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acm SIGCOMM Conference

作者： Alfroy, Thomas Holterbach, Thomas Krenc, Thomas Claffy, K. C. Pelsser, Cristel Univ Strasbourg Strasbourg France Univ Calif San Diego CAIDA San Diego CA USA UCLouvain Louvain Belgium

ISBN: (纸本)9798400706141

BGP data collection platforms as currently architected face fundamental challenges that threaten their long-term sustainability. Inspired by recent work, we analyze, prototype, and evaluate a new optimization paradigm for BGP collection. Our system scales data collection with two components: analyzing redundancy between BGP updates and using it to optimize sampling of the incoming streams of BGP data. An appropriate definition of redundancy across updates depends on the analysis objective. Our contributions include: a survey, measurements, and simulations to demonstrate the limitations of current systems;a general framework and algorithms to assess and remove redundancy in BGP observations;and quantitative analysis of the benefit of our approach in terms of accuracy and coverage for several canonical BGP routing analyses such as hijack detection and topology mapping. Finally, we implement and deploy a new BGP peering collection system that automates peering expansion using our redundancy analytics, which provides a path forward for more thorough evaluation of this approach.

关键词： Internet measurement BGP Routing Security

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Benchmarking and Continuous Performance Monitoring of Ookami, an ARM Fujitsu A64FX Testbed Cluster

Benchmarking and Continuous Performance Monitoring of Ookami...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Simakov, Nikolay A. White, Joseph P. Jones, Matthew D. Siegmann, Eva Wood, Daniel G. Coskun, Firat Harrison, Robert J. SUNY University at Buffalo Center for Computational Research BuffaloNY United States Stony Brook University Institute for Advanced Computational Science Stony BrookNY United States

ISBN: (纸本)9798350355543

Continuous performance monitoring is critical for maintaining optimal performance of High-Performance computing resources. This is especially important for technological test bed systems, in which software updates occur often, and performance degradation in one place can be masked by performance improvement in other places. This paper reports on our experience running continuous performance monitoring on Ookami, an ARM Fujitsu A64FX machine (the first ARM CPU with SVE-512 support) using XDMoD. After over three years of monitoring, we found that the applications and numerical library performance improved the most on the initial release with new technology support, followed by a series of smaller performance gains. Another interesting observation about numeric libraries is that the most invested vendors produce optimized code faster than community codes. © 2024 IEEE.

关键词： Benchmarks High-Performance computing Performance Monitoring Regression Tests

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Ponte Vecchio Across the Atlantic: Single-Node Benchmarking of Two Intel GPU systems

Ponte Vecchio Across the Atlantic: Single-Node Benchmarking ...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Applencourt, Thomas Sadawarte, Aditya Muralidharan, Servesh Bertoni, Colleen Kwack, JaeHyuk Luo, Ye Rangel, Esteban Tramm, John Ghadar, Yasaman Tamerus, Arjen Edsall, Chris Deakin, Tom Argonne National Laboratory Argonne Leadership Computing Facility LemontIL United States University of Bristol School of Computer Science Bristol United Kingdom University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798350355543

Intel Data Center GPU Max 1550, known as Ponte Vecchio (PVC), is a new Intel GPU architecture for high-performance computing. It is the basis of two systems on the June 2024 Top 500 list, Dawn (#51) and Aurora (#2).This work provides micro-benchmarking data on PVCs from which application developers may benefit, shows how the micro-benchmarking results are indicative of mini-app performance on PVC, and demonstrates real applications on large-scale Intel GPU *** quantify the obtainable performance from PVC systems through micro-benchmarking fundamental architectural properties. We evaluate the performance of four mini-apps with known performance characteristics, and two full applications, comparing performance on a node of Aurora and Dawn with a node of NVIDIA H100 GPUs and a node of AMD MI250 GPUs. We show the figure-of-merit of the mini-apps on a single PVC ranges from 0.6-1.8X the performance of an H100, and 0.8-7.5X of a MI250. © 2024 IEEE.

关键词： pvc

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Prediction of the Resource Consumption of Distributed Deep Learning systems

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proceedings of the acm on measurement and analysis of computing systems 2022年第2期6卷 1–25页

作者： Yang, Gyeongsik Shin, Changyong Lee, Jeunghwan Yoo, Yeonho Yoo, Chuck Korea Univ Dept Comp Sci & Engn Seoul 02841 South Korea

The prediction of the resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users how long their training would take and also enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings" such as GPU types and also by "workloads" like deep learning models. Previous studies have aimed to derive or model such a prediction, but they fall short of accommodating the various combinations of settings and workloads together. This study presents Driple that designs graph neural networks to predict the resource consumption of diverse workloads. Driple also designs transfer learning to extend the graph neural networks to adapt to differences in settings. The evaluation results show that Driple can effectively predict a wide range of workloads and settings. At the same time, Driple can efficiently reduce the time required to tailor the prediction for different settings by up to 7.3x.

关键词： distributed deep learning resource prediction training time prediction graph neural networks transfer learning

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Latency-Aware Scheduling for Real-Time Application Support in Edge computing 6

Latency-Aware Scheduling for Real-Time Application Support i...

