Answer-first summary for fast verification
Answer: Introduce parallel interleaving to the pipeline to read and process multiple files simultaneously.
Introducing parallel interleaving to the pipeline is the most effective strategy for optimizing the input pipeline's execution time. This approach allows for the simultaneous reading and processing of multiple files, which significantly reduces the time spent on data loading and preprocessing. By doing so, the GPU can be kept busy with a steady stream of data, thereby improving its utilization without the need for additional resources or costs. Upgrading network bandwidth or increasing CPU utilization may not directly address the bottleneck caused by sequential file reading, and while caching can reduce reprocessing time, it does not inherently speed up the initial data loading process.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
You are working on training a deep learning model using TensorFlow on a Google Cloud Platform (GCP) instance equipped with GPUs. The training data is distributed across multiple large files, and you've noticed that the GPU utilization is lower than expected due to the input pipeline's execution time. Your primary goal is to optimize the input pipeline to fully leverage the GPU's capabilities without increasing the overall cost. Which of the following strategies would be the most effective to achieve this goal? (Choose one)
A
Upgrade the network bandwidth to speed up data transfer rates.
B
Implement data caching within the pipeline to avoid reprocessing the same data.
C
Introduce parallel interleaving to the pipeline to read and process multiple files simultaneously.
D
Increase the CPU utilization to process more data in parallel.
No comments yet.