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Description

The Elastic Fabric Adapter (EFA) serves as a specialized network interface for Amazon EC2 instances, allowing users to efficiently run applications that demand high inter-node communication at scale within the AWS environment. By utilizing a custom-designed operating system (OS) that circumvents traditional hardware interfaces, EFA significantly boosts the performance of communications between instances, which is essential for effectively scaling such applications. This technology facilitates the scaling of High-Performance Computing (HPC) applications that utilize the Message Passing Interface (MPI) and Machine Learning (ML) applications that rely on the NVIDIA Collective Communications Library (NCCL) to thousands of CPUs or GPUs. Consequently, users can achieve the same high application performance found in on-premises HPC clusters while benefiting from the flexible and on-demand nature of the AWS cloud infrastructure. EFA can be activated as an optional feature for EC2 networking without incurring any extra charges, making it accessible for a wide range of use cases. Additionally, it seamlessly integrates with the most popular interfaces, APIs, and libraries for inter-node communication needs, enhancing its utility for diverse applications.

Description

Amazon EC2 Auto Scaling ensures that your applications remain available by allowing for the automatic addition or removal of EC2 instances based on scaling policies that you set. By utilizing dynamic or predictive scaling policies, you can adjust the capacity of EC2 instances to meet both historical and real-time demand fluctuations. The fleet management capabilities within Amazon EC2 Auto Scaling are designed to sustain the health and availability of your instance fleet effectively. In the realm of efficient DevOps, automation plays a crucial role, and one of the primary challenges lies in ensuring that your fleets of Amazon EC2 instances can automatically launch, provision software, and recover from failures. Amazon EC2 Auto Scaling offers vital functionalities for each phase of instance lifecycle automation. Furthermore, employing machine learning algorithms can aid in forecasting and optimizing the number of EC2 instances needed to proactively manage anticipated changes in traffic patterns. By leveraging these advanced features, organizations can enhance their operational efficiency and responsiveness to varying workload demands.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon EC2
Amazon Web Services (AWS)
AWS HPC
AWS Nitro System
AWS ParallelCluster
Amazon
Amazon EC2 UltraClusters
Caffe
Chainer
MXNet
Observe
OpenFOAM
PyTorch
SAP Store
TensorFlow

Integrations

Amazon EC2
Amazon Web Services (AWS)
AWS HPC
AWS Nitro System
AWS ParallelCluster
Amazon
Amazon EC2 UltraClusters
Caffe
Chainer
MXNet
Observe
OpenFOAM
PyTorch
SAP Store
TensorFlow

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

United States

Founded

1994

Country

United States

Website

aws.amazon.com/hpc/efa/

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/ec2/autoscaling/

Product Features

HPC

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Alternatives

Alternatives

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