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Benchmark

As explained in Graph Neural Networks in Action

Benchmarking is the systematic measurement of a system or model against representative data to establish performance references.

Benchmarking measures a model or system on representative data to establish performance baselines. Use it before large-scale training to estimate timelines, compare hardware, and expose resource constraints. section 7.3.2

A practical benchmarking workflow:

  • Understand dataset size and characteristics for each machine-learning lifecycle phase. section 7.3.2
  • Select representative data rather than relying only on small demonstration datasets. section 7.3.2 section 8.4
  • Measure memory utilization, time per epoch, FLOP/s, time to convergence, preprocessing time, and training time. section 7.3.2 section 7.4.2
  • Compare configurations, then use the results to choose processors, algorithms, representations, batching strategies, or distributed approaches. section 7.4.2 section 7.11

Choosing preprocessing and training hardware

A 10 GB sparse graph dataset is benchmarked on CPU and GPU. The GPU trains faster, while the CPU preprocesses comparatively well, so preprocessing uses the CPU and training uses the GPU. When larger graphs exceed GPU memory, subgraph sampling keeps the workload within available hardware. section 7.4.2

Benchmark results are only as useful as the data and workload they represent. Small graph datasets can limit reproducibility and may not predict large-scale performance; OGB supplies real-world datasets at varied scales and publishes task benchmarks. section 8.4

Table 7.2 Pros and cons of processor choice
Hardware
Recommended Workload
Pros
Cons
CPU
Preprocessing
Suitable for data collection and preprocessing
More affordable than GPUs and TPUs
Slower for training due to lack of accelerated parallel processing
GPU
Training
Excellent for training due to parallel processing
More expensive than CPUs
Surpassed by TPUs for deep learning tasks
TPU
Preprocessing and training
Faster computation time and cost-effectiveness for deep learning tasks
Requires specific software
infrastructure
Limited to Google platforms
NPU
Training
Optimized for deep learning and especially good for on-device AI applications, reducing reliance on cloud services
Limited to specific AI workloads, primarily neural network-based tasks
IPU
Training
Especially good for graph-based tasks such as GNNs
Can be more complex to program and optimize compared to NPUs

Compare processor workloads, benefits, limitations, and suitability when interpreting hardware benchmark results. section 7.4.2

How Graph Neural Networks in Action covers it

How deep it goes
Provides a practical measurement workflow and troubleshooting example, but does not specify statistical benchmarking methodology.
Examples
Uses CPU-versus-GPU timing, a 10 GB sparse graph, subgraph sampling, and Open Graph Benchmark datasets.
What it assumes
Assumes familiarity with datasets, machine-learning training, GNNs, and computing hardware while teaching baseline metrics from practical planning.
What you can do after
Evaluate hardware and GNN configurations using representative data and measurements such as memory, epoch time, FLOP/s, and convergence time.
How it connects
Connects benchmarking with hardware selection, dataset representation, GNN algorithms, batching, parallel computing, remote backends, and graph coarsening.
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