Training on one TPU core versus eight can significantly impact performance due to the distributed nature of TPUs. Each TPU has eight cores, and these cores are optimized for parallel processing of tensor operations, which are fundamental to neural network computations[8]. When using a single TPU core, the model trains on that specific core, leveraging its optimized architecture for matrix multiplies[8]. However, when using all eight cores, the training process can distribute across these cores, potentially leading to faster training times due to parallelization.
In general, using multiple cores allows for better utilization of the TPU's resources, such as higher memory bandwidth and interconnect capabilities, which can enhance overall training throughput[2]. This is particularly beneficial for large-scale models or datasets where parallel processing can significantly reduce training time. However, the actual performance gain depends on how well the model and data are optimized for distributed training on TPUs.
In some cases, if the model or data are not well-suited for parallelization across multiple cores, the performance difference might not be as pronounced. Additionally, factors like I/O bottlenecks or model complexity can influence the effectiveness of using multiple TPU cores[2]. Nonetheless, for most machine learning tasks, utilizing all eight cores of a TPU typically offers better performance compared to using just one core.
Citations:[1] https://www.datacamp.com/blog/tpu-vs-gpu-ai
[2] https://eng.snap.com/training-models-with-tpus
[3] https://blog.google/technology/ai/difference-cpu-gpu-tpu-trillium/
[4] https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu
[5] https://www.linkedin.com/pulse/gpus-vs-tpus-comprehensive-comparison-neural-network-workloads-joel
[6] https://www.reddit.com/r/MachineLearning/comments/19e8d1a/d_when_does_it_make_sense_to_train_on_tpu/
[7] https://cloud.google.com/blog/products/compute/performance-per-dollar-of-gpus-and-tpus-for-ai-inference
[8] https://lightning.ai/docs/pytorch/1.6.0/accelerators/tpu.html