A systems engineer is selecting one Linux server to equip with a discrete GPU and plans to install the nvtop utility to watch real-time utilization. The goal is to move a resource-intensive job off the CPU so that it finishes dramatically faster while freeing CPU cores for other services. Which planned workload is most likely to see the greatest performance improvement after being ported to a GPU-accelerated implementation?
Compressing several terabytes of rotated log files each night with gzip
Training a convolutional neural network on a 200 GB image data set with TensorFlow
Hosting a static intranet web site with an Nginx reverse-proxy front end
Operating a recursive DNS resolver for internal clients using unbound
GPUs excel at workloads that can be expressed as thousands of identical, parallel numerical operations-especially the large matrix multiplications used throughout modern deep-learning training. Off-the-shelf frameworks such as TensorFlow and PyTorch automatically detect CUDA, ROCm or other GPU back-ends and can yield order-of-magnitude speed-ups over purely CPU execution. In contrast, gzip compression, static web serving and DNS resolution are I/O-bound or serial tasks that rarely benefit from massive data-parallel hardware. Therefore, training a convolutional neural network is by far the best candidate for GPU acceleration, and monitoring it with nvtop will provide meaningful GPU-side metrics.
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