University and research Institutions
Enhancing AI Research with Smarter Resource Management
Addressing the computing bottlenecks and management challenges in AI education and scientific research in universities, solutions have been developed to tackle issues such as cost, resource allocation, and sudden demand. These solutions include multi-heterogeneous resource scheduling, hybrid deployment, multi-campus interconnection, self-service operation, and automated operation and maintenance tools. Together, they provide robust support for scientific research innovation and contribute to the sustainable development of the scientific research ecosystem.
Capabilities
GPU Virtualization and Segmentation for Enhanced Resource Sharing
Efficiently allocate GPU resources to multiple users or tasks using advanced virtualization technology. This ensures that each task maintains its own performance while sharing the same physical GPU. Critical research projects can be allocated exclusive resources, while non-critical tasks share the remaining resources. Dynamic scheduling algorithms optimize overall GPU utilization, improving efficiency across various tasks.
Diverse and Heterogeneous Architecture for Robust Computing Power
One unified platform manages heterogeneous resources like CPU, GPU, storage, and network to support a wide range of computing-intensive and data-intensive tasks. It guarantees that scientific research projects receive the necessary processing power, speeding up research and maximizing resource utilization. Teachers and students can select the appropriate resources based on their needs, avoiding waste and ensuring cost-effectiveness.
Hybrid Deployment for Flexible and Secure Operations
Enable seamless deployment of scientific research tasks on either private platforms or cloud services, based on project requirements and data sensitivity. This ensures data security while allowing for scalable and flexible resource expansion to meet unexpected demands, providing timely and adequate computing power for research projects.
Cross-Team Collaboration with Multi-Campus Interconnection
Distributed architecture and a unified resource management interface facilitate seamless sharing of computing resources between research teams across multiple campuses. This simplifies collaboration, enhances research efficiency, and promotes innovation by enabling better cross-campus cooperation on scientific projects.
Simplified Self-Service Operation and Maintenance
Researchers can independently apply for computing resources, while administrators can easily monitor resource usage via visual tools. Automated operation and maintenance tools reduce the workload of management teams, ensuring smooth system operation and enhancing the overall user experience.
Challenges
Cost pressure of new infrastructure construction
The construction and maintenance of resources such as GPUs, high-speed networks, and large-capacity storage require investment in funds and personnel. Colleges and universities must consider the balance between costs and benefits.
Realize resource sharing and reasonable allocation
With limited resources, universities need to consider how to efficiently share and allocate computing resources, taking into account both the urgency of the project and the expected scientific research value as well as fairness and the healthy development of the long-term scientific research ecosystem.
Responding to unexpected needs
Some scientific research projects may suddenly require large-scale computing support, such as predictive models for emergency response to natural disasters or viral genome analysis of sudden epidemics. Universities need to have flexible resource scheduling mechanisms.