Google Inc.

Eugene Brevdo

Honglak Lee

Alexandre Tachard Passos

Campbell Bryce Fraser

Lyric Pankaj Doshi

Jacob Buckman

Danijar Hafner

George Jay Tucker

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing critical section subgraphs in a computational graph system. One of the methods includes executing a lock operation including providing, by a task server, a request to a value server to create a shared critical section object. If the task server determines that the shared critical section object was created by the value server, the task server executes one or more other operations of the critical section subgraph in serial. The task server executes an unlock operation including providing, by the task server, a request to the value server to delete the shared critical section object.

Computational graph critical sections

A method includes receiving a database query requesting a database to conditionally return one or more data blocks. The database is stored on memory hardware in communication with the data processing hardware and the database query includes a plurality of parameters characterizing the database query. The method includes generating a set of query plans. Each query plan in the set of query plans is configured to execute the database query using a different order of operations. The method includes training a model using historical database queries and generating, using the trained model, a query plan score for each query plan in the set of query plans. The method includes selecting, using the query plan score of each query plan in the set of query plans, a query plan from the set of query plans. The method also includes executing the database query using the selected query plan.

Database query optimization via parameter-sensitive plan selection

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for efficiently processing dynamic length tensors of a machine learning model represented by a computational graph. A program is received that specifies a dynamic, iterative computation that can be performed on input data for processing by a machine learning model. A directed computational graph representing the machine learning model is generated that specifies the dynamic, iterative computation as one or more operations using a tensor array object. Input is received for processing by the machine learning model and the directed computational graph representation of the machine learning model is executed with the received input to obtain output.

Dynamic-length stateful tensor array

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San Bruno, CA, United States of America

Eugene Brevdo