GPGPU Research Articles

Multi-GPU Acceleration for Finite Element Analysis in Structural Mechanics

This work evaluates the computing performance of finite element analysis in structural mechanics using modern multi-GPU systems. We can avoid the usual memory limitations when using one GPU device for many-core computing using multiple GPUs for scientific computing. We use a GPU-awareness MPI approach implementing a suitable smoothed aggregation multigrid for preconditioning an iterative distributed conjugate gradient solver for GPU computing. We evaluate the performance and scalability of different models, problem sizes, and computing resources. We take an efficient multi-core implementation as the reference to assess the computing performance of the numerical results. The numerical results show the advantages and limitations of using distributed many-core architectures to address structural mechanics problems.

Retrievals on NIRCam Transmission and Emission Spectra of HD 189733b with PLATON 6, a GPU Code for the JWST Era

We present the 2.4–5.0 μm JWST/NIRCam emission spectrum of HD 189733b, along with an independent re-reduction of the previously published transmission spectrum at the same wavelengths. We use an upgraded version of PLanetary Atmospheric Tool for Observer Noobs (PLATON) to retrieve atmospheric parameters from both geometries. In transit, we obtain [M/H] = 0.53−0.12+0.13 and C/O = 0.41−0.12+0.13 , assuming a power-law haze and equilibrium chemistry with methane depletion. In eclipse, we obtain [M/H] = 0.68−0.11+0.15 and C/O = 0.43−0.05+0.06 , assuming a clear atmosphere and equilibrium chemistry without methane depletion. These results are consistent with each other, and with a rerun of our previously published joint retrieval of Hubble Space Telescope and Spitzer transmission and emission spectra. Accounting for methane depletion decreases the C/O ratio by 0.14/0.04 (transmission/emission), but changing the limb cloud parameterization does not affect the C/O ratio by more than 0.06. We detect H2O, CO2, CO, and H2S in both the NIRCam transmission and emission spectra, find that methane is depleted on the terminator, and confirm with VULCAN that photochemistry is a potential cause of this depletion. We also find tentative (1.8σ) evidence of a dayside thermal inversion at millibar pressures. Finally, we take this opportunity to introduce a new version of PLATON. PLATON 6 supports GPU computation, speeding up the code up to 10×. It also supports free retrievals using both volume mixing ratio and centered-log ratio priors; emission from planetary surfaces of different compositions; updated opacities at improved resolution; and Pareto smoothed importance sampling leave-one-out cross-validation.

Curvature Regularization for Non-Line-of-Sight Imaging From Under-Sampled Data.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time.

A 400 Gbit Ethernet Core Enabling High Data Rate Streaming from FPGAs to Servers and GPUs in Radio Astronomy

Abstract The increased bandwidth coupled with the large numbers of antennas of several new radio telescope arrays has resulted in an exponential increase in the amount of data that needs to be recorded and processed. In many cases, it is necessary to process this data in real time, as the raw data volumes are too high to be recorded and stored. Due to the ability of GPUs to process data in parallel, GPUs are increasingly used for data-intensive tasks. In most radio astronomy digital instrumentation (e.g., correlators for spectral imaging, beamforming, pulsar, fast radio burst and SETI searching), the processing power of modern GPUs is limited by the input/output data rate, not by the GPU's computation ability. Techniques for streaming ultra-high-rate data to GPUs, such as those described in this paper, reduce the number of GPUs and servers needed, and make significant reductions in the cost, power consumption, size, and complexity of GPU based radio astronomy backends. In this research, we developed and tested several different techniques to stream data from network interface cards (NICs) to GPUs. We also developed an open-source UDP/IPv4 400 GbE wrapper for the AMD/Xilinx IP demonstrating high-speed data stream transfer from a field programmable gate array (FPGA) to GPU.

Numerical Strategy to Optimize Barrel Electroplating : Discrete Element Method and Electrochemical Modeling

