Difference between revisions of "Resource:Previous Seminars"

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=== History ===
=== History ===
====2024====
====2024====
{{Hist_seminar
|abstract = In this paper, we revisit the problem of the current routing system in terms of prediction scalability and routing result optimality. Specifically, the current traffic prediction models are not suitable for large urban networks due to the incomplete information of traffic conditions. Besides, existing routing systems can only plan the routes based on the past traffic conditions and struggle to update the optimal route for vehicles in real-time. As a result, the actual route taken by vehicles is different from the ground-truth optimal path. Therefore, we propose a Just-In-Time Predictive Route Planning framework to tackle these two problems. Firstly, we propose a Travel Time Constrained Top- kn Shortest Path algorithm which pre-computes a set of candidate paths with several switch points. This empowers vehicles to continuously have the opportunity to switch to better paths taking into account real-time traffic condition changes. Moreover, we present a query-driven prediction paradigm with ellipse-based searching space estimation, along with an efficient multi-queries handling mechanism. This not only allows for targeted traffic prediction by prioritizing regions with valuable yet outdated traffic information, but also provides optimal results for multiple queries based on real-time traffic evolution. Evaluations on two real-life road networks demonstrate the effectiveness and efficiency of our framework and methods.
|confname =ICDE'24
|link = https://ieeexplore.ieee.org/document/10598147/authors#authors
|title= A Just-In-Time Framework for Continuous Routing
|speaker=Zhenguo
|date=2024-11-8
}}
{{Hist_seminar
|abstract = Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensive engineering overhead due to the manual design of deep neural networks (DNNs) for different networking tasks. Besides, DNNs tend to achieve poor generalization performance on unseen data distributions/environments. Motivated by the recent success of large language models (LLMs), this work studies the LLM adaptation for networking to explore a more sustainable design philosophy. With the powerful pre-trained knowledge, the LLM is promising to serve as the foundation model to achieve "one model for all tasks" with even better performance and stronger generalization. In pursuit of this vision, we present NetLLM, the first framework that provides a coherent design to harness the powerful capabilities of LLMs with low efforts to solve networking problems. Specifically, NetLLM empowers the LLM to effectively process multimodal data in networking and efficiently generate task-specific answers. Besides, NetLLM drastically reduces the costs of fine-tuning the LLM to acquire domain knowledge for networking. Across three networking-related use cases - viewport prediction, adaptive bitrate streaming and cluster job scheduling, we showcase that the NetLLM-adapted LLM significantly outperforms state-of-the-art algorithms.
|confname =SIGCOMM'24
|link = https://dl.acm.org/doi/abs/10.1145/3651890.3672268
|title= NetLLM: Adapting Large Language Models for Networking
|speaker=Yinghao
|date=2024-11-8
}}
{{Hist_seminar
{{Hist_seminar
|abstract = Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and inter-node communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13× to 5.77× speedup on 1296 manually configured MoE layers and approximately 3× improvement on two real-world MoE models based on BERT and GPT-2.
|abstract = Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and inter-node communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13× to 5.77× speedup on 1296 manually configured MoE layers and approximately 3× improvement on two real-world MoE models based on BERT and GPT-2.

Revision as of 15:16, 21 November 2024

History

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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