Supercomputer Research Articles

Detailed-level modelling of influence spreading on complex networks.

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

Full-process supported Simulation Platform Framework based on cloud computing and HPC integration

Simulation technology is widely used in many fields, it usually involves three processes, (i) pre-process, (ii) analysis solver, and (iii) post-process. Simulation calculations require a large amount of computing resources, and users usually need to use cloud and High-Performance Computing (HPC) systems to complete works. Simulation works are increasingly depending on the capacity of HPC or cloud, for cost reasons, people are more willing to use the services than self-built an HPC or Cloud computing cluster. However, that leads to the isolation of calculations and pre- and post-processing work, adding additional time for data transfer. Moreover, simulation engineers also want to use cloud servers in pre-processing and post-processing, since compared with local workstations, cloud servers have significant advantages in saving hardware investment, remote office collaboration, and data integration management. Therefore, we provide a platform framework based on cloud computing and HPC integration that supports the full process of simulation. Then, we implemented it in Tianhe-1A and Tianhe exascale supercomputers and THCloud environments. Through a city area-level explosion simulation experiment, it was verified that the framework can fully support the whole process of simulation, and effectively reduce the time of simulation work, improving the simulation engineer’s work efficiency. The study shows that the platform provides a feasible solution for full-process simulation. Compared with other platforms, it has the characteristics of full-process, high performance and high security.

Abstract B061: Assessing saliva for cancer biomarker discovery: A liquid biopsy approach

Abstract Introduction: Liquid biopsy has been gaining traction as a replacement for invasive tumor biopsies in oncology. Several blood-based in vitro diagnostics have been approved by the FDA and other regulatory agencies, and efforts are being made to validate urine, saliva, and buccal swabs as alternative matrices. The primary aim of this study was to investigate the relative mRNA expression in paired blood and saliva samples from cancer patients and healthy individuals. Methods: We collected matched saliva and blood samples from 35 subjects, comprising 26 patients with prostate, breast, colon, lung, melanoma, and pancreatic cancers, and 9 healthy individuals. Samples were collected using Wren's proprietary stabilization buffer kit, and total RNA extracted by TRIzol-based Qiagen reagents. RNA sequencing (RNA-Seq) was performed on a Novaseq 6000 platform, and data was analyzed using high-performance computing (HPC) with PartekFlow software, employing the BWA aligner (hg38). We focused on comparing gene expression in cancers (as a group) compared to controls. Results: The total number of genes detected in saliva was 3,985, compared to 14,459 genes detected in blood samples. A substantial overlap was observed, with more than 92.8% of saliva-detected genes also found in blood. We also examined housekeeping gene detection in both matrices. In this set of 1,003 housekeeping genes, 30% (n=305) were expressed in both saliva and blood. When we narrowed the scope to only include the 1,154 cancer-related genes as listed by the OncoKB database, 88.3% were detected in saliva and/or blood, with 37.1% of these genes (n=429) were expressed in both saliva and blood. Lastly, we examined a set of 241 cancer-associated biomarkers utilized in RT-PCR-based diagnostic blood assays clinically validated by our laboratory. This subset analysis revealed that 23.2% of these biomarkers were expressed in both saliva and blood. Conclusion: Our data indicate that the majority of mRNA detected in saliva was also detected in blood, with differential expression observed in cancer patients compared to normal controls. Given the relatively high limit of detection (low sensitivity) using RNA-Seq technology compared to PCR and the relatively small number of subjects, the data may suggest that saliva could function as a surrogate biomarker matrix for blood. Citation Format: Srinivas V Koduru, Mark Kidd, Abdel B Halim. Assessing saliva for cancer biomarker discovery: A liquid biopsy approach [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B061.

