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HybridVM Performance Optimization Proposal

The RISC-V emulator (rvemu) is 20-190x slower than necessary, creating a critical performance bottleneck. This is caused by a pure interpretation model, massive per-instruction overhead, and severe memory system inefficiencies. This document outlines a 3-phase optimization strategy to address these issues, targeting a 50-200x cumulative performance improvement over 3-6 months. The plan prioritizes immediate, low-risk fixes while building towards a long-term, high-performance architecture using a Just-In-Time (JIT) compiler.

1. Root Cause Analysis: Key Bottlenecks

  • Massive Per-Instruction Overhead (Causes ~70% of slowdown):
    • Pure Interpretation: Every instruction is fetched, decoded, and executed via slow match statements without caching.
    • Constant System Checks: Interrupts and device timers are checked on every single instruction, adding 50-80 cycles of useless overhead when they are only needed every ~1000+ cycles.
  • Inefficient Memory & Integration (Causes ~30% of slowdown):
    • Slow Memory Access: All memory operations are performed byte-by-byte with manual bit-shifting instead of fast, native functions, making them 3-5x slower than necessary.
    • No Address Caching (TLB): Every memory access triggers a full, multi-level page table walk, adding 20-100 cycles of latency.
    • Costly EVM Integration: A new emulator is created and destroyed for every contract call, and data is passed via slow serialization (bincode) instead of direct memory access.

2. Proposed 3-Phase Optimization Strategy

This plan is designed to deliver incremental value, starting with the highest-impact, lowest-risk changes.

Phase 1: Critical Quick Fixes (Timeline: 1-2 Weeks | Expected Speedup: 15-20x)

This phase targets the most severe overheads with minimal code changes.

  1. Reduce Interrupt Check Frequency: Change interrupt and device checks from per-instruction to once every ~1,000 cycles. (Est. 3x speedup)
  2. Optimize Memory Access: Replace manual, byte-by-byte memory operations with native Rust functions (e.g., u64::from_le_bytes). (Est. 3x speedup)
  3. Implement Emulator Pooling: Create a thread-safe pool of emulator instances to eliminate the costly setup/teardown for every contract call. (Est. 5x speedup for repeated calls)
  4. Add Translation Fast Path: Bypass the full page table walk when paging is disabled (the common case). (Est. 2x speedup)
  5. Eliminate Debug Overhead: Remove all performance-counting and debug hooks from release builds using conditional compilation. (Est. 1.2x speedup)
Phase 2: Architectural Improvements (Timeline: 1-2 Months | Expected Speedup: 5-10x additional)

This phase builds the foundational caching layers needed for high-performance emulation.

  1. Implement a Translation Lookaside Buffer (TLB): Introduce a 256-entry cache for virtual-to-physical address translations to avoid expensive page table walks.
  2. Build an Instruction Cache: Cache decoded instructions to eliminate redundant decoding work, especially in loops.
  3. Develop a Zero-Copy Syscall Interface: Replace slow bincode serialization with a shared memory interface for passing data between the host and emulator, drastically reducing syscall overhead.
Phase 3: Advanced Optimizations (Timeline: 3-6 Months | Expected Speedup: 10-50x additional)

This is the final phase to achieve near-native performance for hot paths.

  1. Implement a Basic Block JIT Compiler: Use a mature framework like Cranelift to identify and compile hot-running blocks of RISC-V code directly into native machine code at runtime. This eliminates interpretation overhead for the most frequently executed code.

3. Benchmarking & Validation

Success will be measured against a comprehensive benchmarking framework.

  • Microbenchmarks: Isolate specific workloads (e.g., recursive Fibonacci for branches, matrix multiplication for memory access) to validate the impact of each optimization.
  • Macrobenchmarks: Use real-world EVM contracts to measure end-to-end performance gains in gas-per-second and contract calls-per-second.
  • Tooling: Continuously profile using perf, flamegraph, and valgrind to identify new bottlenecks and ensure no performance regressions are introduced.