AWS ParallelCluster 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.
AWS ParallelCluster Alternatives
JS7 JobScheduler
JS7 JobScheduler, an Open Source Workload Automation System, is designed for performance and resilience. JS7 implements state-of-the-art security standards. It offers unlimited performance for parallel executions of jobs and workflows.
JS7 provides cross-platform job execution and managed file transfer. It supports complex dependencies without the need for coding. The JS7 REST-API allows automation of inventory management and job control.
JS7 can operate thousands of Agents across any platform in parallel.
Platforms
- Cloud scheduling for Docker®, OpenShift®, Kubernetes® etc.
- True multi-platform scheduling on premises, for Windows®, Linux®, AIX®, Solaris®, macOS® etc.
- Hybrid cloud and on-premises use
User Interface
- Modern GUI with no-code approach for inventory management, monitoring, and control using web browsers
- Near-real-time information provides immediate visibility to status changes, log outputs of jobs and workflows.
- Multi-client functionality, role-based access management
- OIDC authentication and LDAP integration
High Availability
- Redundancy & Resilience based on asynchronous design and autonomous Agents
- Clustering of all JS7 Products, automatic fail-over and manual switch-over
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OpenMetal
OpenMetal reimagines Infrastructure as a Service (IaaS) by delivering high-performance, OpenStack-powered private clouds, bare metal dedicated servers, and GPU clusters. Our platform is designed to scale with any organization, from agile startups to established enterprises.
Historically, the power of a private cloud was gated by massive capital requirements and technical complexity. Because managing dedicated infrastructure demands specialized expertise and heavy hardware investment, it remained an exclusive tool for the world's largest corporations.
OpenMetal changes that dynamic. We provide the sovereignty and agility of a private environment without the traditional burdens of manual construction or maintenance.
-Rapid Deployment: Go live in as little as 45 seconds.
-Full Control: Manage your own dedicated infrastructure immediately.
-Accessibility: High-level cloud technology tailored for budgets of all sizes.
We view open source not just as a software model, but as a global engine for progress. By fostering international collaboration and collective innovation, open source empowers individuals to build upon existing successes to create something better for everyone.
Our goal is to streamline the path to open-source adoption. By removing technical friction, we enable teams and individuals to focus on what matters: contributing to the community and driving the future of IT.
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Azure CycleCloud
Design, oversee, operate, and enhance high-performance computing (HPC) and large-scale compute clusters seamlessly. Implement comprehensive clusters and additional resources, encompassing task schedulers, computational virtual machines, storage solutions, networking capabilities, and caching systems. Tailor and refine clusters with sophisticated policy and governance tools, which include cost management, integration with Active Directory, as well as monitoring and reporting functionalities. Utilize your existing job scheduler and applications without any necessary changes. Empower administrators with complete authority over job execution permissions for users, in addition to determining the locations and associated costs for running jobs. Benefit from integrated autoscaling and proven reference architectures suitable for diverse HPC workloads across various sectors. CycleCloud accommodates any job scheduler or software environment, whether it's proprietary, in-house solutions or open-source, third-party, and commercial software. As your requirements for resources shift and grow, your cluster must adapt accordingly. With scheduler-aware autoscaling, you can ensure that your resources align perfectly with your workload needs while remaining flexible to future changes. This adaptability is crucial for maintaining efficiency and performance in a rapidly evolving technological landscape.
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Bright Cluster Manager
Bright Cluster Manager offers a variety of machine learning frameworks including Torch, Tensorflow and Tensorflow to simplify your deep-learning projects.
Bright offers a selection the most popular Machine Learning libraries that can be used to access datasets. These include MLPython and NVIDIA CUDA Deep Neural Network Library (cuDNN), Deep Learning GPU Trainer System (DIGITS), CaffeOnSpark (a Spark package that allows deep learning), and MLPython.
Bright makes it easy to find, configure, and deploy all the necessary components to run these deep learning libraries and frameworks. There are over 400MB of Python modules to support machine learning packages. We also include the NVIDIA hardware drivers and CUDA (parallel computer platform API) drivers, CUB(CUDA building blocks), NCCL (library standard collective communication routines).
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Integrations
Company Details
Company:
Amazon
Year Founded:
1994
Headquarters:
United States
Website:
aws.amazon.com/hpc/parallelcluster/
Media
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Webinars
In Person
Training Videos
Customer Support
Business Hours
Live Rep (24/7)
Online Support
AWS ParallelCluster Features and Options
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