Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2024-09-13 10:30-12:00'''
|time='''2026-04-10 10:30'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
}}
}}


===Latest===
===Latest===
{{Hist_seminar
|abstract = Recent advances in network and mobile computing.
|confname=Talk
|link=http://mobinets.org/index.php?title=Resource:Paper_Carnival_2024
|title=[[Resource:Paper_Carnival_2024|Paper Carnival 2024]]
|speaker=All
|date=2024-9-5 ~ 2024-9-6
}}


{{Hist_seminar
{{Latest_seminar
|abstract = Increasing bandwidth demands of mobile video streaming
|abstract = To effectively utilize heterogeneous specialized hardware units in modern GPUs, such as TensorCores and Tensor Memory Accelerators, this paper introduces PipeThreader, a new DNN compiler. PipeThreader proposes shifting scheduling functionality from hardware to software so as to enable more efficient and sophisticated computation pipelining with minimal manual effort. This is achieved through sTask-graph, a new DNN computation abstraction, a hierarchical hardware abstraction that captures the capabilities of specialized units, and new scheduling primitives. As a result, PipeThreader can discover efficient pipeline scheduling for well-studied DNN architectures like FlashAttention, achieving comparable or even superior performance. Additionally, it can uncover novel pipeline schemes for emerging models like Mamba2, delivering significantly better performance compared to state-of-the-art hand-crafted implementations. The code is open-sourced at https://github.com/tile-ai/tilelang.
pose a challenge in optimizing the Quality of Experience
|confname =OSDI'25
(QoE) for better user engagement. Multipath transmission
|link = https://www.usenix.org/conference/osdi25/presentation/cheng
promises to extend network capacity by utilizing multiple
|title= PipeThreader: Software-defined pipelining for efficient DNN execution
wireless links simultaneously. Previous studies mainly tune
|speaker=Junzhe
the packet scheduler in multipath transmission, expecting
|date=2026-4-9
higher QoE by accelerating transmission. However, since
Adaptive BitRate (ABR) algorithms overlook the impact of
multipath scheduling on throughput prediction, multipath
adaptive streaming can even experience lower QoE than
single-path. This paper proposes Chorus, a cross-layer framework that coordinates multipath scheduling with adaptive
streaming to optimize QoE jointly. Chorus establishes twoway feedback control loops between the server and the client.
Furthermore, Chorus introduces Coarse-grained Decisions,
which assist appropriate bitrate selection by considering
the scheduling decision in throughput prediction, and Finegrained Corrections, which meet the predicted throughput
by QoE-oriented multipath scheduling. Extensive emulation
and real-world mobile Internet evaluations show that Chorus
outperforms the state-of-the-art MPQUIC scheduler, improving average QoE by 23.5% and 65.7%, respectively.  
|confname=MobiCom' 24
|link=https://dl.acm.org/doi/pdf/10.1145/3636534.3649359
|title=[[Chorus: Coordinating Mobile Multipath Scheduling and Adaptive Video Streaming]]
|speaker=Jiahao
|date=2024-9-13
}}
}}


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:37, 10 April 2026

Time: 2026-04-10 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [OSDI'25] PipeThreader: Software-defined pipelining for efficient DNN execution, Junzhe
    Abstract: To effectively utilize heterogeneous specialized hardware units in modern GPUs, such as TensorCores and Tensor Memory Accelerators, this paper introduces PipeThreader, a new DNN compiler. PipeThreader proposes shifting scheduling functionality from hardware to software so as to enable more efficient and sophisticated computation pipelining with minimal manual effort. This is achieved through sTask-graph, a new DNN computation abstraction, a hierarchical hardware abstraction that captures the capabilities of specialized units, and new scheduling primitives. As a result, PipeThreader can discover efficient pipeline scheduling for well-studied DNN architectures like FlashAttention, achieving comparable or even superior performance. Additionally, it can uncover novel pipeline schemes for emerging models like Mamba2, delivering significantly better performance compared to state-of-the-art hand-crafted implementations. The code is open-sourced at https://github.com/tile-ai/tilelang.

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

Instructions

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}}

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