The field of linear algebra and scientific computing is moving towards leveraging GPU acceleration to improve performance and efficiency. Recent developments have focused on creating libraries and algorithms that can effectively utilize GPU capabilities, leading to significant speedups over traditional CPU-based approaches. Notable advancements include the development of templated C++ libraries for GPU linear algebra, efficient GPU-centered singular value decomposition algorithms, and high-performance GPU implementations of dimensionality reduction techniques. These innovations have the potential to accelerate a wide range of applications, from molecular dynamics simulations to machine learning and data analysis. Noteworthy papers include: Efficient GPU-Centered Singular Value Decomposition Using the Divide-and-Conquer Method, which achieves speedups of up to 1293.64x compared to existing methods. A High Performance GPU CountSketch Implementation and Its Application to Multisketching and Least Squares Problems, which demonstrates a multisketched least squares solver that is up to 77% faster than traditional methods.