Welcome to the third lesson of our course "Advanced JAX: Transformations for Speed & Scale"! It's wonderful to continue this journey with you as we explore increasingly sophisticated JAX transformations. In our previous lessons, you mastered JAX's functional approach to randomness through explicit key management and learned how jax.vmap enables elegant, automatic vectorization across batches. These concepts of explicit control and efficient batch processing perfectly prepare us for today's topic: parallel computing across multiple devices.
In this lesson, we'll dive into Single Program, Multiple Data (SPMD) parallelism with JAX's modern jax.shard_map transformation, while also examining the now-deprecated jax.pmap for historical context in legacy code. Just as you learned to explicitly manage randomness and batching, SPMD parallelism allows you to explicitly control how computations are distributed across multiple devices — whether CPUs, GPUs, or TPUs. This capability is fundamental for scaling machine learning and scientific computing to handle truly massive datasets and complex models.
By the end of this lesson, you'll understand how to create device meshes, partition data across multiple devices, implement collective operations for cross-device communication, and leverage JAX's parallelism for significant performance gains. You'll also appreciate why the JAX ecosystem has evolved from pmap to shard_map, and how these tools integrate with the transformations you've already mastered.
Before exploring JAX's parallelism tools, let's understand the computational challenges they address. In modern machine learning and scientific computing, we frequently encounter problems that are too large or too computationally intensive for a single device to handle efficiently:
