// core engineering optimization
Tighter inner loops. Faster outer ones.
We rebuild the hottest 3% of your codebase and the workflow around it — so your service responds in microseconds, and your team ships in minutes.
The inner loop sets the ceiling.
Every system has an inner loop — the small block of code that runs millions of times per second, the build-test cycle a developer hits hundreds of times a day, the deployment train that gates every release. Optimize it, and everything above it gets faster, cheaper, and calmer.
We embed with engineering teams to profile, redesign, and ship the changes that move p99 latency, throughput, and developer velocity in the same direction. No vague audits — measured deltas, merged PRs, and runbooks your team owns when we leave.
# flame graph excerpt — order book matching engine$ perf record -F 4000 -g ./match_engine→ collected 2,481,003 samples62.1% OrderBook::insert ← std::map rebalance14.3% malloc · jemalloc arena lock09.7% memcpy · order serialization04.2% syscall · gettimeofday()# after: intrusive RB-tree + arena alloc + TSC clock✓ p99 1.4ms → 184µs · throughput +6.4×
// velocity telemetry
Measured against your own baseline.
Typical 12-week engagement deltas across recent inner-loop programs. Numbers are medians across our last 14 production deployments — full case studies on the Services and Industries pages.
p99 latency cut
CI wall-clock cut
Infra cost cut
Merges per dev / wk
// concurrency matrix
There is no single right primitive.
Pick the wrong synchronization model and you'll spend a quarter chasing tail latency. We benchmark the candidates against your actual workload before a single line of production code changes. Hover the cells.
| scale ↓ · pattern → | Mutex | RWLock | Lock-free | Sharded | Actor |
|---|---|---|---|---|---|
| 1 core | 100× Baseline single-thread | 100× No contention | 108× CAS overhead | 104× Single shard | 96× Mailbox overhead |
| 4 cores | 140× Lock convoy | 220× Reader-heavy wins | 380× Cache-line tuned | 340× Hash-partitioned | 300× Bounded queues |
| 16 cores | 165× Contention cliff | 280× Reader fairness drops | 1,180× NUMA-aware | 990× 16 shards · hot-key risk | 920× Backpressure tuned |
| 64 cores | 170× Pathological | 260× Writer starvation | 3,680× Hazard pointers | 3,100× 64 shards | 2,750× Supervisor trees |
// Relative throughput (×baseline). Hover a cell to inspect. Source: internal harness · pinned threads · 99p latency budget held.
// delivery pipeline
A perf-gated path from commit to canary.
The CI architecture we ship enforces latency and throughput SLOs as a build step — regressions never reach production. Below: the typical pipeline shape after an InnerLoop engagement.
// PR → prod median 6m 12s · perf-gate rejects regressions > 3% on p99 before canary
Have a hot path that won't behave?
Send us a flame graph, a CI bottleneck, or a deployment story. We respond within one business day with a concrete first move.
info@innerloopresources.com