Difference between revisions of "Resource:Previous Seminars"

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=== History ===
=== History ===
{{Hist_seminar
{{Hist_seminar
|abstract = As Large Language Models (LLMs) continue to scale, optimizing their deployment requires efficient hardware and system co-design. However, current LLM performance evaluation frameworks fail to capture both chip-level execution details and system-wide behavior, making it difficult to assess realistic performance bottlenecks. In this work, we introduce ReaLLM, a trace-driven simulation framework designed to bridge the gap between detailed accelerator design and large-scale inference evaluation. Unlike prior simulators, ReaLLM integrates kernel profiling derived from detailed microarchitectural simulations with a new trace-driven end-to-end system simulator, enabling precise evaluation of parallelism strategies, batching techniques, and scheduling policies. To address the high computational cost of exhaustive simulations, ReaLLM constructs a precomputed kernel library based on hypothesized scenarios, interpolating results to efficiently explore a vast design space of LLM inference systems. Our validation against real hardware demonstrates the framework's accuracy, achieving an average end-to-end latency prediction error of only 9.1% when simulating inference tasks running on 4 NVIDIA H100 GPUs. We further use ReaLLM to evaluate popular LLMs' end-to-end performance across traces from different applications and identify key system bottlenecks, showing that modern GPU-based LLM inference is increasingly compute-bound rather than memory-bandwidth bound at large scale. Additionally, we significantly reduce simulation time with our precomputed kernel library by a factor of 6× for full-simulations and 164× for workload SLO exploration. ReaLLM is open-source and available at https://github.com/bespoke-silicon-group/reallm..
|confname =ASAP'25
|link = https://ieeexplore.ieee.org/abstract/document/11113621
|title= ReaLLM: A Trace-Driven Framework for Rapid Simulation of Large-Scale LLM Inference
|speaker=JunZhe
|date=2025-11-21
}}{{Hist_seminar
|abstract =With the proliferation of mobile devices, spatial crowdsourcing has emerged as a promising paradigm for facilitating location-based services, encompassing various applications across academia and industries. Recently, pioneering works have attempted to infer workers' mobility patterns from historical data to improve the quality of task assignment. However, these studies have overlooked or under-examined issues such as the dynamic mobility patterns of crowd workers, especially in the context of newcomers, the misalignment between the objectives of mobility prediction and task assignment, and the effective utilization of predicted mobility patterns. In this paper, we investigate a problem we term Task Assignment in Mobility Prediction-aware Spatial Crowdsourcing (TAMP). To address the TAMP problem, we first propose a task-adaptive meta-learning algorithm, which trains a set of specific meta-knowledge for workers' mobility prediction models through game theory-based learning task clustering and meta-training within each cluster. Then, we design a task assignment-oriented loss function and develop a task assignment algorithm that incorporates prediction performance, prioritizing assignments with higher confidence of completion. Extensive experiments on real-world datasets validate that our proposed methods can effectively improve the quality of task assignment.
|confname =ICDE'25
|link = https://ieeexplore.ieee.org/document/11113007
|title= Effective Task Assignment in Mobility Prediction-Aware Spatial Crowdsourcing
|speaker= Zhenguo
|date=2025-11-21
}}{{Hist_seminar
|abstract = Entanglement distribution across remote distances is critical for many quantum applications. Currently, the de facto approach for remote entanglement distribution relies on optical fiber for on-the-ground entanglement distribution. However, the fiber-based approach is incapable of global-scale entanglement distribution due to intrinsic limitations. This paper investigates a new hybrid ground-satellite quantum network architecture (QuESat) for global-scale entanglement distribution, integrating an on-the-ground fiber network with a global-scale passive optical network built with low-Earth-orbit satellites. The satellite network provides dynamic construction of photon lightpaths based on near-vacuum beam guides constructed via adjustable arrays of lenses, forwarding photons from one ground station to another with very high efficiency over long distances compared to using fiber. To assess the feasibility and effectiveness of QuESat for global communication, we formulate lightpath provisioning and entanglement distribution problems, considering the orbital dynamics of satellites and the time-varying entanglement demands from ground users. A two-stage algorithm is developed to dynamically configure the beam guides and distribute entanglements, respectively. The algorithm combines randomized and deterministic rounding for lightpath provisioning to enable global connectivity, with optimal entanglement swapping for distributing entanglements to meet users' demands. By developing a ground-satellite quantum network simulator, QuESat achieves multi-fold improvements compared to repeater networks.
|abstract = Entanglement distribution across remote distances is critical for many quantum applications. Currently, the de facto approach for remote entanglement distribution relies on optical fiber for on-the-ground entanglement distribution. However, the fiber-based approach is incapable of global-scale entanglement distribution due to intrinsic limitations. This paper investigates a new hybrid ground-satellite quantum network architecture (QuESat) for global-scale entanglement distribution, integrating an on-the-ground fiber network with a global-scale passive optical network built with low-Earth-orbit satellites. The satellite network provides dynamic construction of photon lightpaths based on near-vacuum beam guides constructed via adjustable arrays of lenses, forwarding photons from one ground station to another with very high efficiency over long distances compared to using fiber. To assess the feasibility and effectiveness of QuESat for global communication, we formulate lightpath provisioning and entanglement distribution problems, considering the orbital dynamics of satellites and the time-varying entanglement demands from ground users. A two-stage algorithm is developed to dynamically configure the beam guides and distribute entanglements, respectively. The algorithm combines randomized and deterministic rounding for lightpath provisioning to enable global connectivity, with optimal entanglement swapping for distributing entanglements to meet users' demands. By developing a ground-satellite quantum network simulator, QuESat achieves multi-fold improvements compared to repeater networks.
|confname = INFOCOM'25
|confname = INFOCOM'25

Revision as of 05:36, 28 November 2025

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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