Invention Application
- Patent Title: ABSTRACTION LAYERS FOR SCALABLE DISTRIBUTED MACHINE LEARNING
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Application No.: US17398295Application Date: 2021-08-10
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Publication No.: US20220101480A1Publication Date: 2022-03-31
- Inventor: DHIRAJ D. KALAMKAR , KARTHIKEYAN VAIDYANATHAN , SRINIVAS SRIDHARAN , DIPANKAR DAS
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Main IPC: G06T1/20
- IPC: G06T1/20 ; G06N3/04 ; G06N3/063 ; G06N3/08

Abstract:
One embodiment provides for a method of transmitting data between multiple compute nodes of a distributed compute system, the method comprising creating a global view of communication operations to be performed between the multiple compute nodes of the distributed compute system, the global view created using information specific to a machine learning model associated with the distributed compute system; using the global view to determine a communication cost of the communication operations; and automatically determining a number of network endpoints for use in transmitting the data between the multiple compute nodes of the distributed compute system.
Public/Granted literature
- US11798120B2 Abstraction layers for scalable distributed machine learning Public/Granted day:2023-10-24
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