A research lab has two options for implementing the low-level execution of sub-matrix multiplications in their new distributed computing framework. Option A uses a single, general-purpose parallel algorithm that is compatible with any GPU. Option B involves developing and maintaining separate, highly-tuned algorithms specifically optimized for the unique hardware architecture of each GPU model they use (e.g., one for 'GPU-V' and another for 'GPU-A'). Option B will require significantly more initial development and ongoing maintenance effort. Based on the goal of maximizing computational efficiency, evaluate the two options and justify which one is the superior choice.