公开(公告)号：US11921611B2

公开(公告)日：2024-03-05

申请号：US17571373

申请日：2022-01-07

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/34 ,  G06F9/54 ,  G06F11/30 ,  G06F11/36

CPC classification number: G06F11/3466 ,  G06F9/542 ,  G06F11/302 ,  G06F11/3495 ,  G06F11/3612 ,  G06F11/348 ,  G06F2201/86 ,  G06F2201/865

Abstract: A computer-implemented method that includes monitoring execution of program code by first and second processor components. A computing system detects that a trigger condition is satisfied by: i) identifying an operand in a portion of the program code; or ii) determining that a current time of a clock of the computing system indicates a predefined time value. The operand and the predefined time value are used to initiate trace events. When the trigger condition is satisfied the system initiates trace events that generate trace data identifying respective hardware events occurring across the computing system. The system uses the trace data to generate a correlated set of trace data. The correlated trace data indicates a time ordered sequence of the respective hardware events. The system uses the correlated set of trace data to analyze performance of the executing program code.

公开(公告)号：US20240062062A1

公开(公告)日：2024-02-22

申请号：US18376362

申请日：2023-10-03

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/10 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N5/04 ,  G06N3/105 ,  G06N3/044 ,  G06N3/045

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US10324817B2

公开(公告)日：2019-06-18

申请号：US15875160

申请日：2018-01-19

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F9/54 ,  G06F11/30 ,  G06F11/34 ,  G06F11/36 ,  G06F16/48

Abstract: A computer-implemented method executed by one or more processors, the method includes monitoring execution of program code executed by a first processor component; and monitoring execution of program code executed by a second processor component. A computing system stores data identifying hardware events in a memory buffer. The stored events occur across processor units that include at least the first and second processor components. The hardware events each include an event time stamp and metadata characterizing the event. The system generates a data structure identifying the hardware events. The data structure arranges the events in a time ordered sequence and associates events with at least the first or second processor components. The system stores the data structure in a memory bank of a host device and uses the data structure to analyze performance of the program code executed by the first or second processor components.

公开(公告)号：US11803747B2

公开(公告)日：2023-10-31

申请号：US16878720

申请日：2020-05-20

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/10 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/105 ,  G06N5/04

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US10896110B2

公开(公告)日：2021-01-19

申请号：US16411569

申请日：2019-05-14

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/30 ,  G06F16/48 ,  G06F11/34 ,  G06F9/54 ,  G06F11/36

Abstract: A computer-implemented method executed by one or more processors, the method includes monitoring execution of program code executed by a first processor component; and monitoring execution of program code executed by a second processor component. A computing system stores data identifying hardware events in a memory buffer. The stored events occur across processor units that include at least the first and second processor components. The hardware events each include an event time stamp and metadata characterizing the event. The system generates a data structure identifying the hardware events. The data structure arranges the events in a time ordered sequence and associates events with at least the first or second processor components. The system stores the data structure in a memory bank of a host device and uses the data structure to analyze performance of the program code executed by the first or second processor components.

公开(公告)号：US20200065206A1

公开(公告)日：2020-02-27

申请号：US16665355

申请日：2019-10-28

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/30 ,  G06F11/36 ,  G06F11/34 ,  G06F9/54 ,  G06F16/48

Abstract: A computer-implemented method executed by one or more processors, the method includes monitoring execution of program code executed by a first processor component; and monitoring execution of program code executed by a second processor component. A computing system stores data identifying hardware events in a memory buffer. The stored events occur across processor units that include at least the first and second processor components. The hardware events each include an event time stamp and metadata characterizing the event. The system generates a data structure identifying the hardware events. The data structure arranges the events in a time ordered sequence and associates events with at least the first or second processor components. The system stores the data structure in a memory bank of a host device and uses the data structure to analyze performance of the program code executed by the first or second processor components.

公开(公告)号：US20200019483A1

公开(公告)日：2020-01-16

申请号：US16520558

申请日：2019-07-24

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/34 ,  G06F11/30 ,  G06F9/54 ,  G06F11/36

Abstract: A computer-implemented method that includes monitoring execution of program code by first and second processor components. A computing system detects that a trigger condition is satisfied by: i) identifying an operand in a portion of the program code; or ii) determining that a current time of a clock of the computing system indicates a predefined time value. The operand and the predefined time value are used to initiate trace events. When the trigger condition is satisfied the system initiates trace events that generate trace data identifying respective hardware events occurring across the computing system. The system uses the trace data to generate a correlated set of trace data. The correlated trace data indicates a time ordered sequence of the respective hardware events. The system uses the correlated set of trace data to analyze performance of the executing program code.

公开(公告)号：US10365987B2

公开(公告)日：2019-07-30

申请号：US15472932

申请日：2017-03-29

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/34 ,  G06F11/30 ,  G06F9/54 ,  G06F11/36

Abstract: A computer-implemented method that includes monitoring execution of program code by first and second processor components. A computing system detects that a trigger condition is satisfied by: i) identifying an operand in a portion of the program code; or ii) determining that a current time of a clock of the computing system indicates a predefined time value. The operand and the predefined time value are used to initiate trace events. When the trigger condition is satisfied the system initiates trace events that generate trace data identifying respective hardware events occurring across the computing system. The system uses the trace data to generate a correlated set of trace data. The correlated trace data indicates a time ordered sequence of the respective hardware events. The system uses the correlated set of trace data to analyze performance of the executing program code.