Pure Storage, Inc.

Augment Computing, Inc.

Igor Ostrovsky

Robert H Lee

John Martin Hayes

Peter Vajgel

Brian Gold

Ivan Jibaja

Roy Kim

John Colgrove

Emily Watkins

Ronald S Karr

Boris Feigin

Joshua Robinson

John D Davis

Hari Kannan

Ying Gao

Shantanu R Gupta

Jeffrey Vincent Tofano

Rusty Sears

Ethan L Miller

Constantine P Sapuntzakis

Peter E Kirkpatrick

Par Botes

Emily Potyraj

Christopher R Lumb

Cary A Sandvig

Richard A Hankins

Joshua P Robinson

Mark Emberson

Abhishek Jain

Shuyi Shao

Victor Yip

Zong Miao

Dirk Meister

Svitlana Tumanova

Srinivas Chellappa

Ian Juch

Mark McAULIFFE

Ramnath Sai Sagar Thumbavanam Padmanabhan

Guangyu Shi

Markus Norman Rabe

Michiel Steven De Jong

Guy Gur-ari Krakover

Methods, systems, products, services, and apparatuses for generative AI systems configured to produce computer program code using a knowledge base that includes a client-specific code base, including: receiving one or more input tokens associated with a computer program; accessing information describing a domain-specific codebase; and generating, based on the one or more input tokens associated with the computer program and information describing a domain-specific codebase, suggested code to insert into the computer program.

Enterprise specific code generation using generative artificial intelligence

Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit ('GPU') servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

Executing machine learning models using transformed datasets

A method for accessing a file in a storage system is provided. The method includes determining, for each file chunk of the file, an authority among differing storage nodes of the storage system and receiving from the authority having ownership of the file chunk, location information for the file chunk. The method includes accessing file chunks of the file as directed by each of the determined authorities.

File chunk distribution and ownership determination

A storage system has a first memory, and a second memory that includes storage memory. The storage system has a processing device. The processing device is to select whether to write data to the first memory and write the data from the first memory to the second memory, or to write the data to the second memory bypassing the first memory. The processing device is to write portions of data for storage according to such selection.

Storage system with multiple write paths

Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit ('GPU') servers, including: storing, within one or more storage systems, a transformed dataset generated by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers; and transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.

Optimizing dataset transformations for use by machine learning models

A system for cloud-based file services, comprising: a plurality of single-tenant file system nodes configured to provide file system access to an object store via a plurality of multitenant storage nodes; the plurality of multitenant storage nodes sharing access to the object store; and one or more management nodes configured to provision resources for the plurality of single-tenant file system nodes and the plurality of multitenant storage nodes, including modifying resources within the system.

Provisioning file system resources in cloud environments

A method of distributed file deletion, performed by a storage system, is provided. The method includes receiving, at the storage system, a request to delete a directory and contents of the directory and adding the directory to a first set, listed in a memory in the storage system. The method includes operating on the first set, by examining each directory in the first set to identify subdirectories, adding each identified subdirectory to the first set as a directory, and adding each examined directory to a second set listed in the memory. The method includes deleting in a distributed manner across the storage system without concern for order, contents of directories, and the directories listed in the second set.

Storage system with distributed deletion

Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.

Machine learning models in an artificial intelligence infrastructure

A system with storage memory and a processing device has a logical deletion to physical erasure time bound. The system dereferences data, responsive to a direction to delete the data. The system monitors physical blocks in storage memory for live data and the dereferenced data. The system cooperates garbage collection with monitoring the physical blocks, so that at least a physical block having the dereferenced data is garbage collected and erased within a logical deletion to physical erasure time bound.

Guaranteeing physical deletion of data in a storage system

Systems and methods of deduplication aware scalable content placement are described. A method may include receiving data to be stored on one or more nodes of a storage array and calculating a plurality of hashes corresponding to the data. The method further includes determining a first subset of the plurality of hashes, determining a second subset of the plurality of hashes of the first subset, and generating a node candidate placement list. The method may further include sending the first subset to one or more nodes represented on the node candidate placement list and receiving, from the nodes represented on the node candidate placement list, characteristics corresponding to the nodes represented on the candidate placement list. The method may further include identifying one of the one or more nodes represented on the candidate placement list in view of the characteristic and sending the data to the identified node.

Storage node data placement utilizing similarity

A method for snapshots with crash consistency is provided. The method includes indicating intent to create a snapshot of storage system contents associated with an existing snapshot copy identifier, responsive to confirmation that all of a plurality of distributed agents are in a snapshot creation mode. The method includes responding, with a new snapshot copy identifier that replaces the existing snapshot copy identifier, to one or more inquiries from the plurality of distributed agents regarding one or more I/O operations, after the indicating the intent to create the snapshot. The method includes creating the snapshot using the existing snapshot copy identifier and committing the snapshot, responsive to receiving responses from the plurality of distributed agents that service is complete for all I/O operations that started before the indicating the intent to create the snapshot or that are associated with the existing snapshot copy identifier.

Snapshot commitment in a distributed system

A method for file locks in a distributed file system, performed by a storage system is provided. The method includes receiving, at a storage system having a distributed file system, a request for access of a file and locking the file, through one of a plurality of persistent file locks in the storage system. The method includes accessing the file, through the distributed file system, and unlocking the file, through the one of the plurality of persistent file locks.

Locks in a distributed file system

File ownership in a distributed system

A storage system has a first memory, a second memory that include solid-state storage memory, and a processing device. The processing device is to select a mode for each portion of data to be written. Selection of the mode is based at least on size of the portion of data. Selection of the mode is from among modes that include a first mode of writing the portion of data in mirrored RAID form to the first memory for later transfer from the first memory to the second memory, a second mode of writing the portion of data in parity-based RAID form to the first memory for later transfer from the first memory to the second memory, and a third mode of writing the portion of data to the second memory, bypassing the first memory. The processing device is to handle portions of data to be written according to such selection.

Storage system with selectable write modes

A processor-based method for locating data and metadata closely together in a storage system is provided. The method includes writing a first range of a file and a first metadata relating to attributes of the file into at least one segment controlled by a first authority of the file. The method includes delegating, by the first authority, a second authority for a second range of the file, and writing the second range of the file and second metadata relating to the attributes of the file into at least one segment controlled by the second authority.

Delegation of data ownership

Growing community of inventors

Mountain View, CA, United States of America

Igor Ostrovsky