NVIDIA’s Tesla P100 Compute Accelerator Boosts Google’s Cloud Platform – Tesla K80 Also Available For High Performance, Scalable Virtual Machines
Google has announced that they have upgraded their cloud platform to utilize NVIDIA’s latest Tesla P100 compute accelerator. Based on the Pascal architecture, users can expect an increase of up to 10x compared to older accelerators.
Google Boosts Cloud Platform and Compute Engine With NVIDIA’s Pascal Based Tesla P100 Accelerator
The new cloud solutions are now equipped with faster GPUs that allow for various workloads such as machine learning training and inference, geophysical data processing, simulation, seismic analysis, molecular modeling, genomics and many more high performance compute use cases.
Compared to traditional solutions, Cloud GPUs provide an unparalleled combination of flexibility, performance and cost-savings:
- Flexibility: Google’s custom VM shapes and incremental Cloud GPUs provide the ultimate amount of flexibility. Customize the CPU, memory, disk and GPU configuration to best match your needs.
- Fast performance: Cloud GPUs are offered in passthrough mode to provide bare-metal performance. Attach up to 4 P100 or 8 K80 per VM (we offer up to 4 K80 boards, that come with 2 GPUs per board). For those looking for higher disk performance, optionally attach up to 3TB of Local SSD to any GPU VM.
- Low cost: With Cloud GPUs you get the same per-minute billing and Sustained Use Discounts that you do for the rest of GCP’s resources. Pay only for what you need!
- Cloud integration: Cloud GPUs are available at all levels of the stack. For infrastructure, Compute Engine and Container Engine (supported on alpha clusters only) allow you to run your GPU workloads with either VMs or containers. For machine learning, Cloud Machine Learning can be optionally configured to utilize GPUs in order to reduce the time it takes to train your models at scale with TensorFlow.
“For certain tasks, [NVIDIA] GPUs are a cost-effective and high-performance alternative to traditional CPUs. They work great with Shazam’s core music recognition workload, in which we match snippets of user-recorded audio fingerprints against our catalog of over 40 million songs. We do that by taking the audio signatures of each and every song, compiling them into a custom database format and loading them into GPU memory. Whenever a user Shazams a song, our algorithm uses GPUs to search that database until it finds a match. This happens successfully over 20 million times per day.” — Ben Belchak, Head of Site Reliability Engineering, Shazam
With today’s announcement, you can now deploy both the NVIDIA Tesla P100 and K80 GPUs in four regions worldwide. All Google GPUs can now take advantage of sustained use discounts, which automatically lower the price (up to 30%), of your virtual machines when you use them to run sustained workloads. No lock-in or upfront minimum fee commitments are needed to take advantage of these discounts.