A faster heterogeneous parallel computing method for Tucker decomposition

Artificial intelligence (AI) technology is developing explosively in application fields such as speech recognition, semantic understanding, and computer vision. In particular, the new generation of knowledge enhancement large language models have gradually become the infrastructure of social productivity. With the development of large models towards multi-mode, the input data and model parameters characterized by tensors are becoming increasingly large. This puts high demands on computation and storage. Tucker decomposition obtains the optimal low-rank representation of the natural tensor by factor matrices and a core tensor, reducing storage and computation requirements in big data and artificial intelligence applications. However, the existing Tucker decomposition methods show limited computational speed and convergence performance. In this paper, a general heterogeneous computing framework of the Tucker decomposition method is proposed, which analyzes the row independence of factor matrices and the column independence of Kruskal matrices, and updates the Kruskal matrices instead of the core tensor in a column-wise manner and factor matrices in a row-wise manner respectively to reduce the storage overhead in computation. Further, the proposed method employs a heterogeneous computing platform to speed up the computing bottlenecks and takes full advantage of fine-grained parallel optimization technology to improve memory access efficiency. The experimental results show that the computation speed achieves a 3.1 to 75.4 times improvement compared to the latest methods. Among all the methods, it exhibits the best convergence performance.