AWS is excited to announce that the new Amazon EC2 P4d instances are now generally available. This instance type brings additional benefits with 2.5x higher deep learning performance; adding to the accelerated instances portfolio, new features, and technical breakthroughs that our customers can benefit from with this latest technology.
Amazon EC2 P4d instances are deployed in hyperscale clusters called EC2 UltraClusters that are comprised of the highest performance compute, networking, and storage in the cloud. Each EC2 UltraCluster is one of the most powerful supercomputers in the world, enabling customers to run their most complex multi-node ML training and distributed HPC workloads. Customers can easily scale from a few to thousands of NVIDIA A100 GPUs in the EC2 UltraClusters based on their ML or HPC project needs.
Researchers, data scientists, and developers can leverage P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as run HPC applications such as pharmaceutical discovery, seismic analysis, and financial modeling. Unlike on-premises systems, customers can access virtually unlimited compute and storage capacity, scale their infrastructure based on business needs, and spin up a multi-node ML training job or a tightly coupled distributed HPC application in minutes, without any setup or maintenance costs.
As you can see from the generalized block diagram above, the p4d comes with dual socket Intel Cascade Lake 8275CL processors totaling 96 vCPUs at 3.0 GHz with 1.1 TB of RAM and 8 TB of NVMe local storage. P4d also comes with 8 x 40 GB NVIDIA Tesla A100 GPUs with NVSwitch and 400 Gbps Elastic Fabric Adapter (EFA) enabled networking. This instance configuration represents the latest generation of computing for our customers spanning Machine Learning (ML), High Performance Computing (HPC), and analytics.
One of the improvements of the p4d is in the networking stack. This new instance type has 400 Gbps with support for EFA and GPUDirect RDMA. Now, on AWS, you can take advantage of point-to-point GPU to GPU communication (across nodes), bypassing the CPU. Look out for additional blogs and webinars detailing use cases of GPUDirect and how this feature helps decrease latency and improve performance for certain workloads.
Check out our new blog post detailing some of those key features and how to integrate them into your current workloads and architectures.