Detail a scenario where texture memory would be more advantageous than global memory in a CUDA kernel, justifying your choice with specific performance considerations.

Texture memory in CUDA offers certain advantages over global memory, making it more suitable in specific scenarios. One such scenario is when performing image filtering or interpolation operations, particularly when dealing with non-unit stride access patterns and spatial locality. Texture memory's caching mechanisms and hardware-accelerated interpolation capabilities can lead to significant performance gains compared to accessing the same data from global memory. Consider an image processing application where you need to apply a bilinear interpolation to scale an image. Bilinear interpolation requires accessing four neighboring pixels to compute the interpolated value at a non-integer coordinate. If you were to access these four pixels directly from global memory, you would likely encounter non-coalesced memory accesses, especially if the image dimensions are not perfectly aligned with the warp size. Furthermore, each pixel access would involve a separate load from global memory, which is relatively slow. Now, let's examine how texture memory can improve performance in this scenario. Texture memory is backed by a hardware-managed cache, which is optimized for 2D spatial locality. When you access a pixel from texture memory, the cache automatically fetches neighboring pixels, effectively prefetching data that is likely to be needed for subsequent interpolation operations. This reduces the number of explicit memory accesses required and increases the likelihood of cache hits. Additionally, texture memory provides hardware-accelerated interpolation capabilities. The texture unit can perform bilinear, bicubic, or nearest-neighbor interpolation directly in hardware, without requiring you to implement the interpolation logic in your k....