GPU Parallel Computing Architectures : Unlocking the Power of Parallelism for High-Performance Applications

Graphics Processing Units (GPUs) have evolved from specialized graphics rendering hardware to become powerful parallel computing architectures, revolutionizing high-performance computing across diverse domains. This comprehensive article explores the fundamental principles of GPU parallel computing architectures, their design, and their impact on modern computational challenges. We begin by examining the multi-core structure, memory hierarchy, and data processing capabilities of GPUs, including the SIMD execution model and thread organization. The article then delves into prominent programming models like CUDA and OpenCL, discussing their features and comparative advantages. We explore how GPUs are leveraged for general-purpose computing in scientific simulations, machine learning, and big data analytics, while also addressing the challenges inherent in GPU parallel computing, such as data transfer bottlenecks and load balancing. Recent technological advancements, including tensor cores, unified memory architecture, and ray tracing acceleration, are analyzed for their transformative potential. The article concludes by examining future directions in GPU technology, including integration with emerging technologies like quantum computing, advancements in energy efficiency, and the potential impact on solving complex global challenges. Through this comprehensive analysis, we illustrate the pivotal role of GPU parallel computing architectures in shaping the future of high-performance computing and their potential to address some of the world's most pressing computational problems.

Film image processing and production based on high-performance calculation

In film and television production, efficient and precise image processing is vital for achieving realistic visual effects. Therefore, exploring and applying advanced image processing technologies has become an essential method for elevating the production quality of film and television projects. This work investigates the application of artificial intelligence (AI) technology in the processing and production of animated images in film and television scenarios. By comparing the performance of standard Generative Adversarial Network (GAN), DenseNet, and CycleGAN models under different noise conditions, it is found that CycleGAN performs the best in image denoising and detail restoration. Experimental results demonstrate that CycleGAN achieves a Peak Signal-to-noise Ratio (PSNR) of 30.1dB and a Structural Similarity Index Measure (SSIM) of 0.88 under Gaussian noise conditions. Moreover, CycleGAN achieves a PSNR of 29.5dB and an SSIM of 0.85 under salt-and-pepper noise conditions. It outperforms the other models in both conditions. Additionally, CycleGAN’s mean absolute error is significantly lower than that of the other models. This work demonstrates that CycleGAN can more effectively handle complex noise and generate high-quality images under unsupervised learning conditions. These findings provide new directions for future image processing research and offer important references for model selection in practical applications. This work not only offers new perspectives on the development of animation image processing technology but also establishes a theoretical foundation for applying advanced AI techniques in film and television production. Through comparative analysis of various deep learning models, this work highlights the superior performance of CycleGAN under complex noise conditions. This advancement not only drives progress in image processing technology but also provides effective solutions for efficient production and quality enhancement of future film and television works.

Sub-volt forward-biased silicon microring modulator at 210 Gb/s.

Low-voltage and efficient optical modulators in the silicon photonic (SiPh) platform are highly desired for realizing high-speed connectivity in chip level interconnects, data center interconnects, and high-performance computing (HPC). With the modulator operating at CMOS compatible voltages, high-voltage modulator drivers are no longer needed, thus reducing driver design complexity and power consumption. We demonstrate a silicon microring modulator (MRM) operating at a driving voltage of 0.8 Vpp. We achieve high modulation efficiency by using a small forward bias of 0.2 V: the forward bias voltage allows the modulator to have an enhanced optical modulation amplitude (OMA) by operating near injection mode, modulating 180 Gb/s (180 Gbaud) non-return-to-zero (NRZ) and 210 Gb/s (105 Gbaud) 4-level pulse amplitude modulation (PAM-4) free of electrical or optical amplification. We also demonstrate the operation at zero bias and achieve up to 200 Gb/s. Error-free operation was observed at 130 Gb/s (NRZ). The absence of an external biasing voltage can further improve energy efficiency and simplifies device integration.

Characterizing structural features of two-dimensional particle systems through Voronoi topology

Abstract This paper introduces a new approach toward characterizing local structural features of two-dimensional particle systems. The approach can accurately identify and characterize defects in high-temperature crystals, distinguish a wide range of nominally disordered systems, and robustly describe complex structures such as grain boundaries. This paper also introduces two-dimensional functionality into the open-source software program VoroTop which automates this analysis. This software package is built on a recently-introduced multithreaded version of Voro++, enabling the analysis of systems with billions of particles on high-performance computer architectures.

