Currently, high-performance computing environments are facing challenges such as limited resources and an increasing number of users. In order to improve the utilization of environmental resources, this paper proposes a high-performance hybrid computing architecture based on big data processing technology, which is constructed on the basis of an HDFS distributed system combined with MapReduce framework and GPU virtualization technology. The PageRank algorithm is utilized to evaluate the performance of rack nodes in the high-performance computing environment, and the evaluation results are applied to design an improvement strategy for task allocation and scheduling through the MapReduce framework. A division function is introduced to dynamically divide the Reduce data, and an approximate sampling method based on sampling information is proposed to guide the setting of the number of Reduce. The IB algorithm is used to cluster the labeled files, and a rack-aware strategy is designed based on HDFS to achieve resource load balancing. The MapReduce-based task allocation scheduling scheme has a reduction in job execution time of up to 39.83% compared to delayed scheduling. The dynamic partitioning design can achieve data load balancing by partitioning 5.382% of the groups and migrating 1.207% of the KVs if the data skew is 1.0. Dynamic balancing of environmental resources and resource scheduling optimization in high-performance computing environments can be achieved through the use of big data processing techniques.