Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance Computing

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Experimental Research on Early-Age Property of High-Performance Concrete Column by Embedded Sensors

This paper aims at monitoring the autogenous shrinkage （AS） of a high-performance concrete （HPC） column specimen using an embedded strain gauge just after concrete pouring. A real size specimen （40 cm×40 cm×100cm） was made to simulate the structural members in construction site. The results show that the amount of HPC AS is comparable to that of drying shrinkage and even larger than it, so AS can not be omitted for HPC. By comparing the plain HPC and reinforced HPC specimens, the influences of reinforced bars on autogenous shrinkage and temperature distribution were obtained.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

基于Qt/Embedded的无线点餐系统软件设计

针对传统人手点餐服务方式工作量大、易出错、统计不便等缺点,设计了基于QT/Embedded的无线点餐系统。系统软件终端部分采用嵌入式Linux为操作系统,Qt/Embedded为用户图形界面,服务器部分采用C#语言进行设计。文章介绍了系统软件的设计及Qt的移植过程。该系统操作简便,可靠性高,运行稳定,具有良好的实用价值。

MatDEM-fast matrix computing of the discrete element method

Discrete element method can effectively simulate the discontinuity,inhomogeneity and large deformation and failure of rock and soil.Based on the innovative matrix computing of the discrete element method,the highperformance discrete element software MatDEM may handle millions of elements in one computer,and enables the discrete element simulation at the engineering scale.It supports heat calculation,multi-field and fluidsolid coupling numerical simulations.Furthermore,the software integrates pre-processing,solver,postprocessing,and powerful secondary development,allowing recompiling new discrete element software.The basic principles of the DEM,the implement and development of the MatDEM software,and its applications are introduced in this paper.The software and sample source code are available online(http://matdem.com).

Convergence Platform of Cloud Computing and Internet of Things (IoT) for Smart Healthcare Application

Internet of Things (IoT) is a widely distributed network which requires small amount of power supply having limited storage and processing capacity. On the other hand, Cloud computing has virtually unlimited storage and processing capabilities and is a much more mature technology. Therefore, combination of Cloud computing and IoT can provide the best performance for users. Cloud computing nowadays provides lifesaving healthcare application by collecting data from bedside devices, viewing patient information and diagnose in real time. There may some concerns about security and other issues of the patient’s data but utilization of IoT and Cloud technologies in healthcare industry would open a new era in the field of healthcare. To ensure basic healthcare needs of the people in the rural areas, we have proposed Cloud-IoT based smart healthcare system. In this system various types of sensors (Temperature, Heart bit, ECG, etc.) are equipped in the patient side to sense the patient’s physiological data. For securing data RSA based authentication algorithm and mitigation of several security threats have been used. The sensed data will process and store in the Cloud server. Stored data can be used by the authorized and/or concerned medical practitioner upon approved by the user for patient caring.

Analysis and Comparison of Five Kinds of Typical Device-Level Embedded Operating Systems

Today, the number of embedded system was applied in the field of automation and control has far exceeded a variety of general-purpose computer. Embedded system is gradually penetrated into all fields of human society, and ubiquitous embedded applications constitute the 'ubiquitous' computing era. Embedded operating system is the core of the em-bedded system, and it directly affects the performance of the whole system. Our Liaoning Provincial Key Laboratory of Embedded Technology has successfully developed five kinds of device-level embedded operating systems by more than ten years’ efforts, and these systems are Webit 5.0, Worix, μKernel, iDCX 128 and μc/os-II 128. This paper mainly analyses and compares the implementation mechanism and performance of these five kinds of device-level embedded operating systems in detail.

Embedded BCI Rehabilitation System for Stroke

In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of problem,a new embedded rehabilitation system based on brain computer interface(BCI)is proposed in this paper.The system is based on motor imagery(MI)therapy,in which electroencephalogram(EEG)is evoked by grasping motor imageries of left and right hands,then collected by a wearable device.The EEG is transmitted to a Raspberry Pie processing unit through Bluetooth and decoded as the instructions to control the equipment extension.Users experience the limb movement through the visual feedback so as to achieve active rehabilitation.A pilot study shows that the user can control the movement of the rehabilitation equipment through his mind,and the equipment is convenient to carry.The study provides a new way to stroke rehabilitation.

