Talked through memory access patterns, occupancy, and reducing warp divergence.
Start by clarifying the kernel's purpose, performance goals, and hardware target. Then describe a systematic optimization process: profile to identify bottlenecks, apply targeted optimizations (e.g., memory, compute, parallelism), and validate improvements with benchmarks. Emphasize iterative refinement and trade-offs.
Pro tip: Always measure before optimizing—use profiling tools to avoid guessing. Also, consider the roofline model to quickly assess whether you're memory- or compute-bound, which guides where to focus efforts.
Clarify what the kernel does, its current performance, and the target metrics (latency, throughput, power). Identify constraints like hardware (CPU/GPU/TPU), memory hierarchy, and precision requirements.
Use profiling tools (e.g., NVIDIA Nsight, perf, VTune) to measure execution time, memory bandwidth, compute utilization, and stalls. Determine if the kernel is memory-bound, compute-bound, or latency-bound.
Based on the bottleneck, apply optimizations: improve memory access patterns (coalescing, tiling), increase compute efficiency (vectorization, FMA), exploit parallelism (threads, SIMD), and reduce overhead (loop unrolling, fusion).
Benchmark the optimized kernel against the baseline, ensuring correctness. Analyze new bottlenecks and repeat the process until performance goals are met or diminishing returns occur.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
AoS vs SoA came up immediately, which felt like the obvious entry point.
Start by contrasting CPU and GPU memory hierarchies, emphasizing the massive parallelism and bandwidth constraints of GPUs. Then discuss how data structure design must account for coalesced memory access, alignment, and minimizing divergence. Finally, tie it back to ML workloads at Waymo, such as point cloud processing or neural network inference, where these considerations directly impact performance.
Pro tip: Demonstrate awareness of real-world trade-offs by mentioning that optimal GPU data structures often require restructuring data from AoS to SoA, but this can increase memory footprint and complexity. Also, note that profiling tools like Nsight can validate assumptions.
Explain the differences between global, shared, and local memory, and how latency and bandwidth vary. Highlight the importance of coalesced access to global memory.
Discuss how data layout (AoS vs SoA) affects memory coalescing and cache utilization. Emphasize that SoA is often preferred for GPU workloads.
Explain how misaligned data can cause uncoalesced accesses and wasted bandwidth. Mention the benefits of aligning data to 128-byte boundaries.
Describe how shared memory bank conflicts and warp divergence can serialize execution. Suggest techniques like padding shared memory arrays and avoiding conditional branching.
Connect these considerations to specific ML tasks, such as sparse matrix operations, point cloud processing, or tensor operations, and discuss trade-offs in performance and memory usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by defining kernel fusion as combining multiple operations into a single GPU kernel to reduce memory traffic and launch overhead. Then discuss when it's beneficial (e.g., element-wise chains, memory-bound ops) and when it's not (e.g., complex control flow, divergent branches). Finally, tie it to real-world ML workloads like those at Waymo, emphasizing trade-offs in performance, compilation complexity, and hardware utilization.
Pro tip: Mention that fusion decisions should be guided by profiling: measure memory bandwidth and kernel launch overhead to identify fusion opportunities, and beware of over-fusion leading to register pressure or reduced occupancy.
Explain that kernel fusion is a compiler optimization that merges multiple operations into a single kernel, reducing global memory reads/writes and kernel launch overhead.
Highlight performance gains: reduced memory bandwidth usage, lower latency from fewer kernel launches, and better instruction-level parallelism.
Discuss scenarios like chains of element-wise operations (e.g., activation functions), memory-bound workloads, and small operations where launch overhead dominates.
Mention cases with complex control flow, divergent branches, large intermediate tensors that don't fit in registers/shared memory, or when fusion increases register pressure and reduces occupancy.
Connect to ML engineering: fusion is common in frameworks like TensorFlow XLA, PyTorch JIT, and TVM; in autonomous driving, fusion can optimize perception and prediction models for latency-critical inference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.