Summary: Tips for profiling WebAssembly

I couldn't find a comprehensive guide to profiling WebAssembly, so I thought I'd share my own limited experience here. In my last post, I talked about benchmarking a WebAssembly port of the scientific Python stack. I knew which benchmarks were doing better than others and had some theories about why, but since I didn't yet know how to profile WebAssembly, I couldn't really answer that with any certainty.

It turns out that profiling WebAssembly is quite easy.

Rebuilding with the --profiling flag

The first step is to rebuild the application with the --profiling flag passed to both the compiler and the linker for every object. This makes sure that all of the information necessary for profiling is available in the output and makes the code more readable. The typical way to do this would be to set CFLAGS and LDFLAGS and let the ./configure script for your project pick those up. In the case of pyodide, the Python cross-compiling setup makes that tricky, or at least I couldn't figure it out in a short amount of time. Fortunately, emscripten provides a handy backdoor to just force this on everything: the EMCC_CFLAGS environment variable. Therefore, to make a profiling-friendly build of pyodide:

make clean
EMCC_CFLAGS=--profiling make

Setting start and stop points for profiling

You generally don't want to profile an entire run, which would include initialization and other things. It turns out there's a handy Javascript API to turn the profiler on and off.

  • console.profile() turns the profiler on.
  • console.profileEnd() turns the profiler off.

If you wanted to call these from C/C++, you could use the EM_ASM macro, which allows you to insert literal Javascript into the C application:


In my case, I wanted to turn the profiler on and off from Python, so I can do:

from js import console


The actual profiling is performed within the development tools of your browser. When you load an .html file that runs the WebAssembly built and instrumented as above, it will record a set of profiling data available from the Performance tab.

I'll refer you to the Performance Tools documentation for more information. Suffice it to say that profiling WebAssembly is almost exactly like profiling vanilla Javascript in the browser.

Case study

For pyodide, I created a profiling build to look into the julia benchmark that I knew was performing poorly. Right away, I noticed from the Call Tree that 50% of the time was spent in this function:

for (var named in NAMED_GLOBALS) {
    (function(named) {
        Module['g$_' + named] = function() {
            return Module['_' + named] // <- 50% of runtime HERE

This code is actually part of the boilerplate that emscripten emits. It helps dynamically loaded modules (such as Numpy in my case) access symbols in the main module. Since these symbols don't change at runtime, we don't actually need to do the dictionary lookup for Module['_' + named] every time, we can cache (memoize) it at startup and then just use that:

for (var named in NAMED_GLOBALS) {
    (function(named) {
        var func = Module['_' + named];
        Module['g$_' + named] = function() {
            return func;

This 2-line change to emscripten resulted in significant speedups in my pyodide benchmarks across the board.


Here, the x-axis is the number of times slower that WebAssembly runs vs. native code. The grey bars are the timings before this change, and blue bars are the timings after this change.

More details about this changes are in the pull request.


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