JAX employs several techniques to minimize memory fragmentation when running on TPUs:
1. Memory Hierarchy Utilization: TPUs have a sophisticated memory hierarchy, including High Bandwidth Memory (HBM), vector memory, scalar memory, and accumulator memory. JAX optimizes computations by structuring data to minimize transfers between these memory levels, which helps reduce fragmentation by ensuring efficient memory usage[3][5].
2. Prefetching and Buffering Strategies: Prefetching data hides memory access latency, while techniques like circular buffers and double buffering are used for streaming data and continuous computation. These strategies help manage memory more efficiently, reducing the likelihood of fragmentation[3].
3. Sharding and Parallel Processing: JAX uses sharding to split computations across TPU cores, which helps manage memory more effectively by distributing the workload. This approach ensures that memory is allocated and deallocated in a way that minimizes fragmentation[3][7].
4. Efficient Data Layout: JAX optimizes data layouts to match TPU-friendly sizes, often padding dimensions to multiples of optimal sizes (e.g., 128). This ensures that memory is allocated in contiguous blocks, reducing fragmentation[3][5].
5. JIT Compilation and Memory Reuse: JAX's Just-In-Time (JIT) compilation and memory reuse mechanisms help minimize unnecessary allocations. By reusing memory blocks when possible, JAX reduces the frequency of new allocations, which can lead to fragmentation[1][3].
These techniques collectively contribute to minimizing memory fragmentation and optimizing performance on TPUs.
Citations:[1] https://app.studyraid.com/en/read/11969/381958/memory-management-best-practices
[2] https://www.datacamp.com/tutorial/combine-google-gemma-with-tpus-fine-tune-and-run-inference-with-enhanced-performance-and-speed
[3] https://app.studyraid.com/en/read/11969/381956/tpu-computation-strategies
[4] https://stackoverflow.com/questions/60871/how-to-solve-memory-fragmentation
[5] https://cloud.google.com/tpu/docs/performance-guide
[6] https://www.kaggle.com/code/matthewdwatson/gemma-2-tpu-fine-tuning
[7] https://docs.jax.dev/en/latest/pallas/tpu/distributed.html
[8] https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html