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检索条件"任意字段=IEEE 27th International Symposium on Computer Architecture and High Performance Computing"

共 1870 条记录，以下是211-220 订阅

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Hierarchical Heterogeneous Cluster Systems for Scalable Distributed Deep Learning

Hierarchical Heterogeneous Cluster Systems for Scalable Dist...

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international symposium on Object-Oriented Real-Time Distributed computing

作者： Yibo Wang Tongsheng Geng Ericson Silva Jean-Luc Gaudiot Electrical Engineering and Computer Science University of California Irvine Irvine USA Electrical Engineering and Computer Science Pontifical Catholic University of Minas Gerais Minas Gerais Brazil

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

Distributed deep learning framework tools should aim at high efficiency of training and inference of distributed exascale deep learning algorithms. there are three major challenges in this endeavor: scalability, adaptivity and efficiency. Any future framework will need to be adaptively utilized for a variety of heterogeneous hardware and network environments and will thus be required to be capable of scaling from single compute node up to large clusters. Further, it should be efficiently integrated into popular frameworks such as TensorFlow, PyTorch, etc. this paper proposes a dynamically hybrid (hierarchy) distribution structure for distributed deep learning, taking advantage of flexible synchronization on both centralized and decentralized architectures, implementing multi-level fine-grain parallelism on distributed platforms. It is scalable as the number of compute nodes increases, and can also adapt to various compute abilities, memory structures and communication costs.

关键词： Deep learning Training Costs Scalability Parallel processing Real-time systems Inference algorithms

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DYAD: Locality-aware Data Management for accelerating Deep Learning Training

DYAD: Locality-aware Data Management for accelerating Deep L...

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international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Hariharan Devarajan Ian Lumsden Chen Wang Konstantia Georgouli Tom Scogland Jae-Seung Yeom Michela Taufer Lawrence Livermore National Laboratory University of Tennessee Knoxville

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

Deep Learning (DL) is increasingly applied across various fields to solve complex scientific challenges in modern high-performance computing (HPC) systems that are beyond the reach of traditional algorithms. Training DL models for scientific applications involves processing multi-terabyte datasets in each epoch. the data access behavior during DL training exposes optimization opportunities to cache these datasets in near-compute storage accelerators in HPC systems, enhancing I/O throughput. However, current middleware solutions employ near-compute storage accelerators primarily as exclusive caches, which limits the effectiveness of cache access locality. To address this problem, we introduce DYAD, a system designed to maximize sample locality in the cache, thereby significantly increasing I/O throughput in HPC *** optimizes I/O for DL training based on three key features. First, DYAD boosts inter-node access speeds by using a novel streaming RPC with RDMA protocol, achieving a 1.25x performance gain over state-of-the-art solutions. Second, DYAD further enhances inter-node access by coordinating data movement, which mitigates network congestion and increases throughput for inter-node accesses by up to 8.78x. Last, DYAD uses smart metadata caching that outperforms traditional global metadata access methods by several orders of magnitude in terms of lookup throughput. We demonstrate how DYAD accelerates large-scale DL training on a high-end HPC cluster with 512 GPUs by up to 10.82x faster epochs compared to UnifyFS by performing locality-aware caching on near-compute storage accelerators.

关键词： Training Deep learning Protocols high performance computing Redundancy Termination of employment Metadata performance gain throughput Resource management

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Hyacinth macaw: a project-based learning program to develop talents in Software Engineering for Artificial Intelligence 23

Hyacinth macaw: a project-based learning program to develop ...

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37th Brazilian symposium on Software Engineering, SBES 2023, held in conjunction with the Brazilian Conference on Software: theory and Practice, CBSoft 2023

作者： Fontão, Awdren Matsubara, Edson Mongelli, Henrique Medeiros, Marcio Lourenço, Carlos Martins, Henrique Cortez, Igor Borges, Maria Faculty of Computing / Software Engineering Lab Federal University of Mato Grosso Do Sul Brazil PDtec SP Brazil B3 SP Brazil Blk SP Brazil Neoway SP Brazil

ISBN: (纸本)9798400707872

Software Engineering for Artificial Intelligence (SE4A) uses SE principles to design and maintain AI systems, requiring analytical thinking for software complexity, while AI demands mathematical knowledge and algorithm adjustment. the ieee Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering states that extracurricular elements impact students' preparation. this study focuses on the first module of a project-based learning talent development program involving undergraduate students, two expert professors (in AI and SE), and mentors from sponsoring companies. An exploratory case study with 39 students from four courses was conducted, challenging them to deliver an MVP in machine learning within 1.5 months. Results showed high agreement (87.5%) in applying learned skills to future projects, recognizing SE's benefits (96.9%) in AI, and acknowledging the connection between SE and AI (78.1%). Participants applied relevant knowledge in ML performance, data analysis, and software architecture for AI. We share strategies used by students to enhance developer experience. © 2023 ACM.

