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检索条件"任意字段=2024 International Symposium on Computer Architecture and High Performance Computing Workshops"

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Federated Learning on Virtual Reality Environments: performance Analysis on Standalone Devices 4th

Federated Learning on Virtual Reality Environments: Performa...

引用

24th international Conference Web Engineering (ICWE)

作者： Flores-Martin, Daniel Diaz-Barrancas, Francisco Pardo, Pedro J. Berrocal, Javier Murillo, Juan M. COMPUTAEX Extremadura Supercomp Ctr Caceres Spain Univ Extremadura Badajoz Spain

ISBN: (纸本)9783031751097;9783031751103

Training algorithms through Federated Learning has emerged as a promising strategy to safeguard data privacy in distributed environments. This training can be performed on several devices, ranging from high-capacity servers to devices with limited capabilities. However, handling numerous data sources can overload these devices, especially low-power ones, increasing response time. A particular scenario is Virtual Reality, as it requires connection to multiple data sources where latency is critical. Virtual Reality devices have traditionally required a continuous connection to computer equipment, limiting their versatility and the advantages of wireless devices. Recent technological advancements in these devices have increased their computational capabilities, enabling them to perform certain tasks independently. This work addresses the challenge of training a neural network on Virtual Reality devices through a federated system, to develop an enriched collaborative model stored and aggregated in the Cloud. The objective is to evaluate the computational costs and discern the possibilities and limitations of Virtual Reality in Artificial Intelligence.

关键词： Virtual Reality Federated Learning Neural Networks Training Cloud computing

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A Communication-efficient Collaborative Task Offloading Method Based on Distributed computing Cloud 22

A Communication-efficient Collaborative Task Offloading Meth...

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22nd IEEE international symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Ke Zong, Ruixing Song, Yingjie Zhou, Yong Xiao, Xiong Hunan University Changsha China

ISBN: (纸本)9798331509712

An interconnected distributed computing cloud, comprising network-linked computational nodes, facilitates the allocation of computational resources across diverse locations. The strategic offloading of computationally demanding tasks from resource-constrained nodes to proximate nodes endowed with ample resources can enhance the resource utilization within the computing cloud. Given the interdependencies among segmented tasks, improper task partitioning may engender supplementary overheads. Furthermore, the process of task offloading introduces additional communication overhead. Consequently, the judicious division of task components and the selection of appropriate offloading destinations assume paramount significance in mitigating collaborative computing complexity and communication overhead. To address these challenges, we propose a communication-efficient collaborative task offloading methodology, which entails the segmentation and offloading of computation-intensive tasks from the source node to one or more high-performance nodes to fully leverage device resources. Additionally, we devise a subtask placement strategy predicated on minimizing communication distances, whereby multiple subtasks are allocated to distinct nodes based on the calculated communication latencies between the source node and various computing nodes. Experiments demonstrate that our task offloading method exhibits the lowest overall loss. © 2024 IEEE.

关键词： Distributed cloud

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Automatic Image Acquisition and Matching in Control System of Near-Field Beam Shaping 8

Automatic Image Acquisition and Matching in Control System o...

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8th international symposium on computer Science and Intelligent Control, ISCSIC 2024

作者： Li, Suicheng Shen, Wenwen He, Zizhou Zhou, Chen Li, Li Luo, Jun Institute of Computer Application Caep Department of Computer Engineering and Science Mianyang China

ISBN: (纸本)9798350380286

In the high power laser system of inertial Confinement fusion (ICF), in order to improve the safe operation flux of the system, near-field beam shaping is necessary to output the laser beam with uniform and smooth intensity distribution. Therefore, how to use optical control systems to automatically and efficiently accomplish near-field beamforming is an important issue. The optical control system based on liquid crystal spatial light modulator can control the beam shape and intensity distribution in real time. The control system of near-field beam shaping algorithm mainly includes the modules of light path control, image loading on liquid crystal spatial light modulator, Charge Coupled Device (CCD) nearfield light spot perception and image processing. There is a lot of data exchange between those modules. In the laser system, the manual operation mainly involves two parts: one is the interaction and data exchange between different modules. In the past work, the image data is collected by the operator on different devices, and then the compensation image is calculated on the computing device, and finally loaded into the display device. The whole process is complex, tedious and repetitive. The other is the image matching of input image on spatial light modulator and near-field light spot. This work requires staff to manually match feature points on computing equipment, and the accuracy of matching is difficult to guarantee, which is very time-consuming and labor-intensive. This paper will address the above problems and propose a distributed network architecture to connect different devices in near-field beam shaping and carry out image acquisition from them. In addition, a new image matching algorithm is proposed to extract and match the feature points of input image on spatial light modulator and near-field light spot. It enables the compensation algorithm to be completed automatically to avoid manual operation. © 2024 IEEE.