Prediction and treatment of joint injuries in basketball training based on improved regression algorithm from the perspective of sports biomechanics

With the increasing popularity of basketball, especially in collegiate competitions like the University Basketball Super League, the sport has become a significant part of student life. The intensity of basketball training and competition has risen, necessitating athletes to have enhanced physical capabilities to meet modern demands. This heightened physical confrontation often leads to various injuries, with joint injuries being particularly common and impactful. This study integrates sports biomechanics with machine learning to address the prediction and treatment of joint injuries in basketball training. By employing an improved regression algorithm and leveraging high-performance computing, we have experimentally analyzed the prediction of joint injuries and proposed effective solutions. Our results indicate that the difference between the highest and lowest predicted residual values for the Back Propagation (BP) model was 0.92, and for the Extreme Learning Machine (ELM) regression model was 0.87. Notably, the improved ELM regression model demonstrated a reduced residual difference of 0.43. This improvement suggests that the enhanced ELM regression model offers superior prediction accuracy for joint injuries in basketball training and provides more comprehensive monitoring of athletes’ physical health, thereby supporting the advancement of basketball training programs.

An assessment of large language models for OpenMP-based code parallelization: a user perspective

Large language models have sparked a lot of attention in the research community in recent days, especially with the introduction of practical tools such as ChatGPT and Github Copilot. Their ability to solve complex programming tasks was also shown in several studies and commercial solutions increasing the interest in using them for software development in different fields. High performance computing is one of such fields, where parallel programming techniques have been extensively used to utilize raw computing power available in contemporary multicore and manycore processors. In this paper, we perform an evaluation of the ChatGPT and Github Copilot tools for OpenMP-based code parallelization using a proposed methodology. We used nine different benchmark applications which represent typical parallel programming workloads and compared their OpenMP-based parallel solutions produced manually and using ChatGPT and Github Copilot in terms of obtained speedup, applied optimizations, and quality of the solution. ChatGPT 3.5 and Github Copilot installed with Visual Studio Code 1.88 were used. We concluded that both tools can produce correct parallel code in most cases. However, performance-wise, ChatGPT can match manually produced and optimized parallel code only in simpler cases, as it lacks a deeper understanding of the code and the context. The results are much better with Github Copilot, where much less effort is needed to obtain correct and performant parallel solution.

Application of Natural Science Methods in Social Science Research

The introduction of natural science methodologies into social science research aims to enhance the scientific rigor and validity of studies through precise techniques. By employing quantitative analysis, data mining, and model construction, researchers are able to delve deeper into the complexities of social phenomena, injecting new vitality into traditional qualitative research. Quantitative analysis methods render research outcomes more objective and reproducible, while data mining techniques can extract underlying patterns and trends from vast amounts of data. Model construction offers a systematic understanding of social dynamic processes, supporting the prediction and intervention of future social developments. Computer simulation technologies, particularly with the backing of big data and high-performance computing, enable hypothesis testing and virtual experiments on social systems, allowing for multi-level and multi-perspective observations of social phenomena. This interdisciplinary integration not only provides new research tools and methodologies for the social sciences but also effectively fosters the interchange of knowledge between different disciplines. However, continuous reflection and adjustment in practical applications are necessary to address the potential pitfalls and limitations that may arise from the complexity of these methods.

Visualizing the human olfactory projection and ancillary structures in a 3D reconstruction

Visualizing in 3D the histological microanatomy of the human olfactory projection from the olfactory mucosa in the nasal cavity to the olfactory bulbs in the cranial cavity necessitates a workflow for handling a great many sections. Here, we assembled a 3D reconstruction of a 7.45 cm3en-bloc specimen extracted from an embalmed human cadaver. A series of 10 µm coronal sections was stained with quadruple fluorescence histology and scanned in four channels. A trained anatomist manually segmented six structures of interest in a subset of the sections to generate the ground truth. Six convolutional neural networks were then trained for automatic segmentation of these structures in 1234 sections. A high-performance computing solution was engineered to register the sections based on the fluorescence signal and segmented structures. The resulting 3D visualization offers several novel didactic opportunities of interactive exploration and virtual manipulation. By extrapolating manual counts of OSNs in a subset of sections to the calculated volume of the envelope of the entire olfactory epithelium, we computed a total of ~2.7 million OSNs in the specimen. Such empirically derived information helps assess the extent to which the organizational principles of the human olfactory projection may differ from those in mice.

