Developed and delivered SparseArrays functionality for the JuliaGPU/CUDA.jl repository, enabling efficient column-wise operations on sparse matrices stored directly on the GPU. This work involved extending the CuSparseDeviceMatrixCSC type to support new SparseArrays interfaces, broadening the interoperability of sparse data pipelines within CUDA-based workflows. Comprehensive tests were added to validate the new features and ensure regression protection, reflecting a methodical approach to quality assurance. Utilizing Julia and CUDA, the developer focused on GPU computing and sparse matrix operations, addressing performance needs for column-centric workloads and enhancing the robustness of the GPU sparse API for future development and maintenance.