· Energy · 2 min read
Scaling optimization runs: bottlenecks and caching
A practical guide to making optimization workloads faster and cheaper.
Scaling optimization runs is about removing the biggest bottlenecks first. Many optimization workloads are slowed down by data access patterns and repeated computations, not by the solver itself.
Profile a single scenario before changing anything. Measure time spent in data loading, solver execution, and result export. This gives you a baseline and prevents guesswork.
Parallelism and batching
Run independent scenarios in parallel, but set resource limits so you do not starve other systems. Batch similar runs to reuse data and warm caches. Keep batch sizes small enough that failures are isolated.
Caching inputs and outputs
Cache processed inputs when they are reused across scenarios. Cache solver outputs during iterative tuning. Use clear version keys so caches are invalidated when inputs or code change.
Cost aware scheduling
Run large batches in off peak windows when possible. Separate urgent operational runs from research workloads. Queue depth is often more important than raw runtime.
Add monitoring for queue time and cache hit rates. Without these metrics, performance gains can erode silently as workloads evolve.
Performance gains are usually simple. The hard part is keeping them after the system evolves, so document the changes and monitor their impact.
Separate data preprocessing from solver execution. If preprocessing is reused, cache it and avoid recomputation. This can produce large wins without changing the solver.
Use small representative datasets for development. Large datasets are useful for final validation, but they slow down iteration. A staged approach keeps teams productive.
Track cost per run and cost per decision. If you cannot explain the cost of an optimization cycle, it will be hard to justify scaling it.
