Multi-GPU and distributed setups: Practice Questions — GPU Acceleration and Optimization (NVIDIA-Certified Professional: Generative AI LLMs)

Practice Questions: Multi-GPU and Distributed Setups for NVIDIA-Certified Professional: Generative AI LLMs

These multiple-choice questions focus on key concepts related to multi-GPU and distributed setups, essential for the NVIDIA-Certified Professional: Generative AI LLMs exam. Each question includes four options, the correct answer, and a brief explanation.

1. Which parallelism technique involves splitting the model's layers across multiple GPUs to reduce memory usage per device?

   • A. Data Parallelism
   • B. Model Parallelism
   • C. Pipeline Parallelism
   • D. Batch Parallelism

   Answer: B. Model Parallelism

   Explanation: Model parallelism distributes different parts of the model across GPUs, allowing large models to fit into memory by splitting layers rather than duplicating the entire model on each GPU.

2. In a distributed training setup using data parallelism, what is the primary role of the all-reduce operation?

   • A. To split the model layers across GPUs
   • B. To synchronize gradients across GPUs
   • C. To increase batch size per GPU
   • D. To profile GPU performance

   Answer: B. To synchronize gradients across GPUs

   Explanation: All-reduce aggregates gradients computed on each GPU to ensure model weights are updated consistently during data parallel training.

3. Which of the following is a common challenge when scaling training across multiple GPUs in a distributed setup?

   • A. Increased model accuracy
   • B. Communication overhead between GPUs
   • C. Reduced batch size per GPU
   • D. Decreased memory usage

   Answer: B. Communication overhead between GPUs

   Explanation: As the number of GPUs increases, communication latency and bandwidth constraints can limit scaling efficiency due to synchronization and data exchange.

4. What is the purpose of using mixed precision training in multi-GPU setups?

   • A. To increase the number of GPUs used
   • B. To reduce memory usage and speed up computation
   • C. To avoid gradient synchronization
   • D. To split the model layers

   Answer: B. To reduce memory usage and speed up computation

   Explanation: Mixed precision uses lower-precision floating-point formats (e.g., FP16) to reduce memory footprint and improve throughput, especially beneficial in multi-GPU training.

5. In pipeline parallelism, how are micro-batches used to improve GPU utilization?

   • A. By replicating the entire model on each GPU
   • B. By splitting a batch into smaller chunks processed sequentially across GPUs
   • C. By synchronizing gradients after each micro-batch
   • D. By increasing batch size per GPU

   Answer: B. By splitting a batch into smaller chunks processed sequentially across GPUs

   Explanation: Pipeline parallelism divides batches into micro-batches that flow through different model stages on GPUs, enabling concurrent computation and better hardware utilization.

6. Which tool or technique is commonly used to profile and troubleshoot performance bottlenecks in multi-GPU training?

   • A. TensorBoard
   • B. NVIDIA Nsight Systems
   • C. GitHub
   • D. Jupyter Notebook

   Answer: B. NVIDIA Nsight Systems

   Explanation: NVIDIA Nsight Systems provides detailed GPU and CPU profiling to identify bottlenecks in distributed training workflows.

7. When optimizing batch size in a multi-GPU distributed training environment, what is a key consideration?

   • A. Batch size should always be as small as possible
   • B. Larger batch sizes improve throughput but may reduce model convergence speed
   • C. Batch size does not affect training speed
   • D. Batch size must be equal to the number of GPUs

   Answer: B. Larger batch sizes improve throughput but may reduce model convergence speed

   Explanation: Increasing batch size can improve GPU utilization and training speed but may require tuning learning rates to maintain model accuracy and convergence.

8. Which communication backend is optimized for high-performance GPU-to-GPU communication in distributed training?

   • A. MPI
   • B. NCCL
   • C. HTTP
   • D. FTP

   Answer: B. NCCL

   Explanation: NVIDIA Collective Communications Library (NCCL) is specifically designed for efficient multi-GPU communication, minimizing latency and maximizing bandwidth.

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