Invention Application
- Patent Title: ASYNCHRONOUS DISTRIBUTED DATA FLOW FOR MACHINE LEARNING WORKLOADS
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Application No.: US18082415Application Date: 2022-12-15
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Publication No.: US20230118303A1Publication Date: 2023-04-20
- Inventor: Jeffrey Adgate Dean , Sudip Roy , Michael Acheson Isard , Aakanksha Chowdhery , Brennan Saeta , Chandramohan Amyangot Thekkath , Daniel William Hurt , Hyeontaek Lim , Laurent El Shafey , Parker Edward Schuh , Paul Ronald Barham , Ruoming Pang , Ryan Sepassi , Sanjay Ghemawat , Yonghui Wu
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Main IPC: G06F9/48
- IPC: G06F9/48 ; G06N3/063 ; G06N3/08

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators. One of the systems comprises a plurality of accelerator islands, each hardware accelerator island comprising a respective plurality of hardware devices that include a plurality of hardware accelerators and a corresponding host for each of the plurality of hardware accelerators; and a respective scheduler for each of the accelerator islands that is configured to schedule workloads across the plurality of accelerators and corresponding hosts in the accelerator island, wherein the system is configured to: receive data representing a machine learning workload; and assign a respective portion of the machine learning workload to each of the plurality of accelerator islands for scheduling by the respective scheduler for the accelerator island.
Public/Granted literature
- US12112198B2 Asynchronous distributed data flow for machine learning workloads Public/Granted day:2024-10-08
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