NCA-AIIO Question 49
Single answerA data scientist is training a deep learning model that requires processing large amounts of image data with thousands of parallel computations. They have access to both GPU and CPU resources. Why would a GPU be a better choice for this workload?
- A
GPUs are optimized for parallel processing, making them ideal for workloads with many simultaneous calculations.
- B
GPUs have higher single-threaded performance compared to CPUs, which is critical for deep learning tasks.
- C
GPUs consume significantly less power than CPUs, making them more energy-efficient for training large models.
- D
GPUs have specialized hardware such as CUDA cores that accelerate matrix operations commonly used in deep learning.
Show answer and explanation
Correct answer: A
Explanation
GPUs are optimized for parallel processing, which is critical for deep learning tasks that involve numerous simultaneous computations. This advantage comes from their architecture, which features thousands of cores designed to handle multiple operations concurrently. In contrast, CPUs are optimized for sequential tasks with high single-threaded performance, making them less suitable for such workloads.
- A. Correct.
Correct. GPUs are designed for parallel processing, which makes them highly efficient for workloads like deep learning that require simultaneous calculations on large datasets.
- B. Incorrect.
Incorrect. CPUs generally have higher single-threaded performance than GPUs, but deep learning tasks typically benefit more from parallelism than single-threaded performance.
- C. Incorrect.
Incorrect. While GPUs can be energy-efficient for specific workloads, energy consumption is not their primary advantage in deep learning tasks.
- D. Incorrect.
Incorrect. Although GPUs do have specialized hardware like CUDA cores, this is a subset of their overall parallel processing capability. The question asks for the primary reason.