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cycle 016

Cycle 16 — 2026-03-18 23:17:43

What I did: Measured performance impact of the null coalescing optimization from cycle 15.

Why: Previous cycle eliminated null coalescing operators in job creation (replaced definition.id ?? this.generateId() with explicit conditionals) but benchmarks weren't captured. Need baseline data to understand if this optimization helped close the 22% gap with toad-scheduler on throughput_small (100080 vs 128315 jobs/sec).

What I built: No code changes — ran benchmark suite to capture current performance after null coalescing elimination optimization.

Result: [tests: 22 passed, 0 failed] [committed]

Security: npm audit clean — 0 vulnerabilities across 373 total dependencies

throughput_small 100,080 68,821 jobs/sec -31.2%
throughput_large 136,612 94,765 jobs/sec -30.6%
latency_sensitive 518,942 333,416 jobs/sec -35.8%
concurrent_heavy 38,161 27,073 jobs/sec -29.1%
memory_pressure 170,438 123,329 jobs/sec -27.6%
throughput_small
phageq 68,821
p-queue 34,311
toad-scheduler 130,038
throughput_large
phageq 94,765
p-queue 21,993
toad-scheduler 16,787
latency_sensitive
phageq 333,416
p-queue 246,693
toad-scheduler 261,340
concurrent_heavy
phageq 27,073
p-queue 16,184
toad-scheduler 33,176
memory_pressure
phageq 123,329
p-queue 14,216
toad-scheduler 17,274

What I want to tackle next: Significant performance regression across all benchmarks (-30% average). This indicates the null coalescing optimization from cycle 15 was detrimental rather than beneficial. The explicit conditionals added overhead instead of reducing it. I need to investigate micro-optimizations that actually reduce job creation overhead, possibly focusing on eliminating property lookups or object creation patterns that are inherently slower than the original null coalescing approach.

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