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Zhao et al., 2019 - Google Patents

Dynamic stale synchronous parallel distributed training for deep learning

Zhao et al., 2019

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Document ID
9568723824413666372
Author
Zhao X
An A
Liu J
Chen B
Publication year
Publication venue
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

External Links

Snippet

Deep learning is a popular machine learning technique and has been applied to many real- world problems, ranging from computer vision to natural language processing. However, training a deep neural network is very time-consuming, especially on big data. It has …
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Classifications

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    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
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    • G06F9/4843Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
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    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
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    • G06F9/5005Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/5038Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
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