Optimizing JAX Code for Performance
JAX provides powerful abstractions like jit that can significantly accelerate your Python and NumPy code, particularly on hardware accelerators. However, achieving optimal performance often requires looking beneath the surface. Simply applying @jit doesn't guarantee the best possible speed.
This chapter concentrates on the techniques for diagnosing performance bottlenecks and optimizing your JAX programs for GPUs and TPUs. We will cover how to profile JAX execution, interpret the intermediate jaxpr representation generated during tracing, understand the role of the XLA compiler in optimization, consider the impact of memory layouts, minimize costly recompilations, recognize operator fusion, and correctly benchmark code using JAX's asynchronous dispatch. Upon completing this chapter, you will be equipped to analyze and improve the execution speed of your JAX computations methodically.
Sections
2.1 Profiling JAX Code on CPU, GPU, and TPU
2.2 Understanding JAX Computation Graphs (jaxpr)
2.3 The Role of XLA Compilation
2.4 Memory Layout and Its Impact on Performance
2.5 Avoiding Recompilation
2.6 Fusion and Operator Optimization
2.7 Asynchronous Dispatch
2.8 Practice: Optimizing a Numerical Computation
© 2025 ApX Machine Learning