This question looked straightforward and then just kept expanding.
Start by systematically comparing the two kernels across the five specified dimensions, then discuss how performance varies with matrix shape and justify your preferred kernel for a given workload. Finally, propose further optimizations such as vectorized loads, tiling, or using shared memory for reuse.
Pro tip: Emphasize that for memory-bound element-wise operations, achieving coalesced global memory access and maximizing occupancy are critical; avoid overcomplicating with shared memory unless it provides a clear benefit. Also, mention that real-world performance should be validated with profiling tools like Nsight Compute.
Discuss how each kernel organizes threads into blocks and grids, and how that affects occupancy, parallelism, and resource utilization. Consider block size, grid size, and whether they adapt to matrix dimensions.
Evaluate whether global memory accesses are coalesced and how each thread maps to matrix elements. Identify if there are strided or uncoalesced accesses that could hurt performance.
Determine if shared memory is used (and whether it's necessary for element-wise addition) and how each kernel handles out-of-bounds accesses for non-multiple matrix dimensions.
Explain how performance might differ for small vs. large matrices, square vs. rectangular, and shapes that are not multiples of block size. Consider memory bandwidth limits and overheads.
State which kernel you would prefer for a given workload and why, then propose further optimizations such as vectorized loads, loop unrolling, or using texture memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.