公开(公告)号：US20230394203A1

公开(公告)日：2023-12-07

申请号：US18310427

申请日：2023-05-01

Applicant: Google LLC

Inventor： Chian-min Richard Ho ,  William Hang ,  Mustafa Nazim Yazgan ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Azalia Mirhoseini ,  Emre Tuncer ,  Ya Wang ,  Anand Babu

IPC: G06F30/27 ,  G06F30/392

CPC classification number: G06F30/27 ,  G06F30/392

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

公开(公告)号：US11769061B2

公开(公告)日：2023-09-26

申请号：US16898971

申请日：2020-06-11

Applicant: Google LLC

Inventor： Paul A. Tucker ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat ,  Yuan Yu

IPC: G06N3/08 ,  G06N3/098 ,  G06F9/50 ,  G06N3/084 ,  G06N3/063 ,  G06N3/045 ,  G06N20/00 ,  G06N5/048

CPC classification number: G06N3/098 ,  G06F9/5038 ,  G06F9/5066 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N3/084 ,  G06N20/00 ,  G06N5/048

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a request from a client to process a computational graph; obtaining data representing the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node that represents an operation that receives, as input, an output of an operation represented by the respective first node; identifying a plurality of available devices for performing the requested operation; partitioning the computational graph into a plurality of subgraphs, each subgraph comprising one or more nodes in the computational graph; and assigning, for each subgraph, the operations represented by the one or more nodes in the subgraph to a respective available device in the plurality of available devices for operation.

公开(公告)号：US11650971B2

公开(公告)日：2023-05-16

申请号：US17834316

申请日：2022-06-07

Applicant: Google LLC

Inventor： Jeffrey Adgate Dean ,  Sanjay Ghemawat

IPC: G06F16/22 ,  G06F16/23 ,  G06F16/2453 ,  G06F9/48 ,  G06F9/54

CPC classification number: G06F16/2282 ,  G06F9/4881 ,  G06F9/54 ,  G06F16/2379 ,  G06F16/24532

Abstract: A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

公开(公告)号：US20220405264A1

公开(公告)日：2022-12-22

申请号：US17834316

申请日：2022-06-07

Applicant: Google LLC

Inventor： Jeffrey Adgate Dean ,  Sanjay Ghemawat

IPC: G06F16/22 ,  G06F16/23 ,  G06F16/2453 ,  G06F9/48 ,  G06F9/54

Abstract: A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

公开(公告)号：US20220357985A1

公开(公告)日：2022-11-10

申请号：US17738909

申请日：2022-05-06

Applicant: Google LLC

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

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.

公开(公告)号：US11100266B2

公开(公告)日：2021-08-24

申请号：US16889130

申请日：2020-06-01

Applicant: Google LLC

Inventor： Chian-min Richard Ho ,  William Hang ,  Mustafa Nazim Yazgan ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Azalia Mirhoseini ,  Emre Tuncer ,  Ya Wang ,  Anand Babu

IPC: G06F30/00 ,  G06F30/30 ,  G06F30/27 ,  G06F30/392

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

公开(公告)号：US11087216B2

公开(公告)日：2021-08-10

申请号：US17015196

申请日：2020-09-09

Applicant: Google LLC

Inventor： Vijay Vasudevan ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat

IPC: G06F15/18 ,  G06N3/08 ,  G06F9/50 ,  G06F9/54 ,  G06N3/063 ,  G06N3/04 ,  G06F17/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for modifying a computational graph to include send and receive nodes. Communication between unique devices performing operations of different subgraphs of the computational graph can be handled efficiently by inserting send and receive nodes into each subgraph. When executed, the operations that these send and receive nodes represent may enable pairs of unique devices to conduct communication with each other in a self-sufficient manner. This shifts the burden of coordinating communication away from the backend, which affords the system that processes this computational graph representation the opportunity to perform one or more other processes while devices are executing subgraphs.

公开(公告)号：US10991005B2

公开(公告)日：2021-04-27

申请号：US16258939

申请日：2019-01-28

Applicant: Google LLC

Inventor： Jeffrey Adgate Dean ,  Georges Harik ,  Paul Buchheit

IPC: G06Q30/02 ,  G06F16/35 ,  G06F16/951

Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

公开(公告)号：US10783435B2

公开(公告)日：2020-09-22

申请号：US15338225

申请日：2016-10-28

Applicant: Google LLC

Inventor： Vijay Vasudevan ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat

IPC: G06F15/18 ,  G06N3/08 ,  G06F9/50 ,  G06F9/54 ,  G06N3/063 ,  G06N3/04 ,  G06F17/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for modifying a computational graph to include send and receive nodes. Communication between unique devices performing operations of different subgraphs of the computational graph can be handled efficiently by inserting send and receive nodes into each subgraph. When executed, the operations that these send and receive nodes represent may enable pairs of unique devices to conduct communication with each other in a self-sufficient manner. This shifts the burden of coordinating communication away from the backend, which affords the system that processes this computational graph representation the opportunity to perform one or more other processes while devices are executing subgraphs.

公开(公告)号：US10534997B2

公开(公告)日：2020-01-14

申请号：US15965742

申请日：2018-04-27

Applicant: Google LLC

Inventor： Paul A. Tucker ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat ,  Yuan Yu

IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/08 ,  G06F9/50 ,  G06N3/063 ,  G06N3/04 ,  G06N5/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a request from a client to process a computational graph; obtaining data representing the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node that represents an operation that receives, as input, an output of an operation represented by the respective first node; identifying a plurality of available devices for performing the requested operation; partitioning the computational graph into a plurality of subgraphs, each subgraph comprising one or more nodes in the computational graph; and assigning, for each subgraph, the operations represented by the one or more nodes in the subgraph to a respective available device in the plurality of available devices for operation.