Electroplating barrel processes are widely used, particularly for corrosion protection of small components like screws, or gold plating applied to micro components. Achieving uniform coating for all parts is essential, requiring the identification of optimal parameters such as applied current, barrel rotation speed, or charge rate. The main goal is to maximize anode exposure times and reduce the risks of shocks or part sticking, which can result in heterogenous thickness distribution and aesthetic defects. Traditionally, the definition of optimal process parameters is on empirical approaches, with inherent limitations. This conference aims to present how complementary simulation methods can enhance the identification of optimal processing conditions, focusing on two key aspects.The first aspect involves modeling the electrochemical kinetics of the process to understand the current distribution due to the geometry part itself and predict thickness distribution or alloy composition in the case of an alloy.The second aspect explores the use of granular simulation with the Discrete Element Method (DEM), leveraging the computational power of GPU technology. Although DEM is known for its high computational cost, advancements in GPU computing have made it feasible to distribute computations across multiple cores, enabling faster simulations with larger particle counts. This is particularly beneficial for simulating barrel finishing processes, where complex shapes such as screws or bolts need to be accurately represented. By simulating the movement and interactions of these particles within the barrel, it can be possible to explore mixing dynamics and optimize charge rates to improve treatment uniformity.To validate the numerical models developed using these simulation methods, reduced-scale experimental tests are conducted. A 3D printed transparent drum is used to facilitate tracking of particle movement and interactions during the treatment process. This experimental validation provides crucial insights into the accuracy and reliability of the numerical models, ensuring that the simulated results align with experimental observations.We will also discuss numerical aspects and challenge in order to link DEM mixing results with electrochemical modeling.The results of this study enable reduction of quality defects with mixing and charge rates during electrolytic barrel treatment. The combination of electrochemical modeling and granular simulation allows for a better understanding of the link between process parameters and coating properties (thickness, composition...).In conclusion, the integration of advanced simulation methods offer potential numerical help to increase efficiency and reduce non-quality of barrel electroplating processes.

Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology

Abstract Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges. Finally, the dissemination of AI models and the ability to download preexisting AI models are not simple tasks due to the size and complexity of most programs. Virtual containers offer a solution to such compatibility issues and provide a simplified way for radiologists to use AI models. Virtual containers are software tools that bundle code, required programs, and necessary software packages to ensure that a program runs identically for all users, regardless of their computing environment. This article outlines the features of virtual containers (compatibility, versatility, and portability) and highlights an applied use case for virtual containers in the development of an AI model.

Reducing the replication time for structural estimations: A successful replication of “An Anatomy of International Trade” using GPU computing

AbstractEaton, Kortum, and Kramarz (2011) (EKK) discovered empirical patterns from French manufacturing firms that a baseline firm heterogeneity model could not explain. The authors proposed and estimated a model that closely matches the patterns observed in French data. This paper successfully replicates their findings using author‐provided data, re‐implementing their algorithms in Python and leveraging graphics processing unit computing to significantly boost computational speed. Applying the model to Chinese manufacturing data, despite differences in context, showed that the model explains most observed patterns well.

Real-Time Identification of Strawberry Pests and Diseases Using an Improved YOLOv8 Algorithm

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification.

Leapfrogging Sycamore: Harnessing 1432 GPUs for 7× faster quantum random circuit sampling

Abstract Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor network methods, has significantly reduced the classical simulation time and challenged the claim of the first-generation quantum advantage experiments. However, in terms of generating uncorrelated samples, time-to-solution, and energy consumption, previous classical simulation experiments still underperform the Sycamore processor. Here we report an energy-efficient classical simulation algorithm, using 1432 GPUs to simulate quantum random circuit sampling which generates uncorrelated samples with higher linear cross entropy score and is 7 times faster than the Sycamore 53 qubits experiment. We propose a post-processing algorithm to reduce the overall complexity, and integrate state-of-the-art high-performance general-purpose GPU to achieve two orders of lower energy consumption compared to previous works. Our work provides the first unambiguous experimental evidence to refute Sycamore’s claim of quantum advantage, and redefines the boundary of quantum computational advantage using random circuit sampling.

Adaptive random-grid and progressive color secret sharing with CNN super-resolution on a many-core system

This study introduces an innovative method combining Convolutional Neural Networks (CNN) with random grid generation for enhanced visual secret sharing. Our approach supports color images, reduces processing time, and offers non-pixel expansion and flexible share combinations. Utilizing GPU computation, it significantly improves practicality and efficiency by operating in a feed-forward manner, avoiding complex optimization. Experimental results demonstrate superior metrics: maximum PSNR of 32 dB, 99% NCC correlation with benchmarks, 2.9% NAE, and SSIM of 98%. We achieve substantial speedups– 493 × for 256 × 256 and 3145 × for 1024 × 1024 images–compared to sequential models. The scheme exhibits robustness against attacks, evidenced by CMY component and share histogram similarities, and scalability shown in combinatorial explosion visualization. These findings underscore the efficacy and efficiency of our approach, advancing secure image sharing applications significantly.

