequentist inference of pathophysiological properties anchored to an anatomical reference. VB methods has been widely adopted in neuroimaging studies and, more recently, they are gaining momentum in radiation oncology research. However, the price for the power of VB analysis is the complexity of the underlying mathematics and algorithms. Purpose: In this paper, we present the Multi-pAradigM voxel-Based Analysis (MAMBA) toolbox, which is intended for a flexible application of VB analysis in a wide variety of scenarios in medical imaging and radiation oncology. Methods: The MAMBA toolbox is implemented in Matlab. It provides opensource functions to compute VB statistical models of the input data,according to a great variety of regression schemes, and to derive VB maps of the observed significance level, performing a non-parametric permutation inference. The toolbox allows for including VB and global outcomes, as well as an arbitrary amount of VB and global Explanatory Variables (EVs). In addition, the Matlab Parallel Computing Toolbox is exploited to take advantage of the perfect parallelizability of most workloads. Results: The use of MAMBA was demonstrated by means of several realistic examples on a synthetic dataset mimicking a radiation oncology scenario. Conclusion: MAMBA is an open-source toolbox, freely available for academic and non-commercial purposes. It is designed to make state-of -the-art VB analysis accessible to research scientists without the programming resources needed to build from scratch their own software solutions. At the same time, the source code is handed out for more experienced users to complement their own tools, also customizing user-defined models. MAMBA guarantees high generality and flexibility in the design of the statistical models, significantly expanding on the features of available free tools for VB analysis. The presented toolbox aims at increasing the reach of VB studies as well as the sharing of research results. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) is the most advanced construction method that involves off‐site manufacturing, factory‐to‐site transportation, and on‐site assembly of free‐standing integrated modules. Despite the growing interest in the manufacturability of MiC, little research is available on the passing ability issues where efficient and safe transportation of modules is critical to the project's success. This paper proposes (1) a novel computational method for formulating and solving the module's horizontal passing ability with road constraints as a motion planning problem and (2) a new index to assess the path performance in critical scenarios. The newly developed Truck‐Parallelized Hybrid A Star (TP‐Hybrid A*) has novelties in collision checking, cost function formulation, and parallel computing on different vehicle dimensions. The effectiveness of the developed algorithm was experimentally verified to be superior to the rapid random tree in computation time and path performance and further demonstrated by applying it to a real‐life MiC project. The new index can serve as a useful tool for decision‐making in module dimension design and transportation planning of MiC projects when tackling the module's horizontal passing ability issues. [ABSTRACT FROM AUTHOR]

The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course. [ABSTRACT FROM AUTHOR]