Your operations team is tasked with monitoring a large-scale AI infrastructure where multiple GPUs are running heavy training workloads in parallel. Since these GPUs are highly interdependent, a slowdown on one card can cause the entire training run to stall. Which two metrics are most essential to monitor on the GPUs to ensure optimal performance and catch bottlenecks early? (Select two)
Select all correct answers, then click Submit.
Short Explanation and Infographic
Okay, listen up: when you're running a massive GPU cluster, you need to know exactly what's happening inside those cards. If a training job slows to a crawl, looking at host CPU temperature (Option A), fan noise (Option D), or CPU threads (Option E) isn't going to tell you why. You need to look at two things: GPU core utilization (Option B) and GPU memory bandwidth (Option C). GPU utilization tells you if the processing cores are actually working or just sitting idle. But here's the trap: your cores might show low utilization simply because they're starving for data. That's where memory bandwidth utilization comes in. If your memory bandwidth is maxed out, your GPU is bottlenecked trying to move data between its high-bandwidth memory (HBM) and the cores. Monitor both, and you'll always know if your GPUs are running at full throttle. Trust me on this!
Full explanation below image
Full Explanation
Efficient operations in a GPU-accelerated cluster require deep observability into hardware resource utilization. During parallel training workloads, a slowdown on a single GPU can impact the entire cluster due to synchronization barriers. Two primary metrics are essential for diagnosing these bottlenecks:
1. GPU Core Utilization (Option B): This represents the percentage of time during which one or more kernels were executing on the GPU cores. It indicates whether the GPU is active. However, core utilization alone can be misleading; low core utilization does not always mean the workload is light—it can indicate that the GPU is waiting for data. 2. GPU Memory Bandwidth Utilization (Option C): This measures the throughput of data transfers between the GPU's High Bandwidth Memory (HBM) and the processing cores. Deep learning workloads (such as large matrix operations) require high volumes of data to be constantly read and written. When memory bandwidth utilization approaches 100%, the memory bus is saturated, and the execution cores must wait for data, creating a memory-bound bottleneck.
Analyzing the incorrect options: - Option A: Host CPU temperature is a host metric. While CPU health is important, it does not reveal GPU-specific computing bottlenecks or memory starvation. - Option D: Acoustic noise levels of cooling fans can indicate physical cooling activity but do not provide any direct computational performance telemetry or reveal data bottlenecks. - Option E: Number of active CPU threads monitors host-side software allocation. While it affects data preprocessing pipelines, it does not measure GPU core workload or memory bus saturation.
Monitoring both GPU utilization and memory bandwidth allows operations teams to identify if a system is compute-bound, memory-bound, or network-starved, enabling precise infrastructure optimization.