Jeet: Speeding WebAssembly Builds for Faster Time to Interactive

Problem Statement

Modern web applications often ship a large bundle of JavaScript. When developers want to push compute‑heavy logic into WebAssembly (Wasm), the usual LLVM‑based toolchain can inflate build times and produce binaries that are too big for the "time to interactive" metric that matters to users. A long compile step also slows the feedback loop for developers, and a large Wasm module can delay the first paint in the browser.

Thesis: Jeet’s Pipeline Cuts Build Time & Binary Size

Jeet is a specialized Wasm compiler that replaces the heavy LLVM backend with a lightweight, browser‑aware pipeline. Its goals are:

• Reduce the compilation time from source to .wasm.
• Shrink the binary size without sacrificing execution speed.
• Respect browser constraints such as the 4 MiB memory limit for Wasm modules and the sandboxed execution model.

The trade‑off is that Jeet focuses on a narrow set of optimizations that work well for typical compute loops but may not match the full optimization suite of LLVM for very complex code.

Architecture Overview

Jeet’s core is a two‑stage pipeline:

1. Front‑end – A lightweight parser that accepts C++ and Rust (via a subset of the language). It emits an intermediate representation (IR) that is smaller than LLVM IR.
2. Back‑end – A custom Wasm emitter that applies a focused set of optimizations: inlining of small functions, loop unrolling up to a configurable depth, and aggressive dead‑code elimination. It also prunes unused imports to keep the binary lean.

Because the back‑end is tuned for the browser environment, it avoids generating code that would trigger large memory footprints or rely on features not yet fully supported in all engines.

Worked Example: A Compute‑Intensive Loop

We’ll compile a simple 2‑D matrix multiplication written in C++ using both the standard LLVM toolchain and Jeet. The goal is to compare compile time, binary size, and runtime performance in a browser.

// matrix.cpp – a naive 512×512 matrix multiply
#include <vector>

extern "C" {
void matmul(const float* a, const float* b, float* out, int N) {
    for (int i = 0; i < N; ++i) {
        for (int j = 0; j < N; ++j) {
            float sum = 0;
            for (int k = 0; k < N; ++k) {
                sum += a[i*N + k] * b[k*N + j];
            }
            out[i*N + j] = sum;
        }
    }
}
}

Standard LLVM Build

Run on a machine with LLVM 18 installed:

# Build with clang targeting WebAssembly
clang++ -O3 -fno-exceptions -fno-rtti \
  --target=wasm32-unknown-unknown \
  -nostdlib -Wl,--no-entry -Wl,--export=matmul \
  matrix.cpp -o matrix_llvm.wasm

Checks:

• Compile time: use the time command to measure wall‑clock duration.
• Binary size: stat -c %s matrix_llvm.wasm (expect ~400 KiB for this example).
• Runtime: load matrix_llvm.wasm in a minimal HTML page and call matmul from JavaScript, measuring execution with performance.now().

Jeet Build

Assuming Jeet is installed and available as jeet:

# Build with Jeet, targeting WebAssembly
jeet build -O3 -o matrix_jeet.wasm matrix.cpp

Checks (same as above):

• Compile time: expect ~30 % faster than the LLVM build on the same hardware.
• Binary size: typically 20–30 % smaller; for this example ~280 KiB.
• Runtime: benchmark the same call. Jeet’s focused optimizations usually give a 5–10 % speedup for tight loops, but results can vary based on the browser engine.

Practical Verification Steps

1. Compile both binaries and record the timestamps.
2. Use wasm-objdump -x matrix_*.wasm to inspect the number of functions and imports; Jeet should have fewer unused imports.
3. In the browser, create a simple index.html that loads the Wasm module and runs the matrix multiply 10 times, logging the average time.
4. Compare the results. If the Jeet binary is noticeably smaller and the runtime is similar or faster, the trade‑off is beneficial.

Trade‑Offs & Limitations

• Source Language Support – Jeet currently targets a subset of C++ (no templates, limited STL). Complex projects may still need LLVM.
• Browser Compatibility – While Jeet emits standard WebAssembly, some optimizations rely on features that are only fully supported in V8 and SpiderMonkey. Test against your target browsers.
• Feature Gaps – Jeet does not yet expose all LLVM passes (e.g., aggressive vectorization). If your code requires those, you may need to fall back to the full LLVM toolchain.
• Maintenance – The Jeet project is actively evolving. Keep an eye on release notes for changes that might affect binary size or performance.

Actionable Takeaway

If your web app has compute‑heavy modules that need to load quickly, try compiling a representative workload with Jeet. Measure compile time, binary size, and runtime on the browsers you target. If the numbers line up with the benefits shown above, you can replace the standard LLVM step for those modules, saving developer time and improving user experience. For larger or more complex codebases, consider a hybrid approach: use Jeet for hot loops and LLVM for the rest.