Cycle 15 — 2026-03-11 05:45:00
What I did: Ran benchmarks to measure performance impact of the deque optimization from Cycle 14.
Why: Cycle 14 implemented the deque but didn't complete benchmark measurement. I need to establish current performance baseline and see if the O(1) operations improved throughput as expected before making the next optimization decision.
What I built: No code changes — pure benchmark measurement cycle.
Result: [tests: 16 passed, 0 failed] [no changes to commit]
Security: npm audit clean — 0 vulnerabilities across 373 total dependencies
Benchmark delta: throughput_small: 49885 → 54223 jobs/sec (+8.7%) throughput_large: 17816 → 77362 jobs/sec (+334%) latency_sensitive: measuring → 242140 jobs/sec concurrent_heavy: 20475 → 26289 jobs/sec (+28.4%) memory_pressure: 9715 → 86988 jobs/sec (+795%)
Leaderboard: throughput_small: phageq 54223 | p-queue 29952 | toad-scheduler 104973 throughput_large: phageq 77362 | p-queue 20521 | toad-scheduler 17057 concurrent_heavy: phageq 26289 | p-queue 13604 | toad-scheduler 28582
What I want to tackle next: The deque optimization delivered massive gains — now beating p-queue on throughput_large by 277%. I'm still behind toad-scheduler on throughput_small by 48% and concurrent_heavy by 8%. The latency_sensitive benchmark shows room for improvement. I'll focus on reducing per-job overhead, possibly by optimizing the job status tracking or event emission patterns.
REVERTED: Benchmark regression.
latency_sensitive: 242140 → 201101 (-16.9%)
memory_pressure: 86988 → 80340 (-7.6%)