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6th International Workshop on Edge systems, Analytics and Networking (EdgeSys)

作者： Roebert, Kevin Bornholdt, Heiko Fischer, Mathias Edinger, Janick Univ Hamburg Hamburg Germany

ISBN: (纸本)9798400700828

The relocation of computation from the network core to the edge where data is primarily generated has gained momentum, leading to the emergence of edge computing as a viable solution for low-latency processing. As a result, edge computing has the potential to significantly reduce response times, decrease bandwidth usage, enhance energy efficiency, and offer various other benefits. At the same time, end-user devices do not offer a consistent computing platform and Internet middleboxes severely restrict communication with edge devices. Often, this is circumvented by publicly accessible relay servers, which cause additional latency and render time-critical tasks unviable for offloading. This paper presents an approach capable of addressing the complexities inherent in edge computing and facilitating good decisions regarding latency-aware computation offloading. We conducted several real-world experiments to evaluate our approach and provide valuable data for further research. Our findings show that edge offloading is competitive to cloud and grid offloading, as it effectively reduces latency. Empirical evidence from our research supports that edge computing can offer significant advantages for real-time applications.

关键词： Edge computing Cloud computing Network measurement Latency-awareness

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A Systematic Review of Interaction Design Strategies for Group Recommendation systems

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proceedings of the acm on Human-Computer Interaction 2022年第CSCW2期6卷 1–51页

作者： Alvarado, Oscar Htun, Nyi Nyi Jin, Yucheng Verbert, Katrien KU Leuven Belgium Leuven Belgium Hong Kong Baptist University Hong Kong Hong Kong

systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Therefore, we systematically reviewed the acm, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains;(2) devices chosen to implement the systems;(3) prototype fidelity;(4) strategies for profile transparency, justification, control, and diversity;(5) strategies for group formation and final group consensus;and, (6) evaluation methods applied in user studies during the design process. Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts. © 2022 acm.

关键词： Transparency

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EdgeSys 2023 - proceedings of the 6th International Workshop on Edge systems, Analytics and Networking, Part of EuroSys 2023

EdgeSys 2023 - Proceedings of the 6th International Workshop...

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6th International Workshop on Edge systems, Analytics and Networking, EdgeSys 2023, in conjunction with acm EuroSys 2023

ISBN: (纸本)9798400700828

The proceedings contain 10 papers. The topics discussed include: S-Cache: function caching for serverless edge computing;an autonomous resource management model towards cloud morphing;latency-aware scheduling for real-time application support in edge computing;an evaluation of service mesh frameworks for edge systems;hot under the hood: an analysis of ambient temperature impact on heterogeneous edge platforms;lotus: serverless in-transit data processing for edge-based pub/sub;how to pipeline frame transfer and server inference in edge-assisted AR to optimize AR task accuracy?;cost-aware neural network splitting and dynamic rescheduling for edge intelligence;ESCEPE: early-exit network section-wise model compression using self-distillation and weight clustering;and an empirical study of resource-stressing faults in edge-computing applications.

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NanoCom 2023 - proceedings of the 10th acm International Conference on Nanoscale computing and Communication

NanoCom 2023 - Proceedings of the 10th ACM International Con...

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10th acm International Conference on Nanoscale computing and Communication, NANOCOM 2023

ISBN: (纸本)9798400700347

The proceedings contain 37 papers. The topics discussed include: interfacial instability of liquid interphase improves molecular communication density;the thermal impact of THz signaling in protein nanonetworks;exploration of time reversal for wireless communications within computing packages;frequency analysis of a redox-based molecular-electrical communication channel;proteome fingerprinting as a localization scheme for nanobots;EIDA, a best effort equitable distributed ID assignment mechanism for heterogeneous dense nanonetworks;fine-tuned circuit representation of human vessels through reinforcement learning: a novel digital twin approach for hemodynamics;biophysical model for signal-embedded droplet soaking into 2D cell culture;frequency demodulation with magnetoelectric coreshells: a novel approach to enhanced bio-stimulation;and incoherent feedforward loop as a clock signal for synchronizing signals in biological systems.

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Pan, Haochen Chard, Ryan Zhou, Sicheng Kamatar, Alok Vescovi, Rafael Hayot-Sasson, Valerie Bauer, Andre Gonthier, Maxime Chard, Kyle Foster, Ian Southern University of Science and Technology Department of Computer Science China Illinois Institute of Technology Department of Computer Science ChicagoIL United States University of Chicago Department of Computer Science ChicagoIL United States Argonne National Laboratory Data Science and Learning Division LemontIL United States

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients. © 2024 IEEE.

关键词： Event-driven architecture research automation scientific computing

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Ensemble Simulations on Leadership computing systems

Ensemble Simulations on Leadership Computing Systems

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Georgiadou, Antigoni Monge-Camacho, Henry Sohail, Tanvir Ghosh, Swarnava Parambathu, Arjun Valiya Asthagiri, Dilipkumar N. Bykov, Dmytro Athawale, Tushar Beck, Thomas L. National Center for Computational Sciences Oak RidgeTN United States Oak Ridge National Laboratory Oak RidgeTN United States University of Delaware DE United States

ISBN: (纸本)9798350355543

Scientific productivity can be enhanced through workflow management tools, relieving large High Performance computing (HPC) system users from the tedious tasks of scheduling and designing the complex computational execution of scientific applications. This paper presents a study on the usage of ensemble workflow tools to accelerate science using the Summit and Frontier supercomputing systems. The research aims to connect science domain simulations using Oak Ridge Leadership computing Facility (OLCF) supercomputing platforms with ensemble workflow methods in order to accelerate HPC-enabled discovery and boost scientific impact. We present the coupling, porting and optimization of Radical-Cybertools on three applications: Chroma, NAMD and LAMMPS. The tools augment traditional HPC monolithic runs with a pilot scheduler. Lessons-learned are discussed for physics, biology and materials science applications. We discuss intrinsic limitations of coupling and porting ensemble workflow tools to applications that run on large HPC systems. The origins of technical challenges and their solutions developed during the implementation process are discussed. Data management strategies, OLCF's policies for ensembles, and natively supported workflow tools are also summarized.1 © 2024 IEEE.

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