# EPF Week 2 Update

This week I focused on completing some of the foundational benchmarking infrastructure and conducting comprehensive performance analysis across SP1, RISC Zero, OpenVM, and Zisk. The week involved deep technical implementation work and revealed significant architectural differences that will shape the optimization strategy moving forward.

## Benchmarking Infrastructure

I examinded the cross-zkVM benchmarking suite with unified interfaces for all four systems. I realized that the implementation would require careful handling of each zkVM's unique compilation and proving workflows.

The most challenging aspect was normalizing memory management across systems. SP1's proving client abstracts much of this complexity, while RISC Zero requires explicit heap management for large beacon states. OpenVM's modular architecture allows fine-grained control over memory allocation per chip, and Zisk (despite being early-stage) shows promising memory efficiency patterns.

## OpenVM Deep Dive
OpenVM's modular no-CPU architecture proved particularly interesting for beacon chain operations. I experimented with custom chips for validator set operations and merkle tree computations. The key insight is that OpenVM's extensible ISA allows us to implement beacon-chain-specific operations as native instructions

- Validator State Transitions: Direct ISA support for validator activation/exit logic
- BLS Signature Aggregation: Utilizing OpenVM's BLS12-381 pairing operations
- Merkle Tree Operations: Custom instructions for beacon state tree manipulations

The continuation mechanism in OpenVM is particularly powerful. Unlike traditional zkVMs that must fit entire computations in memory, OpenVM can checkpoint and resume execution

pub struct ContinuationManager {
    checkpoint_interval: usize,
    current_validator_index: usize,
}

impl ContinuationManager {
    pub fn process_epoch_transition(&mut self, state: &BeaconState) -> Vec<ContinuationProof> {
        let mut proofs = Vec::new();
        
        for chunk in state.validators.chunks(self.checkpoint_interval) {
            let proof = self.prove_validator_chunk(chunk);
            proofs.push(proof);
            self.checkpoint_state();
        }
        
        proofs
    }
}

This approach enables processing of mainnet-scale validator sets by breaking the computation into manageable validator chunks.

## Zisk Analysis

There are several challenges given the fact that Zisk is still in its early development stage. There is limited standard library support as many rust std operations aren't yet supported. There is compilation toolchain instability which leads to frequent changes in the build process. There are some documentation gaps as well wherein some APIs are undocumented or examples are outdated. Nevertheless, I tried experimenting by using a simplified beacon state representation. The approach I took focused on proving state root transitions rather than full state processing, which aligns with Zisk's current capabilities.

## Performance Metrics

### Proving Time Analysis
The most significant finding is the non-linear proving time scaling across systems. SP1 shows the most predictable linear scaling, while RISC Zero exhibits performance cliffs at specific validator counts.
OpenVM's continuation-based approach maintains consistent proving times regardless of validator set size, but with higher constant factors due to proof aggregation overhead.

### Circuit Compilation Insights
Each zkVM handles Rust compilation differently:

- SP1: Standard Rust compilation with zkVM-specific optimizations
- RISC Zero: Custom RISC-V target with specialized syscalls
- OpenVM: Hybrid approach with native field operations compiled to custom ISA
- Zisk: RISC-V based but with significant standard library limitations

## Optimization Strategy Development
- Memory Layout Optimization: Struct-of-arrays vs array-of-structs for validator data
- Computation Partitioning: Breaking epoch transitions into parallelizable chunks
- State Representation: Exploring merkleized state representations vs full state processing

For OpenVM specifically, the modular architecture allows for beacon-chain-specific optimizations that aren't possible in other systems. The continuation support enables a fundamentally different approach to large-scale state processing.

## Resources
In the past week, I went through the following

- [OpenVM Whitepaper](https://openvm.dev/whitepaper.pdf)
- [User Book](https://book.openvm.dev/)
- [Zisk Docs](https://0xpolygonhermez.github.io/zisk/)
- [RISC Zero zkVM Blog](https://risczero.com/blog/risczero-zkvm-1.2)
- [SP1 Introduction](https://blog.succinct.xyz/introducing-sp1/)

## Conclusion
Next week, I plan to explore more the OpenVM custom chips for validator operations and BLS aggregation, complete memory layout optimization experiments across all zkVMs, and conduct end-to-end benchmarks. I will also focus on stabilizing the Zisk implementation by working around the standard library limitations and exploring alternative approaches for beacon state processing that align better with its current capabilities. The goal is to have a comprehensive performance comparison framework ready that can guide the selection of the optimal zkVM architecture for different aspects of beacon chain proving, while beginning preliminary work on the modular proving system design that can leverage the strengths of each platform. I am also eagerly waiting for the SP1 and RISC Zero results to go public in the Grandine repository.