Performance Architecture

Optimization Strategy

Correctness comes first. Optimization follows measurement. The benchmark engine minimizes overhead in measured regions and records enough raw data to distinguish workload performance from framework overhead.

Memory Strategy

• Prepare reusable inputs outside measured loops.
• Avoid unnecessary copies.
• Keep raw observations compact.
• Stream large observation sets where possible.
• Downsample visualization data without losing extrema.

Caching Strategy

Cache only deterministic and safe values:

• Parsed configuration.
• Capability discovery results.
• Model metadata.
• Static report templates.
• Plugin descriptors.

Caches must be invalidated when inputs, package versions, plugin versions, or hardware capabilities change.

Concurrency

Initial concurrency is conservative. Future parallel execution requires explicit resource isolation:

• Device locks.
• Output directory isolation.
• Profiler isolation.
• Random seed policy.
• Scheduler metadata.

Parallel Execution

Parallel execution is modeled as a future scheduler subsystem. It should support local process pools, remote workers, and hardware lab queues without changing domain models.

Lazy Loading

Optional runtimes and vendor SDKs are imported only inside their adapters. Core package imports must remain fast and lightweight.

Large Dataset Handling

Large benchmark campaigns should use streaming observations, artifact manifests, paginated dashboard queries, and incremental report generation.

Performance Validation

Performance-sensitive changes require benchmark evidence or an engineering rationale. Framework overhead should be measured with fake and minimal backends.