关键词： Students

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Adaptive Multi-Layered Non-Terrestrial Network with Integrated FSO and RF Communications for Enhanced Global Connectivity

Adaptive Multi-Layered Non-Terrestrial Network with Integrat...

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ACIS international Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Osei Wusu Brempong Jnr Okechukwu Clement Agomuo Junaid Hussain Muzamal Khalert school of computing University of Utah Salt Lake City Utah USA dept. of Operations and Information Systems University of Utah Salt Lake City Utah USA Fast School of Computing Fast - NUCES Lahore Pakistan

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

this paper introduces the Adaptive Multi-Layered Non-Terrestrial Network (AMLT-NTN), an architecture that integrates satellite, high Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). It leverages a combination of Free-Space Optical (FSO) and Radio Frequency (RF) communications, tailored for specific operational altitudes to enhance connectivity in remote and disaster-stricken regions. the AMLT-NTN tackles the complexities of dynamic power allocation and link selection by incorporating real-time optimization algorithms. this significantly boosted the network’s robustness and adaptability to environmental challenges and demand fluctuations. Simulations in OMNeT++ highlighted a quantifiable enhancement, with up to a 30% increase in throughput and a 40% decrease in latency, outstripping conventional NTN. the AMLT-NTN architecture demonstrates unparalleled resilience, consistently delivering high service levels across various conditions. Looking ahead, this research paves the way for integrating emerging communication technologies and scaling the architecture for widespread adoption. the proposed AMLT-NTN offers transformative solutions for rural connectivity and rapid disaster response, thus poised to impact global digital inclusion.

关键词： Radio frequency Fluctuations Adaptive systems Heuristic algorithms Software algorithms computer architecture throughput

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A Heterogeneous Redundant architecture for Industrial Control System Security

A Heterogeneous Redundant Architecture for Industrial Contro...

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Pacific Rim international symposium on Dependable computing

作者： Zhihao Dai Matthew Leeke Yulong Ding Shuang-hua Yang Department of Computer Science University of Warwick Coventry UK Department of Computer Science and Engineering Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and Technology Shenzhen China

Component-level heterogeneous redundancy is gaining popularity as an approach for preventing single-point security breaches in Industrial Control Systems (ICSs), especially with regard to core components such as Programmable Logic Controllers (PLCs). To take control of a system with component-level heterogeneous redundancy, an adversary must uncover and concurrently exploit vulnerabilities across multiple versions of hardened components. As such, attackers incur increased costs and delays when seeking to launch a successful attack. Existing approaches advocate attack resilience via pairwise comparison among outputs from multiple PLCs. these approaches incur increased resource costs due to them having a high degree of redundancy and do not address concurrent attacks. In this paper we address both issues, demonstrating a data-driven component selection approach that achieves a trade-off between resources cost and security. In particular, we propose (i) a novel dual-PLC ICS architecture with native pairwise comparison which can offer limited yet comparable defence against single-point breaches, (ii) a machine-learning based selection mechanisms which can deliver resilience against non-concurrent attacks under resource constraints, (iii) a scaled up variant of the proposed architecture to counteract concurrent attacks with modest resource implications.

关键词： Integrated circuits Costs Industrial control Redundancy Programmable logic devices computer architecture Machine learning

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Hybrid Solver Techniques for Wideband Antennas

Hybrid Solver Techniques for Wideband Antennas

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international symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Venkat Reddy Kandregula Pavlos I. Lazaridis Qasim Zeeshan Ahmed Faheem A. Khan Zaharias D. Zaharis Vladimir Poulkov School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Faculty of Telecommunications Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350392319

ISBN: (纸本)9798350356137

Evaluating the performance of an antenna using commercially available electromagnetic (EM) solvers such as computer simulation technology (CST) studio suite has become crucial. In this context, a unidirectional advanced technique is used for simulating a Printed Log-Periodic Antenna (PLPDA) enclosed in a fixture with foam. this method employs a hybrid solver approach, in which the PLPDA with foam (source) is simulated using a transient solver, and the fixture (platform) is simulated using an integral equation solver. the proposed technique significantly reduces the simulation time and RAM usage while maintaining high accuracy. It is a viable alternative to traditional full-wave analysis, where the source and platform are simulated using a single solver or bidirectional simulation techniques. A comparative analysis with measured radiation patterns demonstrates the effectiveness of this method, providing a reliable and efficient solution for antenna design and performance evaluation.

关键词： performance evaluation Antenna measurements Wireless communication Time-frequency analysis Accuracy Fixtures Random access memory Reliability engineering Computational efficiency Antenna radiation patterns

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JANUS: A Simple and Efficient Speculative Defense using Reinforcement Learning

JANUS: A Simple and Efficient Speculative Defense using Rein...

