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 Dataflow, Google BigQuery, Google Cloud Storage and Google Cloud Datalab.
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.