NCP-AII Question 25
Select 4You are tasked with optimizing both AMD and Intel servers in your AI infrastructure to enhance the performance of NVIDIA GPUs used for deep learning workloads. Which of the following actions should you implement to achieve optimal performance across these systems?
- A
Enable NUMA (Non-Uniform Memory Access) awareness in the system BIOS and application settings.
- B
Use the latest NVIDIA GPU drivers and CUDA toolkit version compatible with your GPUs and server architecture.
- C
Disable hyper-threading to reduce CPU contention and improve GPU workload execution.
- D
Configure server power profiles to favor energy savings over maximum performance.
- E
Ensure PCIe lanes are configured for maximum bandwidth by verifying BIOS settings and hardware compatibility.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Optimizing AMD and Intel servers for AI infrastructure performance requires addressing both hardware and software configurations. NUMA awareness, up-to-date GPU drivers, minimizing CPU contention, and maximizing PCIe bandwidth are critical steps to ensure efficient GPU utilization and overall system performance. On the other hand, energy-saving power profiles should be avoided in high-performance AI environments as they can throttle computational power.
- A. Correct.
Enabling NUMA awareness ensures that memory is allocated closer to the CPU or GPU that requires it, reducing latency and improving performance in multi-GPU setups.
- B. Correct.
Using the latest NVIDIA GPU drivers and CUDA toolkit ensures compatibility, feature enhancements, and performance optimizations for your specific hardware.
- C. Correct.
Disabling hyper-threading can reduce CPU contention, allowing AI workloads to utilize the CPU effectively, especially when paired with GPUs.
- D. Incorrect.
Configuring power profiles for energy savings may reduce performance as it prioritizes lower power usage over computational speed, which is not ideal for AI workloads.
- E. Correct.
Ensuring PCIe lanes are configured for maximum bandwidth is critical for optimizing data transfer between GPUs and CPUs, a key factor in AI workload performance.