Free NVIDIA NCA-AIIO Actual Exam Questions - Question 15 Discussion
experiencing performance degradation. Which GPU monitoring metric is most critical for identifying
resource contention between jobs?
Good points on A showing compute contention, but what if the compute units aren’t fully used yet performance still drops? Could D be more telling since memory bandwidth throttling can cause hidden slowdowns even with moderate GPU utilization?
Maybe A is better here since high GPU utilization shows if the compute cores are actually competing for resources. Memory bandwidth matters but might not show direct contention like utilization does.
It’s A for me. If multiple jobs are running, seeing the GPU utilization across all jobs gives a clear picture of how busy the compute units are. Even if memory bandwidth is an issue, if the GPU cores aren’t fully utilized, then something else might be causing bottlenecks. So monitoring utilization directly shows if the compute resources are being maxed out or not, which is critical for spotting contention between workloads on the same GPU.
I’d say A is key here. If GPU utilization is high across multiple jobs, that directly points to compute resource contention. Memory bandwidth might be a factor, but if the GPU compute units are maxed out, that’s the more immediate cause of slowdowns. Temperature and network latency don’t really reflect internal GPU resource sharing issues as clearly as utilization does. So keeping an eye on how busy the GPU cores are across jobs gives a solid first indication of contention.
D imo, because even if GPU utilization looks fine, memory bandwidth is often the hidden bottleneck when multiple jobs are reading/writing data simultaneously. Monitoring memory bandwidth utilization helps catch contention that doesn’t show up in compute usage metrics. A lot of times, compute units are idle waiting on memory, so just relying on GPU utilization can miss the real issue.
A/D? GPU utilization shows compute usage but memory bandwidth bottlenecks can cause slowdowns even if compute looks fine. Checking both gives a fuller picture of resource contention.
It’s A, high GPU utilization means jobs compete for compute time directly.
D imo, memory bandwidth utilization can be a big bottleneck when multiple jobs are running. Even if GPU utilization looks okay, if the memory bandwidth is maxed out, jobs will slow down because data isn’t moving fast enough. So checking that metric can reveal hidden contention that GPU utilization alone might miss.
Definitely A. GPU Utilization Across Jobs helps spot when multiple jobs are fighting for GPU time, which causes slowdowns.