关键词： Light modulators

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Evaluation Model and performance Analysis of NIC Aggregations in Containerized Private Clouds

Evaluation Model and Performance Analysis of NIC Aggregation...

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

作者： Anderson M. Maliszewski Dalvan Griebler Eduardo Roloff Rodrigo da Rosa Righi Philippe O.A. Navaux Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Laboratory of Advanced Research on Cloud Computing (LARCC) Três de Maio Faculty (SETREM) Três de Maio Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil Software Innovation Lab (SoftwareLab) University of Vale do Rio dos Sinos (UNISINOS) São Leopoldo Brazi

The availability of computational resources changed significantly due to cloud computing. In addition, we have witnessed efforts to execute high-performance computing (HPC) applications in the cloud attracted by the advantages of cost savings and scalable/elastic resource allocation. Allocating more powerful hardware and exclusivity allocating resources such as memory, storage, and CPU can improve performance in the cloud. For network interconnection, significant noise, and other inferences are generated by several simultaneous instances (multitenants) communicating using the same network. As increasing the network bandwidth may be an alternative, we designed an evaluation model, and performance analysis of NIC aggregation approaches in containerized private clouds. The experiments using NAS Parallel Benchmarks revealed that NIC aggregation approach outperforms the baseline up to ≈98% of the executions with applications characterized by intensive network use. Also, the Balance Round-Robin aggregation mode performed better than the 802.3ad aggregation mode in most assessments.

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The Impact of Asynchronous I/O in Checkpoint-Restart Workloads

The Impact of Asynchronous I/O in Checkpoint-Restart Workloa...

引用

IEEE international symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： Hariharan Devarajan Adam Moody Donglai Dai Cameron Stanavige Elsa Gonsiorowski Marty McFadden Olaf Faaland Greg Kosinovsky Kathryn Mohror Lawrence Livermore National Laboratory X-ScaleSolutions

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

With increasing data production from scientific checkpoint-restart (CR) workloads, data management libraries utilize asynchronous I/O operations to hide the cost of accessing global file systems in high-performance computing (HPC) systems. However, the characteristics of asynchronous operation, such as software and hardware architecture and workload features, dictate the impact of asynchronous I/O operations on checkpoint-restart application resources and asynchronous I/O performance. In this work, we aim to study the impact of these design choices on CR performance, such as CPU, memory, and checkpointing, while also considering the performance of asynchronous I/O operation and the performance variability introduced in the workload. We observe the following three main findings. First, the architecture used for asynchronous I/O must be flexible to manage its resources to reduce application interference while maximizing asynchronous I/O performance. Second, modern MPI-aware schedulers apply an implicit core affinity to each workload, which is helpful for compute-bound applications but can significantly reduce checkpointing performance. Finally, asynchronous I/O designs need to consider the cost of communication between the service and application to reduce the overhead of checkpointing operations. Through this study, we aim to pave the path for future middleware libraries to adopt different designs based on their target workload and HPC system architecture.

关键词： Checkpointing performance evaluation Costs Scalability Systems architecture Interference computer architecture

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HiPC 2024: 16th Student Research symposium (SRS)

HiPC 2024: 16th Student Research Symposium (SRS)

引用

IEEE international Conference on high performance computing workshops (HiPCW)

作者： Abhinandan S Prasad Konduri Aditya Indian Institute of Technology Ropar India Indian Institute of Science India

Description:

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

Description:

关键词： high performance computing Conferences

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Special Session: Security and RAS in the computing Continuum 37

Special Session: Security and RAS in the Computing Continuum

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37th IEEE international symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2024

作者： Alonso, Marti Andreu, David Canal, Ramon Di Carlo, Stefano Chatzopoulos, Odysseas Chenet, Cristiano Costa, Juanjo Girones, Andreu Gizopoulos, Dimitris Papadimitriou, George Morancho, Enric Otero, Beatriz Savino, Alessandro Universitat Politècnica de Catalunya Barcelona Spain Politecnico di Torino Torino Italy University of Athens Athens Greece

