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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Cerin, Christophe Greneche, Nicolas Menouer, Tarek Univ Sorbonne Paris Nord LIPN UMR CNRS 7030 Villetaneuse France Umanis Res & Innovat Levallois Perret France

ISBN: (纸本)9781728199245

In cloud computing, elasticity is defined as "the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible". Adding elasticity to HPC (high performance computing) clusters management systems remains challenging even if we deploy such HPC systems in today's cloud environments. this difficulty is caused by the fact that HPC jobs scheduler needs to rely on a fixed set of resources. Every change of topology (adding or removing computing resources) leads to a global restart of the HPC jobs scheduler. this phenomenon is not a major drawback because it provides a very effective way of sharing a fixed set of resources but we think that it could be complemented by a more elastic approach. Moreover, the elasticity issue should not be reduced to the scaling of resources issues. Clouds also enable access to various technologies that enhance the services offer to users. In this paper, our approach is to use containers technology to instantiate a tailored HPC environment based on the user's reservation constraints. We claim that the introduction and use of containers in HPC job schedulers allow better management of resources, in a more economical way. From the use case of SLURM, we release a methodology for 'containerization' of HPC jobs schedulers which is pervasive i.e. spreading widely throughout any layers of job schedulers. We also provide initial experiments demonstrating that our containerized SLURM system is operational and promising.

关键词： HPC and cloud computing Containers Security Operating system / middleware layers

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Development and Practical Application of Methods for Detecting Similar Supercomputer Jobs 15th

Development and Practical Application of Methods for Detecti...

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15th International Scientific Conference on Parallel Computational Technologies (PCT)

作者： Shaikhislamov, Denis Voevodin, Vadim Lomonosov Moscow State Univ Res Comp Ctr Moscow Russia Moscow Ctr Fundamental & Appl Math Moscow Russia

ISBN: (纸本)9783030816919;9783030816902

In the field of supercomputer technologies, the task of detecting similar applications is poorly developed, despite frequently popping up in the solution of many practically important problems. One of them is the detection of software packages and libraries used in supercomputer applications. In this paper, we address this task. It is worth noting that the task can be partially solved with the help of specialized system software. However, there are many situations when such tools are not enough. We discuss two mutually complementary approaches: the static one, involving the analysis of data obtained by parsing executable files, and the dynamic one, which relies on the analysis of monitoring data during the execution of an application. the analysis of real-life data from the Lomonosov-2 supercomputer confirms that the methods suggested in this paper ensure high accuracy and can be handy in detecting new cases of package usage that can not be identified by other available methods.

关键词： Supercomputers high-performance computing Similar applications Data analysis Software package detection

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Understanding the Strengths and Limitations of AI for Data Access Optimization

Understanding the Strengths and Limitations of AI for Data A...

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International Conference on computing and Networking Technology (ICCNT)

作者： Manpreet Singh Preeti Naval Nagaraj Patil Mohit Tiwari V. Ashok Shilpa M. Katikar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Artificial Intelligence (AI) has emerge as an increasing number of famous in recent years due to its potential to learn from facts, make predictions, and automate decision-making techniques. Within the realm of facts get right of entry to optimization, AI algorithms are getting used to enhance the overall performance and performance of information storage, retrieval, and analysis. but, like every other generation, AI has its personal strengths and boundaries that need to be understood on the way to effectively utilize it for information get entry to *** of the key strengths of AI is its capability to technique and examine massive amounts of facts speedy and as it should be. this is important in records get entry to optimization, wherein the speed and accuracy of records retrieval and evaluation is critical for choice-making. By the use of AI, companies can save time and sources that would in any other case be spent on manual records processing and evaluation. Additionally, AI has the potential to identify hidden patterns and correlations in facts that may not be obvious to humans. this may lead to extra efficient and powerful information get entry to strategies, as AI can find insights and traits which can were omitted through human analysts. this is particularly useful in complicated facts environments with high volumes of statistics.

关键词： Accuracy Correlation Decision making Manuals Companies Prediction algorithms Human in the loop Artificial intelligence Optimization

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ERINS: Novel Sequence Insertion Detection by Constructing an Extended Reference

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第5期18卷 1893-1901页

作者： Yuan, Xiguo Xu, Xiangyan Zhao, Haiyong Duan, Junbo Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Liaocheng Univ Sch Comp Sci & Technol Liaocheng 252000 Shandong Peoples R China Xi An Jiao Tong Univ Sch Life Sci & Technol Key Lab Biomed Informat Engn Minist EducDept Biomed Engn Xian 710049 Peoples R China

Next generation sequencing technology has led to the development of methods for the detection of novel sequence insertions (nsINS). Multiple signatures from short reads are usually extracted to improve nsINS detection performance. However, characterization of nsINSs larger than the mean insert size is still challenging. this article presents a new method, ERINS, to detect nsINS contents and genotypes of full spectrum range size. It integrates the features of structural variations and mapping states of split reads to find nsINS breakpoints, and then adopts a left-most mapping strategy to infer nsINS content by iteratively extending the standard reference at each breakpoint. Finally, it realigns all reads to the extended reference and infers nsINS genotypes through statistical testing on read counts. We test and validate the performance of ERINS on simulation and real sequencing datasets. the simulation experimental results demonstrate that it outperforms several peer methods with respect to sensitivity and precision. the real data application indicates that ERINS obtains high consistent results with those of previously reported and detects nsINSs over 200 base pairs that many other methods fail. In conclusion, ERINS can be used as a supplement to existing tools and will become a routine approach for characterizing nsINSs.

