NEWS
Tags: 

Google launches GPUs in the cloud

The Google Cloud Platform has received a performance boost as Google launch a public beta allowing users to deploy NVIDIA Tesla K80 GPUs.

GPUs can be particularly useful for highly parallel workloads and Google is targeting application areas such as machine learning in the hopes that more customers will begin using the cloud platform for compute-intensive workloads.

Google is supporting machine learning workloads through the use of popular machine learning and deep learning frameworks such as TensorFlow, Theano, Torch, MXNet, and Caffe, as well as NVIDIA’s popular CUDA software for building GPU-accelerated applications.

The new Google Cloud GPUs are tightly integrated with Google Cloud Machine Learning (Cloud ML), which aims to slash the time it takes to train machine learning models at scale using the TensorFlow framework.

Cloud ML is a fully-managed service that provides end-to-end training and prediction workflow with cloud computing tools such as Google Cloud DataflowGoogle BigQueryGoogle Cloud Storage and Google Cloud Datalab.

Google is also offering a CloudML Bootcamp to teach new users how to Supercharge performance using GPUs in the cloud More information and documentation are available on the Google Cloud website.

However, it is not just machine learning workflows that can benefit from GPU acceleration. The company also recommends that GPUs can accelerate many workflows including video and image transcoding, seismic analysis, molecular modelling, genomics, computational finance, simulations, high-performance data analysis, computational chemistry, finance, fluid dynamics, and visualisation.

Company: 
Twitter icon
Google icon
Del.icio.us icon
Digg icon
LinkedIn icon
Reddit icon
e-mail icon
Feature

Robert Roe reports on developments in AI that are helping to shape the future of high performance computing technology at the International Supercomputing Conference

Feature

James Reinders is a parallel programming and HPC expert with more than 27 years’ experience working for Intel until his retirement in 2017. In this article Reinders gives his take on the use of roofline estimation as a tool for code optimisation in HPC

Feature

Sophia Ktori concludes her two-part series exploring the use of laboratory informatics software in regulated industries.

Feature

As storage technology adapts to changing HPC workloads, Robert Roe looks at the technologies that could help to enhance performance and accessibility of
storage in HPC