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Authenticated encryption with associated data (AEAD) has become prominent over time because it offers authenticity and confidentiality simultaneously. In 2018, the National Institute of Standards and Technology (NIST) initiated a competition to standardize lightweight AEAD and hash functions, with Ascon as the final winner among the 10 finalists. Numerous prior works evaluated their performance on FPGA and ASIC, but not on a parallel architecture like GPU, which is a common accelerator already found in many existing cloud servers. In this work, the first GPU implementation of the NIST AEAD finalists is proposed. Several GPU implementation techniques applicable to all AEAD schemes are presented, along with novel techniques for some specific schemes to enhance throughput performance. Experimental results show that all NIST AEAD finalists can achieve high throughput (up to 111.53M AEAD per second), approximately 142.19% and 72.65% improvement compared to unoptimized GPU version, and the investigated FPGA results respectively. [ABSTRACT FROM AUTHOR]

ABSTRACTTechniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time.

Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors. [ABSTRACT FROM AUTHOR]

Solving a significant number of differential-algebraic equations is essential for conducting transient stability simulations on a complex and interconnected electrical power system. Because of the increasing size and intricacy of modern grids, traditional sequential simulation methods are becoming more computationally challenging and time-consuming to use for dynamic simulations. This research aims to examine parallelism techniques from an innovative algorithm viewpoint to the hardware level by utilizing multiple graphics processing units as the main computational tool designed to simulate the dynamic behaviour of power systems on a large scale. Our approach features a novel hybrid algorithm to improve the dynamic simulations of grids with significant renewable energy integration. The algorithm employs a Schwarz-based approach in its initial decomposition phase to isolate synchronous machines and renewable sources from the grid. This separation enhances the algorithm's ability to manage these components effectively. Subsequently, each subsystem undergoes further division into subdomains in the second stage, with separate computations carried out using the Schur-complement technique. The simulations were conducted on various test systems, with a maximum of 25,000 buses, 8,000 synchronous generators, and 256 PV farms all modelled in detail. We introduce a GPU-oriented preprocessing and vectorization parallelization method for the implementation of our two-stage algorithm. This approach provides the flexibility to tailor parallelization techniques, including nested for loops and kernel pragmas, to craft a hardware solution that enhances the performance of our dynamic domain decomposition algorithm. Simulation outcomes have verified that this approach can yield a notable 7.8x acceleration in speed when executed on an NVIDIA GeForce RTX 2070 SUPER GPU.

In this study, we proposed a novel GPU-based solution for modelling two-dimensional inviscid and viscous compressible supersonic/hypersonic flows. Texture and surface pointers are used to access GPU memory locations. For effective and efficient use of surface pointers, we grouped multiple 2D arrays referenced and indexed by a single 3D surface pointer. To enable the proposed solver for double-precision calculations, two consecutive 32-bit memory locations were grouped to maintain the efficiency of surface pointers while taking advantage/accuracy of 64-bit calculations. Resolving data and computation dependencies for parallel applications is another complex task that is the focus of this study. Computation dependencies have been solved by using multiple mutually synchronized GPU kernels and executing them sequentially using the GPU default stream to ensure that all relevant data is available to the threads or computed before they actually use it. Consequently, there is no intra-core data dependency in our proposed approach, while inter-core data dependency is successfully solved by stringing multiple kernels together. Using NVIDIA GTX 660 GPUs, we achieved 20x speedup compared to traditional Core i5® computers. This speedup is the result of the Surface Pointer's GPU capabilities for double precision computations. The simulation results are also consistent with the experimental and numerical results of this study. [ABSTRACT FROM AUTHOR]

Dynamic contingency analysis (DCA) for modern power systems is fundamental to help researchers and operators look ahead of potential issues, arrange operational plans, and improve system stabilities. However, since the system size and the number of contingency scenarios continue to increase, pursuing more effective computational performance faces many difficulties, such as slow algorithms and limited computing resources. This research accelerates the intensive computations of massive DCAs by implementing a two-level hierarchical computing architecture on graphical processing units (GPUs). The performance of the designed method is examined using four test systems of different sizes and compared with a CPU-based parallel approach. The results show up to 2.8x speedup using one GPU and 4.2x speedup using two GPUs, respectively. More accelerations can be observed once more GPUs are configured. It demonstrates that the proposed architecture can significantly enhance the overall computational performance of massive DCAs while maintaining a strong scaling capability under various resource configurations.

Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors.

Techniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time. [ABSTRACT FROM AUTHOR]

Introduction: Wearable assistive devices for the visually impaired whose technology is based on video camera devices represent a challenge in rapid evolution, where one of the main problems is to find computer vision algorithms that can be implemented in low-cost embedded devices.Objectives and Methods: This work presents a Tiny You Only Look Once architecture for pedestrian detection, which can be implemented in low-cost wearable devices as an alternative for the development of assistive technologies for the visually impaired.Results: The recall results of the proposed refined model represent an improvement of 71% working with four anchor boxes and 66% with six anchor boxes compared to the original model. The accuracy achieved on the same data set shows an increase of 14% and 25%, respectively. The F1 calculation shows a refinement of 57% and 55%. The average accuracy of the models achieved an improvement of 87% and 99%. The number of correctly detected objects was 3098 and 2892 for four and six anchor boxes, respectively, whose performance is better by 77% and 65% compared to the original, which correctly detected 1743 objects.Discussion: Finally, the model was optimized for the Jetson Nano embedded system, a case study for low-power embedded devices, and in a desktop computer. In both cases, the graphics processing unit (GPU) and central processing unit were tested, and a documented comparison of solutions aimed at serving visually impaired people was performed.Conclusion: We performed the desktop tests with a RTX 2070S graphics card, and the image processing took about 2.8 ms. The Jetson Nano board could process an image in about 110 ms, offering the opportunity to generate alert notification procedures in support of visually impaired mobility.

We present a new model called GPUFLOW for the modeling and simulation of geophysical flows. GPUFLOW, which is based on the cellular automaton paradigm, features a physical model for the thermal and rheological evolution of lava flows (including temperature-dependent emissivity and cooling by radiation and air convection), support for debris flows without thermal dependency, a parallel implementation on graphic processing units (GPUs), and a simpler and computationally more efficient solution to the grid bias problem. Here, we describe the physical–mathematical model implemented in GPUFLOW and estimate the influence of input data on the flow emplacement through different synthetic test cases. We also perform a validation using two real applications: a debris flow that occurred in July 2006 in the Dolomites (Italy) and the December 2018 lava flow from the Etna volcano. GPUFLOW's reliability prediction is accomplished by fitting the simulation with the actual flow fields, obtaining average values between ~55% and 75%. [ABSTRACT FROM AUTHOR]

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Passive seismic exploration is an environmentally friendly, economical, and highly accessible exploration method that is widely used in different-scale subsurface imaging. Retrieving reflections from ambient noise is a newly developed passive seismic method that can be used in many fields such as mineral exploration and near-surface imaging, but the interferometry calculation is time-consuming because it requires a longer period of data acquisition to improve the signal-to-noise ratio of the retrieved reflections. In this study, we introduced a graphical processing unit (GPU)- based implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings. Because all traces are involved in the computation process, but the size of passive source data often exceeds available memory, repeated disk reads lead to a decrease in computational efficiency. We design a strategy of grouping computations followed by stacking to minimize disk input and output, simultaneously keeping the memory requirements low. Passive source data is read and written only once, and there is no requirement for the memory size to be greater than the data size. Additionally, acceleration technologies such as asynchronous execution, asynchronous memory transfer, and GPU-accelerated libraries are used. We test the efficiency using short data where the number of sampling points per trace is on the order of 30,000, and long data, where the number of sampling points per trace is on the order of 30,000,000. For short data, the average speedup is 4 compared with a multi-core central processing unit (CPU); for long data, the speedup can reach 24. The GPU-based implementation of interferometry greatly reduces the calculation time for retrieving reflections from passive source seismic recordings, providing a solution to the problem of large calculation in three-dimensional (3D) passive source reflection exploration. • Seismic interferometry was designed using a GPU to retrieve reflection responses. • The average speedup was more than 20. • The correctness was verified through numerical simulation and field data. [ABSTRACT FROM AUTHOR]

Several causes make brain cancer identification a challenging task for neurosurgeons during the surgical procedure. The surgeons' naked eye sometimes is not enough to accurately delineate the brain tumor location and extension due to its diffuse nature that infiltrates in the surrounding healthy tissue. For this reason, a support system that provides accurate cancer delimitation is essential in order to improve the surgery outcomes and hence the patient's quality of life. The brain cancer detection system developed as part of the “HypErspectraL Imaging Cancer Detection” (HELICoiD) European project meets this requirement exploiting a non-invasive technique suitable for medical diagnosis: the hyperspectral imaging (HSI). A crucial constraint that this system has to satisfy is providing a real-time response in order to not prolong the surgery. The large amount of data that characterizes the hyperspectral images, and the complex elaborations performed by the classification system make the High Performance Computing (HPC) systems essential to provide real-time processing. The most efficient implementation developed in this work, which exploits the Graphic Processing Unit (GPU) technology, is able to classify the biggest image of the database (worst case) in less than three seconds, largely satisfying the real-time constraint set to 1 minute for surgical procedures, becoming a potential solution to implement hyperspectral video processing in the near future.