关键词： Genomics Bioinformatics Sequential analysis Feature extraction computer science Sensitivity Tools Novel sequence insertions insertion contents genotypes next-generation sequencing extended reference

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Extending heterogeneous applications to remote co-processors with rOpenCL 32

Extending heterogeneous applications to remote co-processors...

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Alves, Rui Rufino, Jose Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9781728199245

In heterogeneous computing systems, general purpose CPUs are coupled with co-processors of different architectures, like GPUs and FPGAs. Applications may take advantage of this heterogeneous device ensemble to accelerate execution. However, developing heterogeneous applications requires specific programming models, under which applications unfold into code components targeting different computing devices. OpenCL is one of the main programming models for heterogeneous applications, set apart from others due to its openness, vendor independence and support for different co-processors. In the original OpenCL application model, a heterogeneous application starts in a certain host node, and then resorts to the local co-processors attached to that host. therefore, co-processors at other nodes, networked with the host node, are inaccessible and cannot be used to accelerate the application. rOpenCL (remote OpenCL) overcomes this limitation for a significant set of the OpenCL 1.2 API, offering OpenCL applications transparent access to remote devices through a TPC/IP based network. this paper presents the architecture and the most relevant implementation details of rOpenCL, together with the results of a preliminary set of reference benchmarks. these prove the stability of the current prototype and show that, in many scenarios, the network overhead is smaller than expected.

关键词： OpenCL heterogeneous computing API forwarding remote execution parallel and distributed computing

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high-performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution 32

High-Performance Low-Memory Lowering: GEMM-based Algorithms ...

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Anderson, Andrew Vasudevan, Aravind Keane, Cormac Gregg, David Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland

ISBN: (数字)9781728199245

ISBN: (纸本)9781728199245

Deep Neural Network Convolution is often implemented with general matrix multiplication ( GEMM) using the well-known im2col algorithm. this algorithm constructs a Toeplitz matrix from the input feature maps, and multiplies them by the convolutional kernel. With input feature map dimensions C x H x W and kernel dimensions M x C x K-2, im2col requires O(K-2 CHW) additional space. Although this approach is very popular, there has been little study of the associated design space. We show that the im2col algorithm is just one point in a regular design space of algorithms which translate convolution to GEMM. We enumerate this design space, and experimentally evaluate each algorithmic variant. Our evaluation yields several novel low-memory algorithms which match the performance of the best known approaches despite requiring only a small fraction of the additional memory.

关键词： neural networks embedded software performance

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SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering

SPADE: Enhancing Adaptive Cyber Deception Strategies with Ge...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Shihab Ahmed A B M Mohaimenur Rahman Md Morshed Alam Md Sajidul Islam Sajid Department of Computer and Information Sciences Towson University Towson Maryland USA Department of Software and Information Systems UNC Charlotte Charlotte North Carolina USA School of Cybersecurity Old Dominion University Norfolk Virginia USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

the rapid evolution of modern malware presents significant challenges to the development of effective defense mechanisms. Traditional cyber deception techniques often rely on static or manually configured parameters, limiting their adaptability to dynamic and sophisticated threats. this study leverages Generative AI (GenAI) models to automate the creation of adaptive cyber deception ploys, focusing on structured prompt engineering (PE) to enhance relevance, actionability, and deploy-ability. We introduce a systematic framework (SPADE) to address inherent challenges large language models (LLMs) pose to adaptive deceptions, including generalized outputs, ambiguity, under-utilization of contextual information, and scalability constraints. Evaluations across diverse malware scenarios using metrics such as Recall, Exact Match (EM), BLEU Score, and expert quality assessments identified ChatGPT-4o as the top performer. Additionally, it achieved high engagement (93%) and accuracy (96%) with minimal refinements. Gemini and ChatGPT-4o Mini demonstrated competitive performance, with Llama3.2 showing promise despite requiring further optimization. these findings highlight the transformative potential of GenAI in automating scalable, adaptive deception strategies and underscore the critical role of structured PE in advancing real-world cybersecurity applications.

