Computing power scheduling and management
Cloud-Native GPU Cluster Management
An efficient and intelligent cloud-native GPU cluster management and computing power scheduling platform to achieve efficient resource utilization and fast task execution. This platform is designed to optimize the management of GPU resources in cloud environments, ensuring high utilization rates and reduced task execution times. The architecture combines advanced scheduling algorithms, containerization, and AI-driven decision-making to dynamically allocate resources based on workload requirements.
Advantages
Comprehensive cloud native support
Seamlessly integrate into the cloud-native ecosystem, simplify the management complexity of GPU clusters and nodes, significantly improve operation and maintenance efficiency, and allow technical teams to focus more on business innovation.
Flexible computing power scheduling
Provides a variety of scheduling strategies and resource optimization solutions to accurately match different task requirements, effectively accelerate model training, and improve overall computing performance.
Intuitive cluster monitoring
Real-time monitoring of cluster resource usage helps users achieve optimal resource allocation and dynamic balance through detailed data analysis, ensuring efficient and stable operation of the cluster.
Powerful computing power management
It supports unified management of heterogeneous computing power, realizes scheduling management at the level of thousands or tens of thousands of cards, and enables multi-department and multi-task collaboration to improve computing efficiency.
Capabilities
Comprehensive cloud-native management
The platform enables unified monitoring and management of heterogeneous computing power, encompassing GPU clusters, nodes, naming conventions, user management, and other key components. It offers detailed, traceable functionalities for efficient oversight, ensuring accurate resource utilization. Additionally, it provides comprehensive operation logs and audit trails to support secure and transparent management practices, ensuring that all actions are recorded and easily retrievable for security and accountability purposes.
Flexible scheduling strategy
The platform supports multiple scheduling strategies, including Kubernetes (K8s), Volcano, and custom-defined strategies. It automatically matches computing resources to task requirements, ensuring fast execution with minimal delays. Once a task is completed, the platform autonomously releases the resources, optimizing resource utilization and preventing unnecessary consumption. This dynamic allocation and deallocation of resources help maintain an efficient and responsive computing environment.
Distributed Scheduling
The platform features a powerful scheduling engine capable of managing thousands, or even tens of thousands, of card-level computing resources. It supports multiple modes to address complex computational needs, offering flexibility in scheduling and resource allocation. A personalized placement group strategy is integrated to enhance computing efficiency, ensuring optimal task placement based on workload requirements. This strategy significantly reduces task completion times by minimizing resource contention and improving data locality, further accelerating overall performance.
Convenient task submission
The platform includes a user-friendly visual interface that enables one-click submission of distributed tasks, simplifying the process for users. It comes with built-in support for common computing frameworks, ensuring seamless integration with popular tools. Additionally, the platform offers a mirroring acceleration function, which reduces distribution time by efficiently replicating necessary resources. This feature enhances overall efficiency by minimizing delays in task deployment and enabling faster execution, optimizing the use of available computing power.
Powerful computing power split
The platform supports multi-instance operation of graphics cards, pass-through technology, and multi-node parallel computing to maximize GPU utilization efficiency. By enabling these features, it allows for the concurrent use of multiple GPU instances on a single card, improving resource allocation and task parallelization. Additionally, the platform supports flexible allocation and memory segmentation across multi-brand GPU cards with customized specifications, ensuring that diverse hardware configurations are efficiently utilized and optimized for specific workload requirements, enhancing overall performance and scalability.
Computing power pool management
The platform allows the creation of shared or exclusive computing power pools to meet the resource sharing needs of teams or specific project requirements. This enables efficient allocation of computing resources based on the needs of different users or projects. Additionally, a single GPU card can be shared among different tenants through time-sharing usage, allowing multiple users to access the same hardware at different times. This approach optimizes resource utilization, ensuring that GPU resources are maximally utilized without unnecessary downtime, while maintaining isolation and security between tenants.
Application Scenarios
Computing Center Builder
Enables builders to manage computing power resources more efficiently, maximizing resource utilization and controlling costs.
Intelligent Computing Center Operator
Allows operators to provide differentiated services, meeting the needs of different customers while improving service quality and customer satisfaction.
Enterprise Resource Management
Helps enterprises manage and optimize internal computing resources, improving resource utilization and reducing operating costs.
Customer Management and Marketing
Enables refined customer management and marketing operations to enhance user conversion rates and repurchase rates.
AI Model Training
Efficiently schedules and manages GPU resources to accelerate the model training process and improve training efficiency.