Profiling JAX Code on CPU, GPU, and TPU

While jax.jit provides a powerful entry point to accelerating your code, achieving peak performance requires a more analytical approach. Code that runs fast on a CPU might behave differently on a GPU or TPU due to architectural variations, memory bandwidth limitations, and specific hardware optimizations performed by the XLA compiler. This is where profiling becomes indispensable. Profiling allows you to look under the hood of your JAX execution, pinpointing exactly where time is being spent and identifying opportunities for optimization specific to your target hardware.

Effective profiling helps answer critical questions:

• Which specific operations consume the most execution time?
• Is the program bottlenecked by computation (CPU/GPU/TPU bound) or by data movement (memory bound)?
• Is the accelerator being utilized efficiently, or are there significant idle periods?
• Are there unexpected delays caused by host-device data transfers?
• Is the program suffering from excessive recompilation overhead?

By understanding these aspects, you can move past guesswork and apply targeted optimizations.

Using jax.profiler for Trace Collection

JAX includes a built-in profiler, jax.profiler, designed to capture detailed execution traces compatible with standard visualization tools like TensorBoard. It allows you to record the sequence and duration of operations executed on the host (CPU) and the accelerator devices (GPU/TPU).

The core functions are jax.profiler.start_trace and jax.profiler.stop_trace. You wrap the section of code you want to analyze within these calls.

import jax
import jax.numpy as jnp
import jax.profiler
import time

# Ensure JAX uses the desired backend (e.g., GPU)
# jax.config.update('jax_platform_name', 'gpu')

@jax.jit
def complex_computation(x, y):
  # A series of JAX operations
  z = jnp.dot(x, y)
  z = jnp.sin(z)
  z = jnp.mean(z * x + y)
  return z

# Prepare some data
key = jax.random.PRNGKey(0)
size = 2000
x = jax.random.normal(key, (size, size))
y = jax.random.normal(key, (size, size))

# Ensure data is on the device before profiling
x = jax.device_put(x)
y = jax.device_put(y)
_ = complex_computation(x, y).block_until_ready() # Warm-up compilation

# Start profiling
log_dir = "/tmp/jax_profiling"
jax.profiler.start_trace(log_dir)

# Run the computation multiple times to get a representative trace
for _ in range(5):
    result = complex_computation(x, y)
    # Important: Block to ensure computation completes before stopping trace
    result.block_until_ready()

# Stop profiling
jax.profiler.stop_trace()

print(f"Profiling trace saved to: {log_dir}")
# You can now view this trace using TensorBoard:
# tensorboard --logdir /tmp/jax_profiling

This code snippet demonstrates the basic workflow:

1. Import necessary libraries.
2. Define the JIT-compiled function to profile.
3. Prepare input data and explicitly place it on the target device using jax.device_put.
4. Perform a warm-up run to trigger compilation before starting the profiler.
5. Use jax.profiler.start_trace(log_dir) to begin recording. log_dir specifies where the trace files will be saved.
6. Execute the function(s) of interest. It's often useful to run the code multiple times within the profiling context.
7. Crucially, use .block_until_ready() after the computation inside the loop (or after the whole loop if profiling a sequence). JAX's asynchronous dispatch means the Python function might return before the accelerator has finished. Blocking ensures the profiler captures the complete execution.
8. Use jax.profiler.stop_trace() to finalize and save the trace files.

Visualizing Traces with TensorBoard

The trace files generated by jax.profiler are designed to be visualized using TensorBoard. Launch TensorBoard by pointing it to the directory where you saved the traces:

tensorboard --logdir /tmp/jax_profiling

Navigate to the "Profile" tab in the TensorBoard web interface. You'll find several useful tools:

1. Trace Viewer: This is often the most informative view. It presents a timeline chart showing operations executed over time across different processing units.

