  Within high-performance computing (HPC), solving large sparse linear systems
efficiently remains paramount, with iterative methods being the predominant
choice. However, the performance of these methods is tightly coupled to the
aptness of the chosen preconditioner. The multifaceted nature of sparse
matrices makes the universal prescription of preconditioners elusive. Notably,
the key attribute of sparsity is not precisely captured by scalar metrics such
as bandwidth or matrix dimensions. Advancing prior methodologies, this research
introduces matrix sparsity depiction via RGB images. Utilizing a convolutional
neural network (CNN), the task of preconditioner selection turns into a
multi-class classification problem. Extensive tests on 126 SuiteSparse matrices
emphasize the enhanced prowess of the CNN model, noting a 32% boost in accuracy
and a 25% reduction in computational slowdown.