关键词： Measurement Systematics Limiting Scalability Large language models Focusing Malware Quality assessment Prompt engineering Optimization

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HEALth-ML: A Machine Learning Framework for Equity-Driven Public Health Outcome Prediction

HEALTH-ML: A Machine Learning Framework for Equity-Driven Pu...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Jake Ekoniak Marjan Asadinia Department of Mathematics University of California Los Angeles USA Computer Science Department California State University Northridge USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Significant health disparities persist across U.S. counties due to socioeconomic inequalities, environmental challenges, and unequal access to healthcare. Leveraging large health datasets, this study employs machine learning and advanced analytics to classify county health levels-categorized as low, medium, or high-based on environmental, health, and social factors. Emphasizing fairness, we incorporate bias mitigation techniques, ensuring the models avoid reinforcing existing disparities. Our approach includes comprehensive data preprocessing, feature engineering, and employing diverse machine learning algorithms, including Neural Networks, tree-based models, Support Vector Machines, and logistic regression, to capture complex relationships within the data. Results show accuracy scores exceeding 87% and AUC scores over 95%, highlighting the predictive strength of our models. Model performance is evaluated through accuracy, precision, recall, F1-score, and fairness metrics such as Equal Opportunity. Additionally, we assess computational efficiency and interpretability, ensuring the models are both practical and actionable. By providing accurate, fair, and interpretable predictions, this research offers a foundation for data-driven interventions, guiding policymakers toward reducing health inequalities across counties. Our study underscores the ethical imperative of fairness in public health analytics, setting a precedent for prioritizing social equity in future health data research.

关键词： Support vector machines Adaptation models Logistic regression Accuracy Computational modeling Neural networks Machine learning Predictive models Computational efficiency Public healthcare

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Trinity: A General Purpose FHE Accelerator

Trinity: A General Purpose FHE Accelerator

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IEEE/ACM International symposium on Microarchitecture (MICRO)

作者： Xianglong Deng Shengyu Fan Zhicheng Hu Zhuoyu Tian Zihao Yang Jiangrui Yu Dingyuan Cao Dan Meng Rui Hou Meng Li Qian Lou Mingzhe Zhang Key Laboratory of Cyberspace Security Defense Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China University of Electronic Science and Technology of China Chengdu China Institute for Artificial Intelligence Peking University Beijing China School of Integrated Circuits Peking University Beijing China University of Illinois Urbana-Champaign IL USA University of Central Florida Orlando FL USA

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Fully Homomorphic Encryption (FHE) is crucial for privacy-preserving computing, which allows direct computation on encrypted data. While various FHE schemes have been proposed, none of them efficiently support both arithmetic FHE and logic FHE simultaneously. To address this issue, researchers explore the combination of different FHE schemes within a single application and propose algorithms for the conversion between them. Unfortunately, all prior ASIC-based FHE accelerators are designed to support a single FHE scheme, and none of them supports the acceleration for FHE scheme conversion. this necessitates FHE acceleration systems to integrate multiple accelerators for different schemes, leading to increased system complexity and hindering performance enhancement. In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single accelerator. To achieve this goal, we first analyze the theoretical foundations of the aforementioned schemes and highlight their composition from a finite number of arithmetic kernels. then, we investigate the challenges for efficiently supporting these kernels within a unified architecture, which include 1) concurrent support for NTT and FFT, 2) maintaining high hardware utilization across various polynomial lengths, and 3) ensuring consistent performance across diverse arithmetic kernels. To tackle these challenges, we propose a novel FHE accelerator named Trinity, which in-corporates algorithm optimizations, hardware component reuse, and dynamic workload scheduling to enhance the acceleration of CKKS, TFHE, and their conversion scheme. By adaptive select the proper allocation of components for NTT and MAC, Trinity maintains high utilization across NTTs with various polynomial lengths and imbalanced arithmetic workloads. the experiment results show that, for the pure CKKS and TFHE workloads, the performance of our Trinity outp

关键词： Heuristic algorithms computer architecture Dynamic scheduling Hardware Polynomials Cryptography Kernel Homomorphic encryption Optimization Arithmetic

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Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset 15

Detection and tracking hand from FPV: benchmarks and challen...

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15th RIVF International Conference on computing and Communication Technologies (RIVF)

作者： Van-Tien Pham thanh-Hai Tran Hai Vu Viettel High Technol Ind Corp Modelling & Simulat Ctr Hanoi Vietnam Hanoi Univ Sci & Technol Sch Elect & Telecommun Hanoi Vietnam Hanoi Univ Sci & Technol Int Res Inst MICA Hanoi Vietnam

ISBN: (纸本)9781665404358

Egocentric vision is an emerging field of computer vision characterized by the acquisition video from the first person perspective. Particularly, for evaluating upper extremity rehabilitation, egocentric vision offers the ability to quantitatively measure the function of hands used in physical-based exercises. For such applications, hand detection and tracking are the first requirement. In this work, we develop a fully automatic tracking by detection pipeline that firstly extracts hands positions and then tracks hands in consecutive frames. the proposed framework consists of state of the art detectors such as RCNN and YOLO family models coupled with advanced trackers (e.g., SORT and DeepSORT) for tracking task. this paper explores how performance of the stand alone object detection algorithms correlates with overall performance of a tracking by detection system. the experimental results show that detection highly impacts the overall performance. Moreover, this work also proves that the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase potential of the whole system. We also present challenges for new egocentric hand tracking dataset for future works.

关键词： egocentric vision hand detection segmentation tracking

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