Adaptive Cluster Manager
Streamlined Cluster Management
Adaptive Cluster Manager is a cluster management solution for efficiently building, deploying and managing Linux-based HPC & AI clusters consisting of Head node(s), Compute nodes and Storage. Adaptive’s solution is designed to simplify and optimize the effort involved in deploying, managing, and scaling high-performance computing (HPC) clusters across diverse environments and scales. We’ve focused on simplicity and value, providing practical, affordably priced options, making it easier to meet your cluster management needs effectively and within budget. Our goal was to create a straightforward, turn-key solution for cluster management. Adaptive Cluster Manager (ACM) supports on-premises environments with optional cloud bursting to major providers including AWS, Azure, OCI, and GCP.
Manage large-scale HPC clusters with advanced resource allocation. Whether managing traditional HPC & AI clusters, or hybrid cloud environments, Adaptive Cluster Manager is designed to meet the evolving needs of modern computing infrastructures.
Solutions to HPC & AI Cluster Management Challenges
Adaptive Cluster Manager (ACM) offers a streamlined, cost-effective solution for managing and optimizing HPC and AI clusters. ACM addresses the following challenges faced by organizations looking to efficiently manage high-performance computing and AI environments:
- Complex AI and HPC Cluster Management increases administrative burden as manual processes slow down deployment, resource management, and monitoring across multiple clusters.
- Wasted Computing Power occurs when resources are underutilized, leading to inefficiencies as AI and HPC workloads are stuck on overloaded nodes while others remain idle.
- Fragmented Tools for AI Workloads make it difficult to manage resources efficiently, with administrators needing multiple tools to handle job scheduling, resource allocation, and monitoring for different applications.
- Inconsistent AI and HPC Policies complicate workload management across clusters, leading to inefficiencies and difficulty maintaining consistent SLAs across departments and teams.
- Data Transfer Delays affect AI workflows as large datasets must be moved between on-premises and cloud environments, causing bottlenecks and slowing progress.
Maximize AI and HPC Performance with Adaptive Cluster Manager
Adaptive Cluster Manager accelerates AI and HPC productivity by simplifying cluster management, optimizing resource utilization, and supporting a wide range of computing environments. ACM offers the following key benefits:
- Rapid Deployment and AI-Ready Integration with support for multiple Linux distributions, enabling quick installation on bare metal or virtual environments, making it easy to get AI and HPC workloads up and running quickly.
- Optimized AI Workload Management with intelligent, automated scheduling and resource allocation, ensuring efficient use of compute resources for high-performance AI applications and HPC workloads.
- Unified Management Interface featuring both a web-based GUI and powerful command-line tools, providing administrators with full control over AI and HPC operations through a single, intuitive platform.
- Automated Node Provisioning for AI Workloads simplifies the configuration and scaling of compute nodes, helping to reduce manual processes and streamline AI training and inferencing tasks.
- Real-Time Cluster Monitoring offers detailed insights into AI and HPC performance metrics, including GPU utilization, enabling administrators to proactively manage workloads and optimize system health.
- Scalable Architecture for AI and HPC ensures that ACM can meet the needs of both small AI training clusters and large-scale HPC environments, allowing seamless scalability as your workload demands grow.
- Seamless Cloud Bursting for AI and HPC allows organizations to dynamically extend on-premises resources to the cloud, providing extra capacity during peak demand periods for AI training and HPC simulations without permanent infrastructure investments.
- Secure User Management for AI Teams with role-based access control (RBAC), ensuring that multiple teams working on AI and HPC projects can securely access the resources they need without compromising security or efficiency.
- Support for AI-Driven Workloads including full compatibility with NVIDIA CUDA and OpenCL, empowering organizations to leverage GPUs and other accelerators for AI model training, deep learning, and high-performance applications.
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