baidu-research/DeepBench

Health v2 · maintenance only

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Adoption not part of the score

1106 stars · 242 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DeepBench is a benchmarking suite from Baidu Research that measures the performance of fundamental deep learning operations (matrix multiplication, convolutions, etc.) across different hardware platforms. It targets low-level operations suitable for hardware evaluation rather than full model training.

Use cases

• compare gpu performance for deep learning operations
• benchmark matrix multiplication on different hardware
• evaluate hardware for neural network training
• measure convolution kernel performance
• test hardware suitability for deep learning inference
• profile low-level deep learning ops for chip design

When to choose

• you need to compare raw operation-level performance across GPUs or CPUs
• you are evaluating or designing hardware for deep learning workloads
• you want to identify bottlenecks in neural network libraries like cuDNN or MKL

When to avoid

• you want to benchmark end-to-end training time of full models
• you need application-level or framework-level benchmarks
• you need a tool that is actively updated with the latest hardware support

Facets

library · maturity maintenance

benchmarking gpu-computing deep-learning deep-learning gpu-computing performance hardware cpp cross-platform neural-network-kernels hardware-evaluation matrix-multiplication convolution training-benchmark inference-benchmark linux gpu

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