Google Inc.

Daniel William Hurt

Jeffrey Adgate Dean

Sanjay Ghemawat

Yonghui Wu

Ruoming Pang

Michael Isard

Chandramohan Amyangot Thekkath

Paul Ronald Barham

Sudip Roy

Ryan Sepassi

Aakanksha Chowdhery

Laurent El Shafey

Brennan Saeta

Hyeontaek Lim

Parker Edward Schuh

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.

Asynchronous distributed data flow for machine learning workloads

Growing community of inventors

Westminster, CO, United States of America

Daniel William Hurt