Emc IP Holding Company LLC

Dell Products Inc.

Christopher S MacLellan

Jinpeng Liu

Jin Li

Zhen Jia

Andrew Anzhou Hou

John S Harwood

Robert Anthony Lincourt, Jr

Victor Fong

William Jeffery White

Douglas L Farley

A decoupled artificial intelligence model management architecture comprises a front-end component, a scheduler component and a back-end component configured to enable an open and dynamic system which accepts any models from any formats to be deployed on any target devices and to dynamically change scheduling of parallelism, parameter movements and computation executions across the target devices.

Decoupled architecture for artificial intelligence model management

Techniques described herein relate to a method for managing workflows. The method may include obtaining, from a client, by a registration manager associated with accelerator pools, a request to perform a portion of a workflow using accelerators; identifying a minimum quantity and a maximum quantity of accelerators associated with the request; identifying a storage to store data associated with the portion of the workflow; identifying an accelerator pool of the accelerator pools that includes at least the maximum quantity of accelerators; establishing a connection between the client, the storage, and accelerators of the accelerator pool to perform the portion of the workflow; and initiating the performance of the portion of the workflow using the storage, the client, and the accelerators of the accelerator pool.

Method and system for managing accelerator pools to perform subportions of portions of workflows

Techniques are disclosed for managing artificial intelligence model partitions for execution in an information processing system with edge computing resources. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A computation graph is generated based on the intermediate representation. The computation graph is partitioned into a set of partitions. The method then schedules the set of partitions for respective execution on a set of computing devices in an edge computing environment, and causes deployment of the set of partitions respectively to the set of computing devices for execution in the edge computing environment.

Managing artificial intelligence model partitions for edge computing environment

Techniques are disclosed for scheduling artificial intelligence model partitions for execution in an information processing system. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A reversed computation graph corresponding to a computation graph generated based on the intermediate representation is obtained. Nodes in the reversed computation graph represent functions related to the artificial intelligence model, and one or more directed edges in the reversed computation graph represent one or more dependencies between the functions. The reversed computation graph is partitioned into sequential partitions, such that the partitions are executed sequentially and functions corresponding to nodes in each partition are executed in parallel.

Scheduling artificial intelligence model partitions based on reversed computation graph

One example method includes transmitting, by an application running in a host CPU, a notification to an application in a coprocessor/accelerator indicating that inbound data generated by the application is ready, receiving, by the application in the coprocessor/accelerator, the notification and delegating, by the application in the coprocessor/accelerator, an IO command to the application running in the host CPU, forwarding, by the application running in the host CPU, the IO command to an OS of the host CPU, transmitting, by the OS of the host CPU, an IO request to an IO device, initiating, by the IO device, a P2PDMA to transmit data associated with the IO request to a memory of the coprocessor/accelerator, and processing, by the application in the coprocessor/accelerator, the data.

Growing community of inventors

Uxbridge, MA, United States of America

Christopher S MacLellan