Sparse matrix-vector multiplication (SpMV) plays a critical role in a wide range of linear algebra computations, particularly in scientific and engineering disciplines. However, the irregular memory access patterns, extensive memory usage, high bandwidth requirements, and underutilization of parallelism hinder the computational efficiency of SpMV on GPUs. In this paper, we propose a novel approach called block-wise dynamic mixed-precision (BDMP) to address these challenges. Our methodology involves partitioning the original matrix into uniformly sized blocks, with each block’s size determined by considering architectural characteristics and accuracy requirements. Additionally, we dynamically assign precision to each block using a precision selection method that takes into account the value distribution of the original sparse matrix. We develop two distinct SpMV computation algorithms for BDMP: BDMP-PBP (Precision-based partitioning) and BDMP-TCKI (Tailored compression and kernel implementation). BDMP-PBP partitions the matrix into two independent matrices for separate computations based on block precision, offering flexibility for integration with other optimization techniques. Meanwhile, BDMP-TCKI focuses on achieving significant thread-level parallelism and memory utilization by tailoring an appropriate compressed storage format and kernel implementation for each block. We compare BDMP with NVIDIA’s cuSPARSE library and three state-of-the-art SpMV methods, including SELLP, MergeBase, and BalanceCSR, using matrices from the University of Florida’s SuiteSparse dataset collection. BDMP-PBP and BDMP-TCKI show average speedups up to 2.64 $$\times $$× and 2.91 $$\times $$× on Turing RTX 2080Ti, and up to 2.99 $$\times $$× and 3.22 $$\times $$× on Ampere A100. The results demonstrate that BDMP enables the optimization of computation speed without compromising the precision necessary for reliable results.

Masked auto-encoders (MAEs) have been extensively employed in the field of semi-supervised hyperspectral image classification (HSIC). However, the developed models encounter significant challenges in learning separable representations, as they do not sufficiently prioritize the reconstruction of the central pixel, which hinders their ability to learn separable representations. To address this limitation, we propose spectral–spatial MAE with central pixel reconstruction (SSMAE-CR), a novel self-supervised framework tailored for HSIC. To capture more comprehensive representations, SSMAE-CR employs a dual-branch architecture comprising a spectral MAE with central pixel reconstruction (SpecMAE-CR) and a spatial MAE with central pixel reconstruction (SpatMAE-CR). SpecMAE-CR highlights the significance of central pixel reconstruction by measuring the deviation between the central pixels of reconstructed and original samples. To preserve the holism of the learned latent representations, SpatMAE-CR maps the central pixels of the reconstructed samples back to their original counterparts through the introduction of an additional linear layer. Rigorous comparative experiments conducted on four publicly available datasets fully demonstrate that SSMAE-CR outperforms state-of-the-art methods. Furthermore, we validate the effectiveness of SSMAE-CR by evaluating the mean intra-class and inter-class distances of the learned representations. Experimental results demonstrate that prioritizing central pixel reconstruction yields a statistically significant increase in the mean inter-class distance, suggesting enhanced class separability in the representation space.