Comprehensive analysis and report for performance metrics.
Provide Comprehensive report and analysis to protocols/frameworks which can ultimately help them improve the protocol's efficiency, security and identify pros/cons along with trade-offs when compared with other protocols.
- Why it matters: Use statistical techniques which can uncover hidden inefficiencies within the proving/verifying system to identify which variable attributes like input program, system memory/cpu have the greatest impact on different metrics.
- Metrics to Investigate:
- Cycles vs. Proof Size: Does increased computation directly reduce proof size, or is there a point of diminishing returns?
- Memory vs. Runtime: Is memory usage the main bottleneck, or are there other computational factors at play?
- Security Level vs. Cycles: Is increased security achieved through more computations, leading to longer execution times?
- Why it matters: Tracking performance changes throughout development reveals regressions, highlights progress, and pinpoints performance-critical code introductions
- Track how a protocol's metrics evolve over time as the implementation is optimized by tracing and marking historical versions.
- Provide visualization for intuitive comparison points for framework developers, offering immediate insights into relative strengths and weaknesses along with an option to change data range.
- Identify performance variability and analyze code changes that led to fluctuations.
- Draw insights from the optimization trajectory - e.g. did shifting to a new polynomial commitment scheme cause a phase change in performance?
- Compare improvement velocity across frameworks to set realistic optimization targets.
- Pinpointing Issues: Look for sudden spikes or dips in the charts. These can be correlated with code changes to identify optimizations with unintended side effects or regressions.
- Run benchmarks on different hardware (CPU architectures, varying memory). This surfaces framework behavior on diverse systems, informing optimization paths for target deployment environments.
- Compare the computational and financial costs of generating and verifying proofs across different systems. This can include direct costs (like computational resources and energy consumption) and indirect costs (such as developer time and maintenance).
- Including power metrics like MMOPS/Watt and total cost of ownership analysis will provide valuable data on the energy and dollar costs of running the proving systems. Teams can compare their systems' efficiency against others and identify ways to optimize power hungry components. This is especially important for applications targeting everyday users where energy efficiency is critical.
- As quantum computing advances, analyzing the resilience of ZKP systems to potential quantum attacks becomes crucial. This forward-looking analysis can guide long-term strategic planning and investment in quantum-resistant cryptographic methods.
- By benchmarking at multiple levels of abstraction (high-level apps down to low-level primitives), uZumaKi can pinpoint specific components that are performance bottlenecks. For example, analysis might reveal that a protocol's choice of hash function or polynomial commitment scheme is limiting its prover times. Teams can use these insights to focus optimization efforts on the parts that matter most.
- Tracking developer experience metrics like ease-of-use, documentation quality etc. alongside performance will give teams a 360 view. Pain points reported by the community can be addressed to smooth adoption. Teams can also see how their UX compares to others and adapt best practices.