Exam NCA-GENL Topic 9 Question 71 Discussion

Actual exam question for NVIDIA's NCA-GENL exam
Question #: 71
Topic #: 9

When implementing data parallel training, which of the following considerations needs to be taken into account?

A. The model weights are synced across all processes/devices only at the end of every epoch. B. A master-worker method for syncing the weights across different processes is desirable due to its scalability. C. A ring all-reduce is an efficient algorithm for syncing the weights across different processes/devices. D. The model weights are kept independent for as long as possible increasing the model exploration.

Suggested Answer: C Vote an answer

In data parallel training, where a model is replicated across multiple devices with each processing a portion of the data, synchronizing model weights is critical. As covered in NVIDIA's Generative AI and LLMs course, the ring all-reduce algorithm is an efficient method for syncing weights across processes or devices. It minimizes communication overhead by organizing devices in a ring topology, allowing gradients to be aggregated and shared efficiently. Option A is incorrect, as weights are typically synced after each batch, not just at epoch ends, to ensure consistency. Option B is wrong, as master-worker methods can create bottlenecks and are less scalable than all-reduce. Option D is inaccurate, as keeping weights independent defeats the purpose of data parallelism, which requires synchronized updates. The course notes: "In data parallel training, the ring all-reduce algorithm efficiently synchronizes model weights across devices, reducing communication overhead and ensuring consistent updates." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

by Yvette at Dec 10, 2025, 12:57 AM

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