How does JAX handle memory fragmentation differently on GPUs compared to TPUs

Memory Fragmentation on GPUs

- Preallocation Strategy: JAX preallocates 75% of the total GPU memory by default when the first operation is run. This strategy minimizes allocation overhead and memory fragmentation but can lead to out-of-memory errors if not managed properly[1][3].
- Memory Fragmentation Issues: GPUs have a complex memory hierarchy, which can exacerbate memory fragmentation issues. This complexity makes it challenging for JAX to efficiently manage memory without fragmentation[6].
- Lack of Automatic Defragmentation: Unlike TPUs, GPUs do not have built-in automatic memory defragmentation in JAX. Implementing such a feature is considered but not currently planned[7].

Memory Fragmentation on TPUs

- Simplified Memory Hierarchy: TPUs have a simpler memory hierarchy compared to GPUs, which reduces the likelihood of significant memory fragmentation issues[6].
- Automatic Memory Defragmentation: The TFRT TPU backend supports automatic memory defragmentation, which helps maintain efficient memory usage and reduces fragmentation[7].
- Sequential Processing: TPUs process data sequentially, which can lead to more predictable memory usage patterns and potentially less fragmentation compared to the parallel processing on GPUs[8].

Overall, JAX's memory management on GPUs is more prone to fragmentation due to the lack of automatic defragmentation and the complex GPU memory hierarchy, whereas TPUs offer a more streamlined approach with built-in defragmentation capabilities.