Elastic Fabric Adapter

EFA’s unique OS bypass networking mechanism provides a low-latency, low-jitter channel for inter-instance communications. This enables your tightly-coupled HPC or distributed machine learning applications to scale to thousands of cores, making your applications run faster.

You can enable EFA support on a growing list of EC2 instances and get the flexibility to choose the right compute configuration for your workload. Simply change your cluster configurations as your needs change and enable EFA support on your new compute instances. No prior reservations or upfront planning is needed.

EFA uses libfabric interface and libfabric APIs for communications. Because almost all HPC programming models support this interface, you can migrate your existing HPC applications to the cloud with little to no modifications.

Advances in Computational Fluid Dynamics (CFD) algorithms enable engineers to simulate increasingly complex flow phenomena, and HPC helps reduce turn-around times. With EFA, design engineers can now scale out their simulation jobs to experiment with more tunable parameters, leading to faster, more accurate results.

Complex weather models require high memory bandwidth, fast interconnects, and robust parallel file systems to deliver accurate results. The closer the grid spacing on the model, the more accurate the results—and the more computational resources the model requires. EFA offers a fast interconnect that allows weather modelling applications to take advantage of the virtually unlimited scaling capabilities of the AWS cloud and get more accurate predictions in less time.

The training of deep learning models can be significantly accelerated with distributed computing on GPUs. Leading deep learning frameworks such as Caffe,Caffe2, Chainer, MxNet, TensorFlow, and PyTorch have already integrated NCCL to take advantage of its multi-GPU collectives for across nodes communications. EFA is optimized for NCCL on AWS, improving the throughput and scalability of these training models, which leads to faster results.