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ease
features
design
support

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Write a Review

Description

AWS ParallelCluster is a free, open-source tool designed for efficient management and deployment of High-Performance Computing (HPC) clusters within the AWS environment. It streamlines the configuration of essential components such as compute nodes, shared filesystems, and job schedulers, while accommodating various instance types and job submission queues. Users have the flexibility to engage with ParallelCluster using a graphical user interface, command-line interface, or API, which allows for customizable cluster setups and oversight. The tool also works seamlessly with job schedulers like AWS Batch and Slurm, making it easier to transition existing HPC workloads to the cloud with minimal adjustments. Users incur no additional costs for the tool itself, only paying for the AWS resources their applications utilize. With AWS ParallelCluster, users can effectively manage their computing needs through a straightforward text file that allows for the modeling, provisioning, and dynamic scaling of necessary resources in a secure and automated fashion. This ease of use significantly enhances productivity and optimizes resource allocation for various computational tasks.

Description

NVIDIA Base Command Manager provides rapid deployment and comprehensive management for diverse AI and high-performance computing clusters, whether at the edge, within data centers, or across multi- and hybrid-cloud settings. This platform automates the setup and management of clusters, accommodating sizes from a few nodes to potentially hundreds of thousands, and is compatible with NVIDIA GPU-accelerated systems as well as other architectures. It facilitates orchestration through Kubernetes, enhancing the efficiency of workload management and resource distribution. With additional tools for monitoring infrastructure and managing workloads, Base Command Manager is tailored for environments that require accelerated computing, making it ideal for a variety of HPC and AI applications. Available alongside NVIDIA DGX systems and within the NVIDIA AI Enterprise software suite, this solution enables the swift construction and administration of high-performance Linux clusters, thereby supporting a range of applications including machine learning and analytics. Through its robust features, Base Command Manager stands out as a key asset for organizations aiming to optimize their computational resources effectively.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Batch
AWS Elastic Fabric Adapter (EFA)
AWS HPC
AWS Lambda
AWS Parallel Computing Service
Amazon API Gateway
Amazon Web Services (AWS)
GitHub
Kubernetes
NVIDIA AI Enterprise
NVIDIA Base Command
NVIDIA Cumulus Linux
NVIDIA DGX Cloud
NVIDIA virtual GPU
Python
Slurm

Integrations

AWS Batch
AWS Elastic Fabric Adapter (EFA)
AWS HPC
AWS Lambda
AWS Parallel Computing Service
Amazon API Gateway
Amazon Web Services (AWS)
GitHub
Kubernetes
NVIDIA AI Enterprise
NVIDIA Base Command
NVIDIA Cumulus Linux
NVIDIA DGX Cloud
NVIDIA virtual GPU
Python
Slurm

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

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/hpc/parallelcluster/

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

www.nvidia.com/en-us/data-center/base-command/manager/

Product Features

Product Features

Alternatives

TrinityX Reviews

TrinityX

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Alternatives

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TrinityX

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