Reversible Data Hiding for Medical Images in Cloud Computing Environments Based on Chaotic Hénon Map

Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.

Aspect-Oriented Design Method for Embedded Systems Based on Timed Statecharts

The formal modelling and verification method has become an effective way of improving the reliability and correctness of complex,safety-critical embedded systems.Statecharts are widely used to formally model embedded applications,but they do not realise the reasonable separation of system concerns,which would result in code scattering and tangling.Aspect-Oriented Software Development(AOSD)technology could separate crosscutting concerns from core concerns and identify potential problems in the early phase of the software development life cycle.Therefore,the paper proposes aspect-oriented timed statecharts(extended timed statecharts with AOSD)to separately model base functional requirements and other requirements(e.g.,scheduling,error handling),thereby improving the modularity and development efficiency of embedded systems.Furthermore,the dynamic behaviours of embedded systems are simulated and analysed to determine whether the model satisfies certain properties(e.g.,liveness,safety)described by computation tree logic formulae.Finally,a given case demonstrates some desired properties processed with respect to the aspect-oriented timed statecharts model.

SAP S/4HANA Embedded Analytics: An Overview

Operational analytics is all about answering business questions while doing business and supporting business users across the organization, from shop floor users to management and executives. Therefore, business transactions and analytics must co-exist together in a single platform to empower business users to drive insights, make decisions, and complete business processes in a single application and using a single source of facts without toggling between multiple applications. Traditionally transactional systems and analytics were maintained separately to improve throughput of the transactional system and that certainly introduced latency in decision making. However, with innovation in the SAP HANA platform, SAP S/4HANA embedded analytics enables business users, business analysts, and management to perform real-time analytics on live transactional data. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics.

Fraud detection on payment transaction networks via graph computing and visualization

With the fast development of Internet technology,more and more payments are fulfilled by mobile Apps in an electrical way which significantly saves time and efforts for payment.Such a change has benefited a large number of individual users as well as merchants,and a few major players for payment service have emerged in China.As a result,the payment service competition becomes even fierce,and various promotion activities have been launched for attracting more users by the payment service providers.In this paper,the problem focused on is fraud payment detection,which in fact has been a major concern for the providers who spend a significant amount of money to popularize their payment tools.This paper tries the graph computing-based visualization to the behavior of transactions occuring between the individual users and merchants.Specifically,a network analysisbased pipeline has been built.It consists of the following key components:transaction network building based on daily records aggregation;transaction network filtering based on edge and node removal;transaction network decomposition by community detection;detected transaction community visualization.The proposed approach is verified on the real-world dataset collected from the major player in the payment market in Asia and the qualitative results show the efficiency of the method.

Agent-Oriented Architecture for Ubiquitous Computing in Smart Hyperspace

Agent-oriented approach is increasingly showing its magic power in a diversity of fields, specifically, ubiquitous computing and smart environment. Meanwhile, it is considered the next creative issue is to interconnect and integrate isolated smart spaces in real world together into a higher level space known as a hyperspace. In this paper, an agent-oriented architecture, which involves the techniques of mobile agents, middleware, and embedded artificial intelligence, is proposed. Detailed implementations describe our efforts on the design of terminal device, user interface, agents, and AI

Evaluation of the Application Benefit of Meteorological High Performance Computing Resources

The meteorological high-performance computing resource is the support platform for the weather forecast and climate prediction numerical model operation. The scientific and objective method to evaluate the application of meteorological high-performance computing resources can not only provide reference for the optimization of active resources, but also provide a quantitative basis for future resource construction and planning. In this paper, the concept of the utility value B and index compliance rate E of the meteorological high performance computing system are presented. The evaluation process, evaluation index and calculation method of the high performance computing resource application benefits are introduced.

