Large-Scale Financial Data Parallel Processing and Model Training Acceleration based on Distributed Deep Learning Framework

In order to solve the problems of low computing efficiency, high communication overhead, and slow model convergence in financial data processing, this paper proposes a hybrid parallel acceleration solution that integrates dynamic batch processing and gradient compression. By building a multilevel communication optimization mechanism based on the parameter server architecture, the elastic AllReduce algorithm is used to improve the efficiency of gradient synchronization, and the adaptive load balancing strategy is combined to dynamically allocate GPU cluster tasks. At the same time, mixed precision training and pipeline parallel technology are applied to reduce video memory occupancy. When processing 10TB of high-frequency trading data on a 100-node GPU cluster, the system achieves an 18.7-fold acceleration; the training throughput is increased to 23,500 samples/second; the average convergence time in the training phase is 3.2 hours; the throughput gain of dynamic load balancing in heterogeneous clusters can reach 2.3 times that of a homogeneous environment. This verifies the superiority of the framework in processing high-dimensional time series data and provides a scalable distributed training solution for financial AI systems.