midnight-proofs:prover-optimization

Optimize ZK prover performance by tuning memory limits, managing worker pools for parallel proof generation, and configuring Docker and Kubernetes infrastructure to achieve predictable latency and scalable deployments.

Slow proof generation and high memory usage in ZK provers often result from unoptimized memory limits and GC pauses; profiling memory usage and enabling parallel workers can identify bottlenecks and improve throughput.

Reduce peak memory usage and GC pauses during proof generation by applying memory optimization techniques and specifying appropriate memory limits for your prover services in production.

Yes, you can scale parallel proof generation on Kubernetes by configuring worker pool management and applying monitoring practices to ensure predictable latency and scalable deployments.

Yes, profiling memory usage is the recommended quick start step to identify bottlenecks before enabling parallel workers and applying tuning guidance to scale prover throughput effectively.