General heterogeneous real-time baseband backend system scheme applied to QTT based on RFSoC+GPU

This paper proposes a heterogeneous real-time baseband astronomical backend solution centered on RFSoC and combined GPU. The solution implemented the integration of the high-performance, completely configurable RF signal link to digitize, process, and transmit increasingly massive radio astronomy signals and provided a highly flexible and scalable scheme for the QTT backend. The RFSoC-based integration RF pre-processing front-end directly digitizes dual-polarization signals at the sampling rate of 2048 MHz and converts them to dual-16-channel data streams for parallel mixing and polyphase decimation filtering. The generated baseband data with 256 MHz center frequency and 100 MHz bandwidth is packaged and output to the GPU server through the 100GbE Interface. Complete processes of direct RF sampling, pre-processing, and high-speed transmission are implemented at the receiver. The GPU server executes real-time post-processing at the remote end. The dynamic configuration of two observation modes can be achieved with frequency resolutions of 3.051 and 0.763 kHz. The H2CO absorption line observations with the Nanshan 26-m radio telescope verified the system’s performance and the design’s rationality. This paper explores and confirms the RFSoC’s applicability on the backend. The flexible and efficient QTT backend scheme was planned based on the proposed real-time baseband pattern by expanding multi-RFSoC and GPU computing nodes.

Some computational results on a conjecture of de Polignac about numbers of the form p + 2k

Let U be the set of positive odd numbers that can not be written in the form p+2k. Recently, by analyzing possible prime divisors of b, Chen proved b≥11184810 and ω(b)≥7 if an arithmetic progression a(modb) is in U, with ω(b)=7 if and only if b=11184810, where ω(n) is the number of distinct prime divisors of n. In this paper, we take a computational approach to prove b≥11184810 and provide all possible values of a if a(mod11184810) is in U. Moreover, we explicitly construct nontrivial arithmetic progressions a(modb) in U with ω(b)=8, 9, 10, or 11, and provide potential nontrivial arithmetic progressions a(modb) in U such that ω(b)=s for any fixed s≥12. Furthermore, we improve the upper bound estimate of numbers of the form p+2k by Habsieger and Roblot in 2006 to 0.490341088858244 by enhancing their algorithm and employing GPU computation.

Design and implementation of a scalable correlator based on ROACH2 + GPU cluster for tianlai 96-dual-polarization antenna array

The digital correlator is one of the most crucial data processing components of a radio telescope array. With the scale of radio interferometeric array growing, many efforts have been devoted to developing a cost-effective and scalable correlator in the field of radio astronomy. In this paper, a 192-input digital correlator with six CASPER ROACH2 boards and seven GPU servers has been deployed as the digital signal processing system for Tianlai cylinder pathfinder located in Hongliuxia observatory. The correlator consists of 192 input signals (96 dual-polarization), 125-MHz bandwidth, and full-Stokes output. The correlator inherits the advantages of the CASPER system, for example, low cost, high performance, modular scalability, and a heterogeneous computing architecture. With a rapidly deployable ROACH2 digital sampling system, a commercially expandable 10 Gigabit switching network system, and a flexible upgradable GPU computing system, the correlator forms a low-cost and easily-upgradable system, poised to support scalable large-scale interferometeric array in the future.

Wave Propagation for Structural Health Monitoring (WaveProSHM): open software with GUI

Guided wave–based SHM methods have been explored extensively in recent decades. The wave propagation modelling including piezoelectric transducers and various damage types helps understand intricacies of guided wave behavior and for designing a robust SHM system. However, modelling aspects are still challenging. Moreover, commercially available software for the modelling of guided waves often lacks appropriate high-order elements and code optimization for fast GPU computations. This research aimed to develop and disseminate to the SHM community the open software based on highly efficient vectorised code of the time domain spectral element method which can be run on GPU. The developed code is written in MATLAB with Graphical User Interface (GUI) and communicates with GMSH – open source finite element mesh generator. It can model signals of propagating guided waves in structures which are excited and registered by an array of piezoelectric transducers. The capabilities of the software are shown in an example of a large-scale problem of interacting guided waves with delamination.

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.

Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-center, multi-ethnic, and multi-device data consisting of 309 CBCT scans from Finnish and Thai patients. The localization performance of our approach resulted in the mean distance of 1.99 ± 1.55 mm for the Finnish cohort and 1.96 ± 1.25 mm for the Thai cohort. This performance turned out to be clinically significant i.e., ≤ 2 mm with 61.7% and 64.3% of the landmarks with Finnish and Thai cohorts, respectively. Furthermore, the estimated landmarks were used to measure cephalometric characteristics successfully i.e., with ≤ 2 mm or ≤ 2° error, on 85.9% of the Finnish and 74.4% of the Thai cases. Between the two patient cohorts, 33 of the landmarks and all cephalometric characteristics had no statistically significant difference (p < 0.05) measured by the Mann-Whitney U test with Benjamini-Hochberg correction. Moreover, our method is found to be computationally light, i.e., providing the predictions with the mean duration of 0.77 s and 2.27 s with single machine GPU and CPU computing, respectively. Our findings advocate for the inclusion of this method into clinical settings based on its technical feasibility and robustness across varied clinical datasets.