   • Host Threads: Show activity on the CPU, including Python function calls, JAX dispatch overhead, and potentially some NumPy operations if your code mixes them.
   • Device Streams (e.g., /GPU:0/stream:all, /TPU:0/stream:all): Display kernels executing on the accelerator. Different streams might handle computation, memory copies (like HtoD for Host-to-Device, DtoH for Device-to-Host), or communication.
   • Analysis: Look for long-running kernels on the device streams (potential compute bottlenecks), large gaps indicating idle time, or significant time spent on HtoD/DtoH copy operations (data transfer bottlenecks).

   The process of generating and viewing JAX profile traces.

A simplified timeline view similar to TensorBoard's Trace Viewer, showing concurrent activity on CPU and GPU streams. Notice the gap on the GPU Compute stream between 2.5ms and 4ms, potentially indicating idle time or waiting for data.

2. Ops View: Provides aggregated statistics for each type of operation executed (e.g., dot_general, sin, reduce_mean). It shows total time spent, average time, and number of calls. This helps quickly identify the most computationally expensive JAX primitives in your code.

3. Memory Viewer: Helps diagnose memory-related issues by showing memory allocation patterns over time. High peak memory usage or frequent allocation/deallocation cycles might indicate problems. (Availability and detail level can vary).

Device-Specific Notes

Profiling needs slight adjustments depending on the target hardware:

• CPU: While jax.profiler captures JIT-compiled work on the CPU, standard Python profilers like cProfile remain useful for analyzing the parts of your code that run in pure Python interpretation (e.g., data loading, preprocessing loops outside JIT, overall script logic). Interleaving JITted functions with significant Python logic can introduce overhead easily visible in CPU profiles.
• GPU: TensorBoard's trace viewer clearly separates GPU compute streams from memory copy streams (PCIe transfers). Pay close attention to the duration of HtoD and DtoH operations. Long copy times suggest your data transfer strategy needs optimization. Are you moving unnecessary data? Can data stay on the GPU longer? For extremely detailed kernel analysis, tools like NVIDIA Nsight Systems and Nsight Compute can provide deeper insights into individual CUDA kernel performance, but TensorBoard is usually sufficient for JAX-level optimization.
• TPU: TPU profiling in TensorBoard often shows high utilization of the specialized hardware units (like MXUs for matrix multiplication). Look for metrics specific to TPU performance, such as MXU utilization percentage. Operations might be padded to fit the TPU's hardware requirements (e.g., dimensions divisible by 128 for MXU), which can sometimes appear as overhead. Low utilization might indicate bottlenecks elsewhere (e.g., input pipeline, host CPU) or that the computation isn't well-suited to the TPU architecture. For TPU VMs, Google Cloud Profiler can offer system-level insights.

Identifying Common Bottlenecks

Profiling often reveals recurring performance patterns:

1. Host-Device Data Transfer: Visible as long HtoD/DtoH bars in the trace viewer's copy streams. Minimize these by moving data to the device once and keeping it there as long as possible. Process data in batches on the device rather than element by element.
2. Small Kernels / Launch Latency: A large number of very short bars on the device stream. Each kernel launch has some overhead. JAX's JIT compilation (and XLA) tries to fuse operations to mitigate this, but excessive fragmentation can still occur. Aim for larger, fused kernels where possible.
3. Idle Device Time: Significant gaps in the device compute stream timeline. This could mean the host CPU isn't feeding data fast enough, data transfers are blocking computation, or the computation itself has synchronization points causing waits.
4. Compute-Bound Operations: One or a few very long bars dominate the device compute stream. This indicates specific JAX operations are the main consumers of time. Optimization might involve algorithmic changes, using more efficient primitives, or checking if mixed-precision training (covered later) is applicable.

Profiling is an iterative process. Use the insights gained from TensorBoard to hypothesize about bottlenecks, implement changes in your code (like optimizing data movement, adjusting JIT compilation strategies, or modifying algorithms), and then profile again to measure the impact. Remember to use block_until_ready() when timing or profiling JAX code to account for asynchronous execution and get accurate measurements of the actual accelerator work.