Nonlinear finite element model updating and fourier neural operator for digital twin of reinforced concrete containment vessel under seismic excitations

A series of unidirectional seismic excitation tests on a 1/20 scaled reinforced concrete containment vessel (RCCV) specimen was conducted using a 6-DOF shake table. This study presents the development of a digital twin to accurately capture the complex behavior of reinforced concrete structures under seismic effects. With the aid of finite element (FE) software Abaqus, the RCCV structure was initially modeled and analyzed to obtain preliminary structural acceleration and displacement results. To enhance the accuracy of these simulations, a nonlinear FE model updating framework was developed by integrating Abaqus software with Python. The model updating of RCCV was conducted subsequently based on the shake table measured data to obtain results with significantly improved accuracy. It is shown that the FE simulations and the nonlinear FE model updating algorithm are highly effective for the high-fidelity simulation of large and complex structures. Furthermore, a Fourier neural operator (FNO) model is used to learn the FE simulation database and predict the FE simulation results. It is observed that FNO has an excellent performance in predicting the structural responses with various material and damping parameters and accomplished speedup over 5000 times compared to the FE simulations. The findings provide a basis for the application of nonlinear model updating algorithms at the structure system level through the high-performance GPU-based parallel computing and underscore that the strategy of FE analysis combined with artificial intelligence (AI) has great potential in the field of digital twins for large and complex structures.

A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture

The industrialization, high-density, and greener aquaculture requires a more precise and intelligent aquaculture management. Phenotypic and behavioral information of fish, which can reflect fish growth and welfare status, play a crucial role in aquaculture management. Stereo vision technology, which simulates parallax perception of the human eye, can obtain the three-dimensional phenotypic characteristics and movement trajectories of fish through different types of sensors. It can overcome the limitations in dealing with fish deformation, frequent occlusions and understanding three-dimension scenes compared to the traditional two-dimensional computer vision techniques. With the deep learning development and application in aquaculture, stereo vision has become a super computer vision technology that can provide more precise and interpretable information for intelligent aquaculture management, such as size estimation, counting and behavioral analysis of fish. Hence, it is very beneficial for researchers, managers, and entrepreneurs to possess a thorough comprehension about the fast-developing stereo vision technology for modern aquaculture. This study provides a critical review of relevant topics, including the four-layer application structure of stereo vision technology in aquaculture, various deep learning-based technologies used, and specific application scenarios. The review contributes to research development by identifying the current challenges and provide valuable suggestions for future research directions. This review can serve as a useful resource for developing future studies and applications of stereo vision technology in smart aquaculture, focusing on phenotype feature extraction and behavioral analysis of fish.

Efficient I/O Performance-Focused Scheduling in High-Performance Computing

High-performance computing (HPC) systems are becoming increasingly important as contemporary exascale applications with demand extensive computational and data processing capability. To optimize these systems, efficient scheduling of HPC applications is important. In particular, because I/O is a shared resource among applications and is becoming more important due to the emergence of big data, it is possible to improve performance by considering the architecture of HPC systems and scheduling jobs based on I/O resource requirements. In this paper, we propose a scheduling scheme that prioritizes HPC applications based on their I/O requirements. To accomplish this, our scheme analyzes the IOPS of scheduled applications by examining their execution history. Then, it schedules the applications at pre-configured intervals based on their expected IOPS to maximize the available IOPS across the entire system. Compared to the existing first-come first-served (FCFS) algorithm, experimental results using real-world HPC log data show that our scheme reduces total execution time by 305 h and decreases costs by USD 53 when scheduling 10,000 jobs utilizing public cloud resources.

Embankment Breach Simulation and Inundation Mapping Leveraging High-Performance Computing for Enhanced Flood Risk Prediction and Assessment

Abstract. Embankment breaches represent significant hazards to communities and infrastructure, precipitating catastrophic flood occurrences. The precise prediction of floods and understanding the scope of inundation stemming from embankment failures are imperative for effective disaster preparedness and response. This research delves into a case study on simulating embankment breaches to evaluate the extent of flooding. Leveraging advanced hydrodynamic models validated through high-performance computing (HPC) systems, and integrating real-time data assimilation, we aim to improve accuracy in flood forecasting. The study endeavours to bolster flood risk management by furnishing detailed inundation maps and insights into embankment breach dynamics, thereby facilitating enhanced preparedness and response strategies. Our findings reveal that simulations conducted on multicore processors offer superior performance compared to single-core setups, yielding enhanced result accuracy and providing administrators with increased lead time. It unlocks high-resolution simulations for intricate basin details, explores a wider range of flood scenarios quickly, and allows for efficient ensemble modelling to assess model uncertainty. Through HPC utilization, we can harness high-resolution digital elevation models (DEMs) for 2D-hydrodynamic modelling, enabling rapid assessment of water spread resulting from embankment breaches within a mere 20-minute timeframe, a significant improvement from the previous 3–4 hours duration.

