Large-Scale AI Training
Scenarios such as autonomous driving, NLP, and recommendation systems are characterized by large data volumes and intensive computing. Hyper Computing Cluster instances can support high-speed, low-latency RDMA network interconnection and the next-generation CPU architecture and heterogeneous GPU components for the computing of compute-intensive workloads. In this way, it can meet business needs of high computing performance, stability, and timeliness.
Industrial Simulation
Many manufacturing enterprises in automobile, aviation, and other industries need to use simulated computing to drive design. The high-performance computing clusters built by enterprises themselves require large investments and long cycles, and it is difficult to continuously meet the demand. Hyper Computing Cluster instances can be quickly deployed and elastically scaled. Through the high-speed, low-latency RDMA network and the latest CPU architecture, Hyper Computing Cluster instances implement parallel processing to quickly meet the simulation demands of enterprises in aerospace, industrial manufacturing, and other industries and promote product R&D promptly.
Life Sciences
Hyper Computing Cluster instances can use the high-speed, low-latency RDMA network to conduct large-scale molecular dynamics simulations, predict and analyze the interactions between and changes of biological protein molecules and lipid molecules, and assist in drug research.
Scientific Research and Education
Hyper Computing Cluster instances can provide supercomputing platforms for universities and research institutions for numerical simulation, numerical computation, simulation verification, and other applications.
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