Resource: Seminar

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Time: 2022-6-27 10:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [talk] Resource:Paper Carnival 2022 , all
    Abstract: Recent advances in network and mobile computing.

Visible Light Communication--Wenliang

[Sensys 2021] CurveLight: An Accurate and Practical Indoor Positioning System

[Sensys 2021] SpiderWeb: Enabling Through-Screen Visible Light Communication

Lora--Kaiwen

[ICNP2022] X-MAC: Achieving High Scalability via Imperfect-Orthogonality Aware Scheduling in LPWAN

Response to Mobility--Luwei

[Infocom2022] Enabling QoE Support for Interactive Applications over Mobile Edge with High User Mobility

[Infocom2022] User Experience Oriented Task Computation for UAV-Assisted MEC System

[TMC2022] ECHO: Efficient Zero-Control-Packet Broadcasting for Mobile Ad Hoc Networks

Mobility--Zhuoliu

[MobiCom21] Visage: enabling timely analytics for drone imagery

Offloading, Delivery--Wenjie

[Infocom2022] An Efficient Two-Layer Task Offloading Scheme for MEC Networks with Multiple Services Providers

[Infocom2022] Two Time-Scale Joint Service Caching and Task Offloading for UAV-assisted Mobile Edge Computing

[Infocom2022] AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web

[TMC2022] A Force-Directed Approach to Seeking Route Recommendation in Ride-on-Demand Service Using Multi-Source Urban Data

[Xinyu][INFOCOM 2022] IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes

[Jiajun][INFOCOM 2022] Kalmia: A Heterogeneous QoS-aware Scheduling Framework for DNN Tasks on Edge Servers


Video Service in Edge Networks--Congrong

[SigComm 2022] NeuroScaler: neural video enhancement at scale

[INFOCOM 2022] FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics

[INFOCOM 2022] DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation [MobiHoc 2021] Task Offloading with Uncertain Processing Cycles


History

History

|abstract =The rapid expansion of large language models (LLMs) requires the development of extensive GPU clusters, with companies deploying clusters with tens to hundreds of thousands of GPUs. This growth significantly expands the design space for LLM training systems, requiring thorough exploration of different parallelization strategies, communication parameters, congestion control, fabric topology, etc. Current methods require up to 10k simulation experiments to identify optimal configurations, with inadequate exploration leading to significant degradation of training performance. In this paper, we tackle the overlooked problem of efficiently conducting parallel simulation experiments for design space exploration. Our

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

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