Artificial photoelectric synaptic devices with ferroelectric diode effect for high-performance neuromorphic computing

With the rapid advancement of artificial intelligence and machine learning, the demand for neuromorphic computing systems has intensified. High-performance artificial synaptic devices are crucial to achieving this objective. Ferroelectric diode artificial synaptic devices were prepared using aluminum-doped BaTiO3 (BTAO) thin films by a low-cost sol-gel method. These devices mimic the basic properties of biological synapses, such as long-term plasticity (LTP) and short-term plasticity (STP), under electrical stimulation. Additionally, the devices exhibit an excellent UV light response, enabling the transition from STP to LTP by adjusting the light pulse intensity, width, and number of pulses. Aluminum doping significantly enhances the ferroelectricity of BaTiO3 films, increasing the Pr from 2.31 µC/cm² to 9.08 µC/cm². By modulating the Schottky barrier through polarization, the BTAO films exhibit a switchable diode effect, which facilitates fine-tuning of the synaptic connection strength while maintaining synaptic stability. Recognition accuracies of 97.32% for the MNIST dataset and 87.48% for the Fashion-MNIST dataset were achieved by convolutional neural network simulations. These results suggest new possibilities for brain-like processing with ferroelectric diodes.

High throughput edit distance computation on FPGA-based accelerators using HLS

Edit distance is a computational grand challenge problem to quantify the minimum number of editing operations required to modify one string of characters to the other, finding many applications of natural language processing. In recent years, relevant and increasing interest has also emerged from deoxyribonucleic acid (DNA) applications, like Next Generation Sequencing and DNA storage technologies. Both applications share two crucial features: i) the information is coded into the four bases of DNA and ii) the level of operational noise is still high causing errors in the data, requiring inclusion in the workflow of the computation of algorithms such as the edit distance for finding similarities between sequences. To boost this computation many solutions are available in the literature. Among them, the FPGAs are largely used since the data domain of those applications is strings of 4 characters represented as two-bit values, inconveniently fitting the basic data types of ordinary CPUs and GPUs, with additional benefits of providing a high level of parallelism and low processing latency. This contribution presents a computing- and energy-efficient design implementing the edit distance algorithm combining metaprogramming and High-Level Synthesis. We also assess the performance of our design targeting recent FPGA-based accelerators. Our solution uses nearly 90% of FPGA basic-block hardware resources achieving about 90% of computing efficiency delivering a maximum throughput of 16.8 TCUPS and an energy efficiency of 46 Mpair/Joule, enabling the use of FPGAs as a new class of accelerators for High Performance Computing in DNA applications.

Overview of High-Performance Computing And It’s Current Trend in India

This is an era of High-Performance Computing abbreviated as HPC. HPC evolved due to increase in demand of Processing speed. It is a hybrid technology that continuously changes its colors, so it is hard to define it in static words. However, if we still persist to define it the effort leads to a definition as below – “HPC is a branch of computer science that concentrates on combining the power of computing devices, networking infrastructure and storage systems using parallel hardware, networking and software technology that utilizes human expertise to gain optimized and sustained performance for solving scientific, engineering, big data and research problems including grand challenges like computer modeling, simulations and analysis of data.

Democratizing uncertainty quantification

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale.In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.

Construction of Akushsky Core Functions Without Critical Cores

The residue number system is widely used in cryptography, digital signal processing, image processing systems, and other areas where high-performance computing is required. One of the main tools used in the residue number system is the Akushsky core function. However, its use is limited due to the existence of so-called critical cores. This study aims to develop Akushsky core functions that effectively eliminate the occurrence of critical cores, thereby enhancing their applicability in real-world scenarios. We introduce a fundamental approach to critical core detection that reduces the average time for critical core detection by 99.48% compared to the brute force algorithm. The results of our analysis indicate not only a substantial improvement in the speed of core detection but also an enhancement in the overall performance of systems utilizing the Akushsky core function. Our findings provide important insights into optimizing residue number systems and encourage further exploration into advanced computational techniques within this domain.