Intel Corporation

Karthikeyan Vaidyanathan

Dipankar Das

Srinivas Sridharan

Dhiraj D Kalamkar

Chandrasekaran Sakthivel

Mikhail E Smorkalov

Akshay Ramesh Kadam

Sanjeev N Trika

Richard Paul Mangold

Paritosh Saxena

Nicholas D Triantafillou

Paul J Thadikaran

Mark E Scott-Nash

Royce Fernald

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

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

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 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.

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 to 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.

One embodiment provides for a non-transitory machine readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising providing an interface to define a neural network using machine-learning domain specific terminology, wherein the interface enables selection of a neural network topology and abstracts low-level communication details of distributed training of the neural network.

Abstraction library to enable scalable distributed machine learning

Described herein are articles, systems, and methods for using a storage controller to protect secure data blocks through the enforcement of a read only policy. In some embodiments, the articles use a combination of hardware protections and software protections (e.g., virtualization) to protect a system against attack from malware while such secure data is updated. Also described are systems and methods for securely updating data blocks secured in this fashion, and detecting and preventing the corruption of data stored on secondary storage media using a disk eventing mechanism.

Methods and apparatus for a safe and secure software update solution against attacks from malicious or unauthorized programs to update protected secondary storage

An anti-malware approach uses a storage drive with the capability to lock selected memory areas. Platform assets such as OS objects are stored in the locked areas and thus, unauthorized changes to them may not be made by an anti-malware entity.

Growing community of inventors

Bengaluru, India

Karthikeyan Vaidyanathan