A cloud resource elastic scaling system for high performance computing, which belongs to the technical field of high performance computing, wherein the system comprises a resource expansion sub-system responsible for adding nodes to a cluster and a resource contraction sub-system responsible for deleting node from the computing cluster. A scheduling system accepts tasks submitted by an external user or system, and distributes the tasks to a waiting queue, the resource elastic scaling system scans the task waiting queue, combines aspects of expansion decision algorithms, and applies for bidding resources in a suitable area, and the tasks are finally run on the newly added computing nodes; when the tasks are distributed, the computing nodes in the cluster are slowing idle, a contraction strategy of the resource elastic scaling system is triggered to recover and release the nodes. The system integrates elastic scaling APIs of large public cloud providers to manage and control global resources; and an optimal resource use mode is predicted through statistical learning of a large number of existing and continuously added different types of task running time.
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