We report on the acceleration of annual daylighting simulations for fenestration systems in the Radiance ray-tracing program. The algorithm was optimized to reduce both the redundant data input/output operations and the floating-point operations. To further accelerate the simulation speed, the calculation for matrix multiplications was implemented using parallel computing on a graphics processing unit. We used OpenCL, which is a cross-platform parallel programming language. Numericalexperiments show that the combination of the above measures can speed up the annual daylighting simulations 101.7 times or 28.6 times when the sky vector has 146 or 2306 elements, respectively.

We present a new model called GPUFLOW for the modeling and simulation of geophysical flows. GPUFLOW, which is based on the cellular automaton paradigm, features a physical model for the thermal and rheological evolution of lava flows (including temperature-dependent emissivity and cooling by radiation and air convection), support for debris flows without thermal dependency, a parallel implementation on graphic processing units (GPUs), and a simpler and computationally more efficient solution to the grid bias problem. Here, we describe the physical–mathematical model implemented in GPUFLOW and estimate the influence of input data on the flow emplacement through different synthetic test cases. We also perform a validation using two real applications: a debris flow that occurred in July 2006 in the Dolomites (Italy) and the December 2018 lava flow from the Etna volcano. GPUFLOW’s reliability prediction is accomplished by fitting the simulation with the actual flow fields, obtaining average values between ~55% and 75%.

This paper presents a GPU based parallel implementation for the non-uniform illumination image restoration method, which uses a retinex based algorithm to decompose the original image into brightness and reflectance components, and adjusts the brightness value through an adaptive gamma correction and nonparametric mapping to achieve the restoration. Specifically, we parallelize the improved retinex algorithm on GPU to extract the brightness value of each pixel. After that, the probability of different brightness range is counted through each block to the entire image to reduce the competition of memory access. Finally, we use two different parallel reduce methods to calculate the probability density and cumulative density of brightness value and generate the mapping curve. The experiment conducted on three different GPUs and two CPUs with different resolution images shows that our method can process a 1024 × 2048 image in 1.024 ms on RTX2080Ti, indicates a great potential for real-time application. [ABSTRACT FROM AUTHOR]

The chemical-potential multiphase lattice Boltzmann method (CP-LBM) has the advantages of satisfying the thermodynamic consistency and Galilean invariance, and it realizes a very large density ratio and easily expresses the surface wettability. Compared with the traditional central difference scheme, the CP-LBM uses the Thomas algorithm to calculate the differences in the multiphase simulations, which significantly improves the calculation accuracy but increases the calculation complexity. In this study, we designed and implemented a parallel algorithm for the chemical-potential model on a graphic processing unit (GPU). Several strategies were used to optimize the GPU algorithm, such as coalesced access, instruction throughput, thread organization, memory access, and loop unrolling. Compared with dual-Xeon 5117 CPU server, our methods achieved 95 times speedup on an NVIDIA RTX 2080Ti GPU and 106 times speedup on an NVIDIA Tesla P100 GPU. When the algorithm was extended to the environment with dual NVIDIA Tesla P100 GPUs, 189 times speedup was achieved and the workload of each GPU reached 96%. [ABSTRACT FROM AUTHOR]

The rapid expansion in scale of power systems and the emergence of new smart grid technologies continuously increase computational complexity of power system simulations. Graphic processing unit (GPU), which features massive concurrent threads and excellent floating-point performance, brings new opportunities into power system simulations. This paper introduces an advanced GPU-CPU based parallel power flow (PF) approach by adopting vectorization parallelization and sparse techniques. Specifically, the root cause behind sparsity property of PF and its impacts on the first two steps of Newton-Raphson (NR) based PF calculation, i.e., forming nodal power mismatch vector and updating Jacobian matrix, are quantitatively analyzed. Moreover, a novel GPU-CPU based parallel PF approach is presented, which effectively integrates advanced GPU-based vectorization parallelization and sparse techniques to accelerate performance of PF calculations. Numerical studies validate the effectiveness of various customized parallel schemes for individual key steps of the proposed NR-based parallel PF approach. [ABSTRACT FROM AUTHOR]