How do I benchmark deep learning code (C/C++/CUDA) to compare performance of the generated code running on CPUs and GPUs?

2025 11 19
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I'm looking at different deep learning networks (e.g. squeezenet, mobilenet, etc) and I want to be able to profile on different types of hardware (CPU and GPU).
I know I can generate code for these using MATLAB Coder and GPU Coder. I'd like an automated way of benchmarking the code running on different hardware so I can quickly compare performance.

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Bill Chou
Bill Chou 2025-11-19

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You can use the dlCodegenBench function to benchmark the runtime performance of deep learning models running as C/C++/CUDA code generated from MATLAB Coder and GPU Coder.
dlCodegenBench automates the execution running with different code generation configurations so you can compare performance on different hardware (CPU/GPU), different deep learning optimization libraries (target-independent code, MKL-DNN, cuDNN, TensorRT, etc).
More details here:

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