The field of scientific computing is experiencing a significant shift towards leveraging GPU acceleration to improve performance and efficiency. Recent developments have focused on optimizing algorithms and frameworks to take advantage of GPU capabilities, resulting in substantial speedups and reduced computational overhead. Notably, advancements in GPU-accelerated rendering, dynamic memory management, and parallel computing have enabled researchers to tackle complex simulations and data analysis tasks with increased ease. Additionally, innovations in programming libraries and frameworks have simplified the process of developing and optimizing GPU-accelerated applications. Overall, these developments are poised to revolutionize the field of scientific computing by enabling faster, more efficient, and more accurate simulations and data analysis. Noteworthy papers include: From Cluster to Desktop, which introduces a cache-accelerated INR framework for interactive visualization of tera-scale data, achieving an average 5x speedup over state-of-the-art methods. GPU-Accelerated Parallel Selected Inversion for Structured Matrices Using sTiles, which presents an efficient implementation of a two-phase parallel algorithm for computing selected elements of the inverse of a sparse symmetric matrix, achieving up to 13X speedup on large-scale structured matrices.