Pure Storage, Inc.

Emily Potyraj

Brian Gold

Joshua P Robinson

Bennett Amodio

Igor Ostrovsky

Robert H Lee

Ivan Jibaja

Roy Kim

Brian Carpenter

Ronald S Karr

Hari Kannan

Cary A Sandvig

Fabio Margaglia

Ramnath Sai Sagar Thumbavanam Padmanabhan

Matthew Strathman

Application migration using data movement capabilities of a storage system, including: executing an application on a first on-premises cloud infrastructure; storing, in a first storage system that is coupled for data communications with the first on-premises cloud infrastructure, data associated with the application; replicating, from the first storage system to a second storage system that is coupled for data communications with a second on-premises cloud infrastructure, the data; and initiating, based on the data, execution of a new instance of the application on the second on-premises cloud infrastructure.

Application migration using data movement capabilities of a storage system

Utilizing data transfer estimates for active management of a storage environment, including: determining an estimated data transfer time from a source storage environment to a destination storage environment; determining whether the estimated data transfer time exceeds a threshold; and performing, in response to the estimated data transfer time exceeding the threshold, a remedial action associated with data transfers between the source storage environment and the destination storage environment.

Utilizing data transfer estimates for active management of a storage environment

Providing persistent storage to transient cloud computing services, including: creating a cloud computing instance, wherein the cloud computing instance is created on on-premises cloud infrastructure; and storing, in non-volatile storage in a storage system that is communicatively coupled to the on-premises cloud infrastructure, data associated with the cloud computing instance.

Providing non-volatile storage to cloud computing services

A storage system has storage devices and a storage controller that includes a processing device. The processing device is to receive a request for portions of data stored at locations of one or more storage devices. The processing device is to generate a file at a storage device. The file corresponds to a view of the portions of data stored at the locations of the one or more storage devices. The processing device is to identify the portions of data that are stored at the locations of the one or more storage devices. The processing device is to provide the view comprising the portions of data.

Enhanced data access using composite data views

Nonsequential readahead for deep learning training that includes: receiving an indication of a list of batch storage locations for a batch of data objects; prefetching, for each storage location in the list of batch storage locations, storage content corresponding to the batch of data objects; and storing the storage content corresponding to the batch of data objects within a cache accessible to an artificial intelligence workflow.

Training artificial intelligence workflows

Application placement for distributed applications, including: identifying, from amongst a plurality of disparate storage environments, a storage environment that contains data that can be utilized by a portion of a distributed application; and initiating execution of the portion of the distributed application in an execution environment that is communicatively coupled to the storage environment.

Intelligent application placement in a hybrid infrastructure

Enabling data portability between systems, including: submitting, to a cloud-based metadata store storing metadata describing data stored in a plurality of storage systems each coupled for data communications with a corresponding on-premises cloud infrastructure of a plurality of on-premises cloud infrastructures, a query; selecting a subset of the data responsive to the query and stored in at least one first storage system coupled to at least one first on-premises cloud infrastructure; and storing, from the at least one first storage system to a second storage system coupled to a second on-premises cloud infrastructure, the subset of the data responsive to the query.

Enabling data portability between systems

An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

Search acceleration for artificial intelligence

Accelerating artificial intelligence workflows, including: receiving, from a computing process of an artificial intelligence workflow, a request for information stored on a data repository; issuing, from a user space of an operating system environment, parallel requests to the data repository using a network protocol that operates serially at the kernel level of the operating system environment; receiving, from the data repository, one or more responses to the parallel requests; and providing, to the computing process of the artificial intelligence workflow and based on the one or more responses to the parallel requests, a response to the request for information.

Accelerating artificial intelligence ('AI') workflows

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 a machine learning model in an artificial intelligence infrastructure

Providing persistent storage to transient cloud computing services

Nonsequential readahead based on data access patterns, the method comprising: determining a set of access patterns for stored content; determining, based on the set of access patterns, a list of storage locations for content expected to be used; and prefetching, based on the list of storage locations for content expected to be used, one or more data objects.

Inferred nonsequential prefetch based on data access patterns

Non-sequential readahead for deep learning training

Batch building for artificial intelligence workflows, including: issuing, responsive to a request for a batch of data objects, requests to a data repository for multiple data objects stored among one or more directories; selecting, in accordance with a batch building policy, a subset of data objects based on one or more responses to the requests; and providing, to the artificial intelligence workflow, a batch of data objects that includes the subset of data objects.

Batch building for deep learning training workloads

Data transformation offloading in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit ('GPU') servers, including: storing, within the storage system, a dataset; identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to the dataset; and generating, by the storage system in dependence upon the one or more transformations, a transformed dataset.

Growing community of inventors

Mountain View, CA, United States of America

Emily Potyraj