Daniel William Hurt

US Patent:

20220357985, Nov 10, 2022

Filed:

May 6, 2022

Appl. No.:

17/738909

Inventors:

- Mountain View CA, US
Sudip Roy - San Jose CA, US
Michael Acheson Isard - San Francisco CA, US
Aakanksha Chowdhery - Mountain View CA, US
Brennan Saeta - Kirkland WA, US
Chandramohan Amyangot Thekkath - Palo Alto CA, US
Daniel William Hurt - Westminster CO, US
Hyeontaek Lim - Palo Alto CA, US
Laurent El Shafey - Mountain View CA, US
Parker Edward Schuh - Mountain View CA, US
Paul Ronald Barham - San Francisco CA, US
Ruoming Pang - New York NY, US
Ryan Sepassi - Palo Alto CA, US
Sanjay Ghemawat - Mountain View CA, US
Yonghui Wu - Fremont CA, US

International Classification:

G06F 9/48
G06N 3/063
G06N 3/08

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.