ISBN: (纸本)9798350366884

Security and RAS are two non-functional requirements under focus for current systems developed for the computing continuum. Due to the increased number of interconnected computer systems across the continuum, security becomes especially pervasive at all levels, from the smallest edge device to the high-performance cloud at the other end. Similarly, RAS (Reliability, Availability, and Serviceability) ensures the robustness of a system towards hardware defects. Namely, making them reliable, with high availability and design for easy service. In this paper and as a result of the Vitamin-V Eu project, the authors detail the comprehensive approach to malware and hardware attack detection;as well as, the RAS features envisioned for future systems across the computing continuum. © 2024 IEEE.

关键词： Cloud security

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high-level GPU code: a case study examining JAX and OpenMP.

High-level GPU code: a case study examining JAX and OpenMP.

引用

2023 international Conference on high performance computing, Network, Storage, and Analysis, SC workshops 2023

作者： Demeure, Nestor Kisner, Theodore Keskitalo, Reijo Thomas, Rollin Borrill, Julian Bhimji, Wahid National Energy Research Scientific Computing Center BerkeleyCA United States Computational Cosmology Center BerkeleyCA United States

ISBN: (纸本)9798400707858

In recent years, a new scientific software design pattern has emerged, pairing a Python interface with high-performance kernels in lower-level languages. The rise of general-purpose GPUs necessitates the rewriting of many such kernels, which poses challenges in GPU programming and ensures future portability and flexibility. This paper documents our experience and observations during the process of porting TOAST, a cosmology software framework designed to take full advantage of a supercomputer, to work with GPUs. This exploration led us to compare two different porting strategies: utilizing the JAX Python library and employing OpenMP Target Offload compiler directives. JAX allows kernel code to be written in pure Python, whereas OpenMP Target Offload is a directive-based strategy that integrates seamlessly with our existing OpenMP-accelerated C++ kernels. Both frameworks are high-level, abstracting system architecture details while aiming for straightforward, portable, yet performant GPU code. Through the porting of a dozen kernels, we delve into the analysis of development cost, performance, and the viability of employing either of these frameworks for complex numerical Python applications. © 2023 Owner/Author.

关键词： Python

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Structured Design of Multilevel Simulation Models 28

Structured Design of Multilevel Simulation Models

引用

28th IEEE/ACM international symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Serena, Luca Marzolla, Moreno D'angelo, Gabriele University of Bologna Department of Computer Science and Engineering Italy University of Bologna Center for Inter Department Industrial Research ICT Italy

ISBN: (纸本)9798331527211

Multilevel modeling is a design technique that allows complex models to be specified in terms of simpler ones. An obvious advantage of multilevel modeling is the possibility of reusing existing models, therefore reducing development cost. However, multilevel modeling entails more than the mere application of the ubiquitous principle of decomposition. Indeed, in a multilevel model the same entity may be described at different levels of detail during execution. This allows, for example, to focus on areas of interest by switching to a more detailed model, and revert back to a faster but less accurate one when possible. In this paper we propose GEMMA (GEneric Multilevel Modeling Abstraction), a methodology inspired by DEVS that can help the designer to produce sound and correct multilevel models. We propose a practical realization of GEMMA as a concrete software architecture that can be instantiated in different programming languages. We illustrate a simple use case where different sub-models written in different programming languages are combined according to the GEMMA methodology. Our ultimate goal is to bridge the gap between theory and practice of multilevel modeling, to foster a broader adoption of this useful technique. © 2024 IEEE.

关键词： Digital elevation model

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AI-Driven Virtual Mock Interview Development 13

AI-Driven Virtual Mock Interview Development

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Joint 13th international Conference on Soft computing and Intelligent Systems and 25th international symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mishra, Prabhat Kumar Arulappan, Arun Kumar Ra, In-Ho Thanga Mariappan, L. Gina Rose, G. Lee, Young-Seok School of Computer Science Engineering and Information Systems Vit University Vellore India School of Computer and Software Kunsan National University Gunsan Korea Republic of Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350373332

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from 3000 to just 300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners. © 2024 IEEE.

关键词： Cost reduction

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