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international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Pavlos Aimoniotis Stefanos Kaxiras Uppsala University Uppsala Sweden

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

Speculative execution and the emergence of Spectre attacks have forced architects to rethink how microprocessors are designed. Several approaches aim to close this security vulnerability while trying to minimize performance degradation, often involving complex and sophisticated mechanisms. these strategies typically entail substantial modifications to the processor core and memory hierarchy, which ultimately inhibit their adoption in real *** this work, we leverage two of the simplest speculative defense ideas, NDA and DoM, that can co-exist in the same core, and we apply a simple form of Reinforcement Learning (RL) to select the most effective mechanism, as the underlying processor defense, for a window of execution. NDA forbids the propagation of a potential secret to subsequent instructions while DoM prohibits the creation of observable timing differences in the cache. We observe that their impact on different applications can vary significantly, but, often, they can complement each other within the same application. However, our investigation also reveals vulnerabilities in previous proposals that try to combine these secure speculation schemes into one. We demonstrate an attack scenario that violates the security of the combined scheme and we present the conditions that must hold to safely combine them. Lastly, while the cost and complexity of reinforcement learning may seem inordinately high for microarchitectural implementations, we build on recent research that demonstrates remarkably lightweight solutions, provided that the action space is *** present JANUS, a lightweight architecture leveraging an RL agent based on a two-armed bandit algorithm. JANUS selects the optimal, performance-wise, defense mechanism that protects the processor within a specific time window. We evaluate JANUS on SPEC2017 benchmark suite and find that it outperforms NDA by +4.9%, STT (a more sophisticated and complex scheme that uses taint tracking) by +1%, and DoM by +2.6

关键词： threat modeling Reinforcement learning computer architecture Side-channel attacks Benchmark testing Timing Security Proposals Protection Optimization

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TECS/Rust: Memory-safe Component Framework for Embedded Systems

TECS/Rust: Memory-safe Component Framework for Embedded Syst...

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international symposium on Object-Oriented Real-Time Distributed computing

作者： Nao Yoshimura Hiroshi Oyama Takuya Azumi Graduate School of Science and Engineering Saitama University OKUMA Corporation

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

As embedded systems grow in complexity and scale due to increased functional diversity, component-based development (CBD) emerges as a solution to streamline their architecture and enhance functionality reuse. CBD typically utilizes the C programming language for its direct hardware access and low-level operations, despite its susceptibility to memory-related issues. To address these concerns, this paper proposes TOPPERS Embedded Component Systems/Rust (TECS/Rust), a Rust-based framework specifically designed for TECS, which is a component framework for embedded systems. It leverages Rust’s compile-time memory-safe features, such as lifetime and borrowing, to mitigate memory vulnerabilities common with C. the proposed framework not only ensures memory safety but also maintains the flexibility of CBD, automates Rust code generation for CBD components, and supports efficient integration with real-time operating systems. An evaluation of the amount of generated code indicates that the code generated by this paper framework accounts for a large percentage of the actual code. Compared to code developed without the proposed framework, the difference in execution time is minimal, indicating that the overhead introduced by the proposed framework is negligible.

关键词： computer languages Codes Embedded systems Memory management Real-time systems Hardware Safety

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vASP: Full VM Life-cycle Protection Based on Active Security Processor architecture

vASP: Full VM Life-cycle Protection Based on Active Security...

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ieee/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Jiayun Chen Qihang Zhou Weijuan Zhang Nan Jiang Yamin Xie Xiaoqi Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Cloud computing has been applied on a large scale due to its competitive advantages. However, the introduction of virtualization brings new risks, which can come from within the VM and the host. Due to the abstraction of hardware resources by the hypervisor, traditional trusted computing methods, such as TPM and ASP, are no longer available in cloud environments. Existing work focusing on enabling trusted computing in cloud computing is primarily based on TPM and vTPM, but there are still issues such as the trusted chain not covering all stages of the VM life cycle and the integrity measurement operation potentially causing high overhead. In this paper, we present the vASP architecture, which solves the limitation of the ASP architecture in a cloud environment. Using customization features provided by the ASP, we customize interfaces for the vASP architecture and pass the trusted relationship to the upper layer to form a complete chain of trust. the vASP front-end plugs into the hypervisor actively and regularly operates the dynamic measurement process of the guest to ensure that data from the guest machine are not tampered with. With the introduction of vASP in the cloud computing platform, the security of vASP components during VM operation is also a concern. As a result, we propose a full VM life-cycle protection method through verification and measurement mechanisms that cannot be bypassed to ensure that vASP maintains a match with specific VMs. We have implemented the vASP architecture on a commercial platform deployed with ASP architecture and evaluated it. the result shows that the vASP architecture can protect VM integrity well during full life-cycle and has very low overhead compared to the native virtualization architecture.

关键词： performance evaluation Cloud computing Trusted computing Virtual machine monitors Prototypes computer architecture Security Protection Virtualization Plugs

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27th international Workshop on high-Level Parallel Programming Models and Supportive Environments (HIPS 2022)

27th International Workshop on High-Level Parallel Programmi...

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ieee international symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

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