Q: 2
A healthcare company is training a large convolutional neural network (CNN) for medical image
analysis. The dataset is enormous, and training is taking longer than expected. The team needs to
speed up the training process by distributing the workload across multiple GPUs and nodes. Which of
the following NVIDIA solutions will help them achieve optimal performance?
Options
Discussion
Makes sense to pick B for this one.
B. official guide and practice exams mention NCCL+DALI a lot for multi-GPU workloads. Anyone using labs will see these two together often.
Pretty clear it's B
Yeah B makes sense here. NCCL is for multi-GPU comms and DALI speeds up data loading, both crucial for distributed training. The others don’t really help when scaling to multiple nodes. Pretty sure it’s B but happy to hear if someone has another angle.
Option A feels right to me since cuDNN is the go-to for optimizing CNNs. I know it’s mostly about single GPU performance, but I thought it’d speed up the training itself regardless of scaling. Not 100% sure here though, could be missing something about multi-GPU communication.
Makes sense to go with B here. NCCL is for scaling across GPUs/nodes and DALI handles fast data input, which is exactly what you need for distributed training jobs. The other options are more focused on single GPU, inference or video analytics. Pretty sure about B but open to other thoughts if someone disagrees.
Don't think A is right, that's for single GPU, B is the pick here.
B shows up as the right answer in both official NVIDIA study guides and most practice tests for distributed CNN training.
Pretty sure B here. NCCL handles communication across multiple GPUs and nodes, and DALI speeds up the data pipeline so you aren't waiting on I/O. I've seen similar training questions recommend both in practice tests. Official NVIDIA docs or hands-on labs could help if anyone wants to dig deeper. Agree?
Practice tests usually favor A here, and the official guide puts cuDNN as key for CNN optimization. A
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