Sparse tensor contraction (SpTC) is an important operator in tensor networks, which tends to generate a large amount of sparse high-dimensional data, placing higher demands on the computational performance and storage bandwidth of the processor. Using GPUs with powerful arithmetic characteristics is a reliable choice for accelerating SpTC, however, the high dimensionality and sparsity of tensor makes GPU-accelerated SpTC operators suffer from the difficulties of low computational intensity and high memory consumption. The recent introduction of Tensor Core Units (TCUs) on GPUs brings even more powerful arithmetic, which exacerbates the memory wall problem. To cope with the challenges, this paper proposes a new BCB format that linearizes the indices of multidimensional blocks to reduce block index accesses and uses a bitmap to store the distribution of non-zero elements in a block to reduce the storage overhead. A parallel blocking algorithm of BCB-SpTC is designed to divide the binary linear indices into free and contracted indexes to improve the pairing overhead of computational tasks. Then based on the characteristic computation method of TCUs, the proprietary filling method of TCUs is designed to overcome the inefficiency of parallel computation of sparse data on TCUs. Finally, experimental results on the A100 dataset show that BCB-SpTC improves the acceleration ratio by$1.1\times$to$21.3\times$over the existing SpTC GPU method.

Analysis of multi-dimensional data, especially tensor decomposition, which extracts latent information, is becoming considerably popular. Although multi-dimensional sparse data is typically processed on multi-core processors, developing highly optimized GPU-basedSparseTensorMatrixChainMultiplication (SpTMCM) is challenging. The purpose of this paper is to investigate a novel approach named SpTMCM and to explore the discovery of SpTMCM coupled with the emerging computing core, Tensor Core Unit (TCU). In contrast to prior work, the proposed novel approach enables a uniform storage format and optimization approach for SpTMCM. We design a hybrid tensor format based on multi-dimensional tiling that divides the tensor depending on the tile threshold to address the inefficient memory accesses caused by the irregular nonzero distribution of the sparse tensor. Further, we develop a TCU-based tensor parallel algorithm with our novel approach to increase the memory bandwidth. Compared to state-of-the-art works, our method achieves$1.16\sim 24.12\times$speedup for SpMTTKRP and$5.07\sim 7.15\times$speedup for SpTTMChain across NVIDIA A100 GPU on a range of real-world sparse tensors.