Intel Corporation

Srinivas Sridharan

Dipankar Das

Dheevatsa Mudigere

Karthikeyan Vaidyanathan

Naveen K Mellempudi

Dhiraj D Kalamkar

Mikhail E Smorkalov

Elmoustapha Ould-Ahmed-Vall

Eriko Nurvitadhi

Chandrasekaran Sakthivel

Amit Bleiweiss

Deborah T Marr

Jeffrey J Cook

Asit K Mishra

Dhawal Srivastava

Anavai Ramesh

Mohammad Ashraf Bhuiyan

Md Faijul Amin

Niharika Maheshwari

Wei W Wang

Aravindh V Anantaraman

Wei D Wang

Wei Wang

James Dinan

Ajaya V Durg

Mohammad R Haghighat

Venkata Krishnan

Sudarshan Srinivasan

David A Webb

Embodiments described herein provide an apparatus comprising an interconnect switch configured to couple with a plurality of graphics processors via a plurality of point-to-point interconnects and one or more processors including a graphics processor coupled with the interconnect switch via a point-to-point interconnect of the plurality of point-to-point interconnects.

Communication optimizations for distributed machine learning

An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

Machine learning accelerator mechanism

One embodiment provides for a graphics processing unit to perform computations associated with a neural network, the graphics processing unit comprising a hardware processing unit having a dynamic precision fixed-point unit that is configurable to convert elements of a floating-point tensor to convert the floating-point tensor into a fixed-point tensor.

Dynamic precision management for integer deep learning primitives

One embodiment provides for a method of transmitting data between multiple compute nodes of a distributed compute system, the method comprising creating a global view of communication operations to be performed between the multiple compute nodes of the distributed compute system, the global view created using information specific to a machine learning model associated with the distributed compute system; using the global view to determine a communication cost of the communication operations; and automatically determining a number of network endpoints for use in transmitting the data between the multiple compute nodes of the distributed compute system.

Abstraction layers for scalable distributed machine learning

One embodiment provides for a graphics processing unit including a fabric interface configured to transmit gradient data stored in a memory device of the graphics processing unit according to a pre-defined communication operation. The memory device is a physical memory device shared with a compute block of the graphics processing unit and the fabric interface. The fabric interface automatically transmits the gradient data stored in memory to a second distributed training node based on an address of the gradient data in the memory device.

Hardware implemented point to point communication primitives for machine learning

One embodiment provides for a method of transmitting data between multiple compute nodes of a distributed compute system, the method comprising multi-dimensionally partitioning data of a feature map across multiple nodes for distributed training of a convolutional neural network; performing a parallel convolution operation on the multiple partitions to train weight data of the neural network; and exchanging data between nodes to enable computation of halo regions, the halo regions having dependencies on data processed by a different node.

Data parallelism and halo exchange for distributed machine learning

One embodiment provides for a system to configure distributed training of a neural network. The system includes memory to store a library to facilitate transmission of data during distributed training of the neural network; a network interface to transmit and receive gradient data associated with the trainable parameters; a general-purpose processor to execute instructions provided by the library, the instructions to cause the general-purpose processor to configure the network interface to transmit and receive the gradient data associated with the trainable parameters during a workflow of a machine learning framework; and a graphics processor to perform compute operations associated with machine learning framework workflow to generate the gradient data associated with the trainable parameters, wherein, based on the machine learning framework workflow, the library is to interleave the compute operations on the graphics processor with transmission and receipt of gradient data via the network interface.

Fine-grain compute communication execution for deep learning frameworks via hardware accelerated point-to-point primitives

Embodiments described herein provide a system to configure distributed training of a neural network, the system comprising memory to store a library to facilitate data transmission during distributed training of the neural network; a network interface to enable transmission and receipt of configuration data associated with a set of worker nodes, the worker nodes configured to perform distributed training of the neural network; and a processor to execute instructions provided by the library. The instructions cause the processor to create one or more groups of the worker nodes, the one or more groups of worker nodes to be created based on a communication pattern for messages to be transmitted between the worker nodes during distributed training of the neural network. The processor can transparently adjust communication paths between worker nodes based on the communication pattern.

One embodiment provides for a graphics processing unit to perform computations associated with a neural network, the graphics processing unit comprising a hardware processing unit having a dynamic precision fixed-point unit that is configurable to quantize elements of a floating-point tensor to convert the floating-point tensor into a dynamic fixed-point tensor.

One embodiment provides for a system to compute and distribute data for distributed training of a neural network, the system including first memory to store a first set of instructions including a machine learning framework; a fabric interface to enable transmission and receipt of data associated with the set of trainable machine learning parameters; a first set of general-purpose processor cores to execute the first set of instructions, the first set of instructions to provide a training workflow for computation of gradients for the trainable machine learning parameters and to communicate with a second set of instructions, the second set of instructions facilitate transmission and receipt of the gradients via the fabric interface; and a graphics processor to perform compute operations associated with the training workflow to generate the gradients for the trainable machine learning parameters.

Growing community of inventors

Bengaluru, India

Srinivas Sridharan