Solution:
In an 8x8 block (64 threads), threads are linearized using $Index = tx + (ty \times 8)$. Warp 0 covers Linear IDs 0 to 31. T(0,0) is ID 0. ID 31 is calculated as $tx=7, ty=3$ ($7 + 3 \times 8 = 31$). Therefore, Warp 0 starts at $T(0,0)$ and ends with $T(7,3)$. Warp 1 begins at $T(0,4)$ (ID 32) and ends at $T(7,7)$ (ID 63).

Solution:
No, shared memory cannot reduce global memory bandwidth for matrix addition. In matrix addition, each output element $C[i][j]$ is the sum of $A[i][j] + B[i][j]$. Each element from the input matrices $A$ and $B$ is accessed exactly once by exactly one thread to compute its unique output. Shared memory is a high-speed scratchpad designed for data reuse—where multiple threads within a block read the same global memory address repeatedly (as seen in matrix multiplication). Since there is zero data commonality between threads in matrix addition, loading data into shared memory first would actually increase overhead. It would require one global load to shared memory, a `__syncthreads()` barrier, and then a shared memory load before the addition. This adds instruction count and synchronization latency without decreasing the number of global memory transactions. To optimize matrix addition, one should focus on global memory coalescing rather than shared memory utilization.