Advances in granular flow modeling: GPU-based multi-sphere DEM approach and tumbling mill dynamics

Modeling irregular-shaped particulate flow systems poses challenges, especially in mineral processing where accurate representation of particle shape is crucial for capturing macroscopic behavior. The multi-sphere approach, employing DEM, addresses this by approximating particles as overlapping spheres within GPU computing. This study validates particle flow dynamics using literature-based data, showcasing the model’s fidelity and GPU advantages. However, calibration and shape approximation limitations require careful consideration. A quaternion-based orientation method enhances DEM and validates experimental data from packing, hoppers, and tumbling mills. Simulations reveal how particle shape influences granular dynamics, affecting lift-off tendencies, responses to lifter face angles, and power draw trends with mill speed changes. Cubical and spherical particles exhibit orderly packing behavior, contrasting with irregular shapes. This investigation underscores the significant role of particle shape in granular dynamics and its implications for industrial processes.

P‐239: Late‐News Poster: Modeling Particle Deposition in Evaporating Colloidal Droplets

Inkjet printing is a cost‐effective method of particle deposition applicable, for example, in the manufacture of Quantum Dot (QD) color filters. However, it is a challenge to achieve uniform deposition of particles because evaporating colloidal dispersions are prone to the coffee‐ring effect.When the particle size is small, it is difficult to gauge directly by experiment how particles migrate during the evaporation process. Consequently, it is a challenge to optimize the process conditions to improve uniformity by experiment alone. To aid in this task, we have developed a simulator for colloidal droplet evaporation. Rather than use an effective particle concentration field as in previous works, motion of each individual particle is calculated by Stokesian Dynamics, with particle interactions governed by the DLVO theory. In order to facilitate the simulation of large collections of particles, GPU computing is applied to reduce the simulation time.A phase‐field approach is taken to capture the interface physics at the solvent‐air interface. Within a colloidal droplet we find that, by controlling the temperature, it is possible to promote Marangoni flow and, in turn, improve deposition uniformity. In future, we aim to use simulation to further optimize the process conditions, such as the chamber pressure and temperature, in order maximize uniformity.

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update.

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, supportand integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.

Knowledge Graph Reasoning via Dynamic Subgraph Attention with Low Resource Computation

Knowledge graphs (KGs) suffer from inherent incompleteness, which has spurred research into knowledge graph reasoning (KGR), i.e., ways to infer missing facts based on existing triples. The most prevalent approaches employ a static mechanism for entity representation across the entire graph, sharing entity embeddings for different query relations. Nevertheless, these static entity embeddings fail to capture specific semantics for various query scenarios, resulting in inaccurate entity representations and susceptibility to reasoning errors. Furthermore, the whole graph learning style requires substantial computational resources, especially since KGs are often of large-scale. Consequently, these methods are impractical in low-resource environments. To address these issues, we devise a framework called dynamic subgraph attention (DSA), which learns dynamic entity embeddings for different query relations across subgraphs. In our approach, by utilizing multi-hop path history information obtained through path-based learning, we guide a dynamic attention mechanism to generate dynamic entity embeddings for different query relations. To ensure the semantic information of entities, the embedding-based and path-based learning are jointly trained for KGR. Additionally, the proposed dynamic aggregation mechanism operates on subgraphs, resulting in a remarkable 6–7 times resource conservation compared to GPU computations. Empirical experiments further demonstrate that DSA outperforms the current methods significantly on three benchmark datasets.

AI Face Recognition and Processing Technology Based on GPU Computing

In recent years, with the development of deep neural network technology, real-time object detection has become increasingly common in mobile applications. However, practical application requirements drive the algorithm to optimize in terms of speed, energy consumption and accuracy. This paper introduces the application of artificial intelligence in the field of face recognition, especially using TensorRT accelerated reasoning technology to improve the speed and performance of face recognition. At the same time, the paper also discusses the key role of GPU computing in face recognition, and expounds the importance of AI chips for optimizing inference tasks. Through the analysis of experimental results and methods, the performance advantages and application prospects of BlazeFace algorithm in mobile applications are demonstrated, which provides a valuable reference for industry.