Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving. As more data-intensive applications move to run on top of task-based systems, collective communication efficiency has become an important problem. Unfortunately, traditional collective communication libraries (e.g., MPI, Horovod, NCCL) are an ill fit, because they require the communication schedule to be known before runtime and they do not provide fault tolerance. We design and implement Hoplite, an efficient and fault-tolerant collective communication layer for task-based distributed systems. Our key technique is to compute data transfer schedules on the fly and execute the schedules efficiently through fine-grained pipelining. At the same time, when a task fails, the data transfer schedule adapts quickly to allow other tasks to keep making progress. We apply Hoplite to a popular task-based distributed framework, Ray. We show that Hoplite speeds up asynchronous stochastic gradient descent, reinforcement learning, and serving an ensemble of machine learning models that are difficult to execute efficiently with traditional collective communication by up to 7.8x, 3.9x, and 3.3x, respectively.
|abstract=Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving. As more data-intensive applications move to run on top of task-based systems, collective communication efficiency has become an important problem. Unfortunately, traditional collective communication libraries (e.g., MPI, Horovod, NCCL) are an ill fit, because they require the communication schedule to be known before runtime and they do not provide fault tolerance. We design and implement Hoplite, an efficient and fault-tolerant collective communication layer for task-based distributed systems. Our key technique is to compute data transfer schedules on the fly and execute the schedules efficiently through fine-grained pipelining. At the same time, when a task fails, the data transfer schedule adapts quickly to allow other tasks to keep making progress. We apply Hoplite to a popular task-based distributed framework, Ray. We show that Hoplite speeds up asynchronous stochastic gradient descent, reinforcement learning, and serving an ensemble of machine learning models that are difficult to execute efficiently with traditional collective communication by up to 7.8x, 3.9x, and 3.3x, respectively. Vid: https://www.youtube.com/watch?v=pHLIrkNj4w0
Vid: https://www.youtube.com/watch?v=pHLIrkNj4w0
|confname=SIGCOMM 2021
|confname=SIGCOMM 2021
|link=https://dl.acm.org/doi/pdf/10.1145/3452296.3472897
|link=https://dl.acm.org/doi/pdf/10.1145/3452296.3472897
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}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=This paper re-evaluates the performance of the EPaxos consensus protocol for geo-replication and proposes an enhancement that uses synchronized clocks to reduce operation latency. The benchmarking approach used for the original EPaxos evaluation does not trigger or measure the full impact of conflict behavior on system performance. Our re-evaluation confirms the original claim that EPaxos provides optimal median commit latency in a WAN, but it shows much worse tail latency than previously reported (more than 4x worse than Multi-Paxos). Furthermore, performance is highly sensitive to application workloads, particularly at the tail. In addition, we show how synchronized clocks can be used to reduce conflicts in geo-replication. By imposing intentional delays on message processing, we can achieve roughly in-order deliveries to multiple replicas. When applied to EPaxos, this technique reduced conflicts by at least 50% without introducing additional overhead, decreasing mean latency by up to 7.5%.
|abstract=This paper re-evaluates the performance of the EPaxos consensus protocol for geo-replication and proposes an enhancement that uses synchronized clocks to reduce operation latency. The benchmarking approach used for the original EPaxos evaluation does not trigger or measure the full impact of conflict behavior on system performance. Our re-evaluation confirms the original claim that EPaxos provides optimal median commit latency in a WAN, but it shows much worse tail latency than previously reported (more than 4x worse than Multi-Paxos). Furthermore, performance is highly sensitive to application workloads, particularly at the tail. In addition, we show how synchronized clocks can be used to reduce conflicts in geo-replication. By imposing intentional delays on message processing, we can achieve roughly in-order deliveries to multiple replicas. When applied to EPaxos, this technique reduced conflicts by at least 50% without introducing additional overhead, decreasing mean latency by up to 7.5%. Vid: https://www.usenix.org/conference/nsdi21/presentation/tollman
Vid: https://www.usenix.org/conference/nsdi21/presentation/tollman
|confname=NSDI 2021
|confname=NSDI 2021
|link=https://www.usenix.org/system/files/nsdi21-tollman.pdf
|link=https://www.usenix.org/system/files/nsdi21-tollman.pdf

Revision as of 23:41, 4 October 2021

Time: 2021-10-08 8:40
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [SIGCOMM 2021] Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems, Xianyang
    Abstract: Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving. As more data-intensive applications move to run on top of task-based systems, collective communication efficiency has become an important problem. Unfortunately, traditional collective communication libraries (e.g., MPI, Horovod, NCCL) are an ill fit, because they require the communication schedule to be known before runtime and they do not provide fault tolerance. We design and implement Hoplite, an efficient and fault-tolerant collective communication layer for task-based distributed systems. Our key technique is to compute data transfer schedules on the fly and execute the schedules efficiently through fine-grained pipelining. At the same time, when a task fails, the data transfer schedule adapts quickly to allow other tasks to keep making progress. We apply Hoplite to a popular task-based distributed framework, Ray. We show that Hoplite speeds up asynchronous stochastic gradient descent, reinforcement learning, and serving an ensemble of machine learning models that are difficult to execute efficiently with traditional collective communication by up to 7.8x, 3.9x, and 3.3x, respectively. Vid: https://www.youtube.com/watch?v=pHLIrkNj4w0
  2. [NSDI 2021] EPaxos Revisited, Jianfei
    Abstract: This paper re-evaluates the performance of the EPaxos consensus protocol for geo-replication and proposes an enhancement that uses synchronized clocks to reduce operation latency. The benchmarking approach used for the original EPaxos evaluation does not trigger or measure the full impact of conflict behavior on system performance. Our re-evaluation confirms the original claim that EPaxos provides optimal median commit latency in a WAN, but it shows much worse tail latency than previously reported (more than 4x worse than Multi-Paxos). Furthermore, performance is highly sensitive to application workloads, particularly at the tail. In addition, we show how synchronized clocks can be used to reduce conflicts in geo-replication. By imposing intentional delays on message processing, we can achieve roughly in-order deliveries to multiple replicas. When applied to EPaxos, this technique reduced conflicts by at least 50% without introducing additional overhead, decreasing mean latency by up to 7.5%. Vid: https://www.usenix.org/conference/nsdi21/presentation/tollman

History

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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