Invention Grant
- Patent Title: Abstraction layers for scalable distributed machine learning
-
Application No.: US17398295Application Date: 2021-08-10
-
Publication No.: US11798120B2Publication Date: 2023-10-24
- 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
- Agency: JAFFERY WATSON MENDONSA & HAMILTON LLP
- Main IPC: G06N3/06
- IPC: G06N3/06 ; G06T1/20 ; G06N3/063 ; G06N3/084 ; G06N3/044 ; G06N3/045

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
- US20220101480A1 ABSTRACTION LAYERS FOR SCALABLE DISTRIBUTED MACHINE LEARNING Public/Granted day:2022-03-31
Information query