Collection of codes to benchmark GPU machine
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When benchmarking GPU hardware for deep learning, it's important to consider various factors to assess the performance and efficiency of the hardware. Here are some key considerations:
- GPU Utilization: Batch size significantly impacts GPU utilization. Larger batch sizes can fully exploit the parallel processing capabilities of modern GPUs, resulting in higher throughput.
- Memory Usage: Larger batch sizes require more GPU memory. Benchmarking with different batch sizes helps assess how well the GPU can handle varying memory demands.
- Memory and Throughput: Input size affects memory consumption and the amount of data processed per iteration. Larger input sizes may require more memory and might influence the data transfer rates between CPU and GPU.
- Model Complexity: Different neural network architectures have varying numbers of parameters and computational requirements. A more complex model might stress the GPU more than a simpler one.
- Algorithm and Operator Compatibility: Some GPUs have hardware acceleration for specific deep learning operations. Benchmarking different architectures can help assess how well the GPU performs with various network designs.
In summary, the choice to change batch size, input size, or network architecture during GPU benchmarking depends on your objectives. Some benchmarking tasks focus on assessing raw GPU performance and efficiency, so they might test different batch sizes and input sizes to understand how the hardware behaves under varying workloads.
To compare the efficiency of different GPUs, it's essential to control these parameters to create a fair comparison. Benchmarking can provide valuable insights into how a specific GPU handles different deep learning workloads, helping you make informed hardware choices.