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Event loop worker pool inspired by libuv's "one loop per thread" model. Each loop has its own worker and maintains event ordering. Process affinity: All tasks from the same Erlang process are routed to the same event loop (via PID hash), guaranteeing that timers and related async operations execute in order. Changes: - py_event_loop_pool.erl: Rewrite with process affinity, persistent_term for O(1) access, distributed task API (create_task, run, spawn_task, await) - py_event_loop.erl: Export get_process_env/0 for pool use - py_event_loop_pool_SUITE.erl: New test suite (9 tests) - bench_event_loop_pool.erl: New benchmark Configuration: - event_loop_pool_size: Number of loops (default: schedulers count) Benchmarks (Python 3.14, 14 schedulers): - Sequential (pool): 150k tasks/sec (vs 83k single loop) - Concurrent (50 procs): 164k tasks/sec - Fire-and-collect: 417k tasks/sec
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Summary
Benchmarks (Python 3.14, 14 schedulers)
Configuration
{event_loop_pool_size, N} % default: schedulers count