NCP-GENL Practice Questions: GPU Acceleration and Optimization Domain

Correct Answer: D

Explanation: Implementing asynchronous data transfer using CUDA streams allows data to be transferred concurrently with computation, reducing idle time and alleviating bottlenecks. Increasing PCIe bandwidth (A) is hardware-dependent and not always feasible. Using RAPIDS Memory Manager (B) helps with memory allocation but not data transfer speed. Data compression (C) can reduce size but introduces additional processing overhead. Best practice: Use CUDA streams for efficient data transfer and overlap with computation.

Correct Answer: D

Explanation: Utilizing NVIDIA's Apex for automatic mixed precision and distributed data parallelism can enhance GPU workload balance by optimizing memory usage and synchronizing gradient updates efficiently across GPUs. Switching to model parallelism (A) might not solve uneven utilization unless the model architecture is specifically suited for it. Mixed precision training (B) alone does not address distribution issues. Gradient checkpointing (C) helps with memory but not load balancing. Best practice: Combine distributed data parallelism with mixed precision for optimal GPU utilization.

Correct Answer: C

Explanation: Enabling dynamic batching in Triton allows the server to combine requests, optimizing memory usage and avoiding peaks. Switching to FP16 (A) reduces memory but may not fully address peaking issues. Decreasing batch size (B) can reduce memory per inference but may underutilize the GPU. Using RAPIDS (D) is unrelated to GPU memory management during inference. Best practice: Use dynamic batching to efficiently manage memory and improve throughput.

Correct Answer: A

Explanation: Increasing the batch size for inference requests can improve GPU utilization by allowing more data to be processed simultaneously, which is particularly effective in maximizing throughput on NVIDIA GPUs. Option B is incorrect because using a CPU-based engine would likely decrease performance. Option C is not ideal because mixed precision optimizes performance and memory usage. Option D could further reduce utilization by limiting parallel processing. Best practice: Use batch processing to enhance GPU throughput.

Correct Answer: D

Explanation: Increasing the number of concurrent inference requests can improve model throughput by better utilizing the GPU resources. Option A is incorrect because model pruning is more about reducing model size and memory usage rather than directly increasing throughput. Option B is incorrect as RAPIDS is used for data preprocessing, not directly for inference optimization. Option C, while beneficial during training, does not directly affect inference throughput. Best practice is to configure Triton to handle multiple simultaneous requests to maximize GPU utilization.

Correct Answer: B

Explanation: Enabling mixed precision with TensorRT can significantly reduce computation time by leveraging Tensor Cores on NVIDIA GPUs, thus improving latency without sacrificing accuracy. Increasing batch size (A) may improve throughput but not necessarily latency. Quantization (C) can reduce model size but may impact accuracy if not carefully managed. Pruning (D) is effective for reducing model size but is less impactful on latency compared to mixed precision. Best practice: Use mixed precision to balance speed and accuracy effectively.

Correct Answer: B

Explanation: Using cuDF's multi-threaded I/O can significantly improve data loading speeds by leveraging parallelism and GPU acceleration. Option A would likely slow down processing as RAPIDS is optimized for GPU. Option C could reduce memory usage but not necessarily speed. Option D would negate the benefits of using RAPIDS. Best practice: Utilize multi-threaded I/O in RAPIDS for efficient data processing.

Correct Answer: D

Explanation: Enabling Multi-Instance GPU (MIG) allows for better resource allocation, reducing latency spikes by isolating workloads. Quantization (A) reduces computational load but might not address spikes. Reducing batch size (B) could lower throughput. Network bottlenecks (C) are less likely to cause latency spikes specific to GPU resource allocation.

Correct Answer: B

Explanation: Data loading bottlenecks can cause GPUs to wait for data, leading to underutilization. Ensuring efficient data loading can improve GPU utilization. Increasing the learning rate (A) does not address the root cause of the underutilization. Reducing batch size (C) can worsen GPU utilization. Switching to a single-GPU setup (D) does not solve the problem and reduces computational power.

Correct Answer: A

Explanation: Increasing the batch size can improve GPU utilization by allowing more data to be processed simultaneously, thus maximizing the throughput of the GPU. Option B is incorrect because reducing the number of concurrent model instances would likely decrease GPU utilization further. Option C is incorrect as CPU-only inference would significantly reduce performance compared to GPU inference. Option D, while potentially beneficial for reducing memory usage, does not directly address low GPU utilization. Best practice is to balance batch size with latency requirements to optimize throughput.