Why is my multi-GPU training slower than single-GPU training

Question

With the help of code, can you tell me why my multi-GPU training is slower than single-GPU training?

Answer 1

Multi-GPU training can become slower due to communication overhead, inefficient batch sizes, or improper data parallelism strategies. 

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Communication Overhead: Multi-GPU requires synchronization, which can slow down performance.
• Inefficient Batch Size: Small batch sizes may not fully utilize GPU power.
• Imbalanced Workload: Uneven distribution across GPUs can cause bottlenecks.
• Data Transfer Delays: Slow PCIe or memory bandwidth can impact training.
• Optimization Required: Techniques like torch.nn.DataParallel or torch.distributed can improve efficiency.

Hence, By referring to above, you can know why multi-GPU training slower than single-GPU training.