Intel Corporation

Meenakshi Arunachalam

Vikram A Saletore

Deepthi Karkada

Adam Procter

Mohan J Kumar

Rahul Khanna

Devadatta V Bodas

Justin J Song

Charles R Yount

Muralidhar Rajappa

Brian J Griffith

Ramakrishna Huggahalli

Kushal Datta

Ilya Sharapov

Scott B Huck

Vishal Verma

Arun Tejusve Raghunath Rajan

Vamsi Sripathi

Lingdan (Linda) Zeng

Methods, apparatus, systems and articles of manufacture for distributed training of a neural network are disclosed. An example apparatus includes a neural network trainer to select a plurality of training data items from a training data set based on a toggle rate of each item in the training data set. A neural network parameter memory is to store neural network training parameters. A neural network processor is to generate training data results from distributed training over multiple nodes of the neural network using the selected training data items and the neural network training parameters. The neural network trainer is to synchronize the training data results and to update the neural network training parameters.

Methods and apparatus for distributed training of a neural network

Systems, apparatuses and methods may provide for technology that identifies a first set of compute nodes and a second set of compute nodes, wherein the first set of compute nodes execute more slowly than the second set of compute nodes. The technology may also automatically determine a compute node configuration that results in a relatively low difference in completion time between the first set of compute nodes and the second set of compute nodes with respect to a neural network workload. In an example, the technology applies the compute node configuration to an execution of the neural network workload on one or more nodes in the first set of compute nodes and one or more nodes in the second set of compute nodes.

Automated resource usage configurations for deep learning neural network workloads on multi-generational computing architectures

Systems, apparatuses and methods may provide for technology that conducts a first timing measurement of a blockage timing of a first window of the training of the neural network. The blockage timing measures a time that processing is impeded at layers of the neural network during the first window of the training due to synchronization of one or more synchronizing parameters of the layers. Based upon the first timing measurement, the technology is to determine whether to modify a synchronization barrier policy to include a synchronization barrier to impede synchronization of one or more synchronizing parameters of one of the layers during a second window of the training. The technology is further to impede the synchronization of the one or more synchronizing parameters of the one of the layers during the second window if the synchronization barrier policy is modified to include the synchronization barrier.

Synchronization scheduler of distributed neural network training

A method of assessing energy efficiency of a High-performance computing (HPC) system, including: selecting a plurality of HPC workloads to run on a system under test (SUT) with one or more power constraints, wherein the SUT includes a plurality of HPC nodes in the HPC system, executing the plurality of HPC workloads on the SUT, and generating a benchmark metric for the SUT based on a baseline configuration for each selected HPC workload and a plurality of measured performance per power values for each executed workload at each selected power constraint is shown.

Method to assess energy efficiency of HPC system operated with and without power constraints

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Portland, OR, United States of America

Meenakshi Arunachalam