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Every wasm-opt, wasm-merge, wasm-ctor-eval, and wasm-metadce subprocess spawned by fuzz_opt.py initializes a Binaryen ThreadPool sized to hardware_concurrency() (e.g. 128 threads on a 128-core workstation) and synchronizes across condition variables for each function-parallel pass. On small fuzzer modules, thread creation and barrier synchronization dominate execution time and cause severe contention when running multiple fuzzer workers in parallel. Default BINARYEN_CORES to 4 so parallel pass execution is still exercised without spawning a thread per hardware core in every subprocess. Steady-state throughput increases by +97.7% (1.37 -> 2.71 iters/s, 1.98x) for a single worker and +186.4% (25.77 -> 73.78 iters/s, 2.86x) across 32 parallel workers on a 128-thread machine.
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This pull request updates scripts/fuzz_opt.py to set a default value of '4' for the BINARYEN_CORES environment variable. This limits the default thread pool size per subprocess, ensuring parallel passes are exercised without spawning a thread for every hardware core on each invocation. There are no review comments to address, and I have no additional feedback to provide.
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Every wasm-opt, wasm-merge, wasm-ctor-eval, and wasm-metadce subprocess spawned by fuzz_opt.py initializes a Binaryen ThreadPool sized to hardware_concurrency() (e.g. 128 threads on a 128-core workstation) and synchronizes across condition variables for each function-parallel pass. On small fuzzer modules, thread creation and barrier synchronization dominate execution time and cause severe contention when running multiple fuzzer workers in parallel. Default BINARYEN_CORES to 4 so parallel pass execution is still exercised without spawning a thread per hardware core in every subprocess.
Steady-state throughput increases by +97.7% (1.37 -> 2.71 iters/s, 1.98x) for a single worker and +186.4% (25.77 -> 73.78 iters/s, 2.86x) across 32 parallel workers on a 128-thread machine.