A Multilayer Security Framework for Cloud Computing in Internet of Things (IoT) Domain

Cloud computing is a type of emerging computing technology that relies on shared computing resources rather than having local servers or personal devices to handle applications. It is an emerging technology that provides services over the internet: Utilizing the online services of different software. Many works have been carried out and various security frameworks relating to the security issues of cloud computing have been proposed in numerous ways. But they do not propose a quantitative approach to analyze and evaluate privacy and security in cloud computing systems. In this research, we try to introduce top security concerns of cloud computing systems, analyze the threats and propose some countermeasures for them. We use a quantitative security risk assessment model to present a multilayer security framework for the solution of the security threats of cloud computing systems. For evaluating the performance of the proposed security framework we have utilized an Own-Cloud platform using a 64-bit quad-core processor based embedded system. Own-Cloud platform is quite literally as any analytics, machine learning algorithms or signal processing techniques can be implemented using the vast variety of Python libraries built for those purposes. In addition, we have proposed two algorithms, which have been deployed in the Own-Cloud for mitigating the attacks and threats to cloud-like reply attacks, DoS/DDoS, back door attacks, Zombie, etc. Moreover, unbalanced RSA based encryption is used to reduce the risk of authentication and authorization. This framework is able to mitigate the targeted attacks satisfactorily.

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers,providers and the workers. Requisition for Edge Computing based items havebeen increasing tremendously. Apart from the advantages it holds, there remainlots of objections and restrictions, which hinders it from accomplishing the needof consumers all around the world. Some of the limitations are constraints oncomputing and hardware, functions and accessibility, remote administration andconnectivity. There is also a backlog in security due to its inability to create a trustbetween devices involved in encryption and decryption. This is because securityof data greatly depends upon faster encryption and decryption in order to transferit. In addition, its devices are considerably exposed to side channel attacks,including Power Analysis attacks that are capable of overturning the process.Constrained space and the ability of it is one of the most challenging tasks. Toprevail over from this issue we are proposing a Cryptographic LightweightEncryption Algorithm with Dimensionality Reduction in Edge Computing. Thet-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. Thethree dimensional image data obtained from the system, which are connected withit, are dimensionally reduced, and then lightweight encryption algorithm isemployed. Hence, the security backlog can be solved effectively using thismethod.

API Development Increases Access to Shared Computing Resources at Boston University

Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must be able to access computing resources. One way to provide such resources is through High-Performance Computing (HPC) centers. Many academic research institutions offer their own HPC Centers but struggle to make the computing resources easily accessible and user-friendly. Here we present SHABU, a RESTful Web API framework that enables S & E communities to access resources from Boston University’s Shared Computing Center (SCC). The SHABU requirements are derived from the use cases described in this work.

Evaluation of embedded modular branched stent graft in treating aortic arch aneurysm using imaging-based computational flow analysis

Embedded modular branched stent graft(EMBSG)was a new option for aortic arch aneurysm.However,the therapeutic effect of this innovative stenting technique has not been fully assessed.Computational fluid dynamics and three-dimensional structural analyses were performed on three patients(Patient Ⅰ,Patient Ⅱ and Patient Ⅲ)with aortic arch aneurysm,both before and after EMBSG implantation.Patient-specific alterations from preoperative to postoperative were analyzed via morphological and functional metrics.Patient Ⅰ and Patient Ⅱ showed notable curvature changes and area reduction after intervention procedure.Three patients showed an increase in flow velocity after EMBSG implantation,while the pressure drop from ascending aorta to the aortic arch was remarkable in Patient I and Patient Ⅱ with the value of 7.09mmHg,and 10.95mmHg,respectively.Patient I and Patient Ⅱ also showed elevated time-averaged wall shear stress(TAWSS)in the stenting region,while Patient Ⅲ showed a trivial change in TAWSS after intervention procedure.Three patients showed low relative residence time after EMBSG insertion.The short-term results of EMBSG in treating aortic arch aneurysm were promising.Hemodynamic parameters have the potential to assist in the outcome evaluation and might be used to guide the stent graft design and wise selection,thereby improving the long-term therapeutic effect in managing complex vascular disease.

New prospects for computational hydraulics by leveraging high-performance heterogeneous computing techniques

In the last two decades, computational hydraulics has undergone a rapid development following the advancement of data acquisition and computing technologies. Using a finite-volume Godunov-type hydrodynamic model, this work demonstrates the promise of modern high-performance computing technology to achieve real-time flood modeling at a regional scale. The software is implemented for high-performance heterogeneous computing using the OpenCL programming framework, and developed to support simulations across multiple GPUs using a domain decomposition technique and across multiple systems through an efficient implementation of the Message Passing Interface （MPI） standard. The software is applied for a convective storm induced flood event in Newcastle upon Tyne, demonstrating high computational performance across a GPU cluster, and good agreement against crowd- sourced observations. Issues relating to data availability, complex urban topography and differences in drainage capacity affect results for a small number of areas.