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# System prepended metadata

title: 'Dev Update Week 1: zkVM Performance Research'

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# Dev Update Week 1: zkVM Performance Research

**Developer:** Developeruche
**Week Ending:** June 20, 2025

### Summary
This week, my focus shifted from framework development to a deep-dive investigation into a critical bottleneck affecting the entire zkVM ecosystem: trace generation. As tasked by my mentor, I explored an innovative solution by the ZisK team that dramatically accelerates this process. The primary objective was to understand the fundamentals of their approach and assess its potential for broader adoption across different zkVMs.

### Accomplishments This Week
* **Research & Analysis:** I conducted a thorough analysis of the recent breakthrough by the ZisK team, who achieved a 1.5GHz trace generation speed for their RISC-V zkVM. This represents a 10x performance increase compared to other existing zkVMs, marking a significant milestone for the field.

* **Understanding the Innovation:** I identified the core technique behind this leap: the use of ahead-of-time (AOT) compilation to translate RISC-V instructions into highly optimized, custom x86 assembly. This method sidesteps the traditional, slower interpretation loop, enabling near-native execution speeds.

* **Technical Teardown:** I broke down the ZisK implementation, noting that it compiles each RISC-V instruction into just 3-4 x86 instructions, with a couple of additional instructions for segmenting the trace. This efficiency is the key to its high-speed performance.

* **Performance Benchmarking:** I reviewed the performance metrics from the ZisK team's tests. On a $3,000 gaming workstation (AMD Ryzen 9 9950X3D), their emulator processed Ethereum block 21,926,929 (36M gas) and generated 1.5GB of trace data in only 0.5 seconds. This highlights the massive potential for reducing the hardware requirements for real-time proving.

* **Initial Content Creation:** I authored an article titled "Breakthrough Emulation Bottleneck in zkVM" to share these findings. The article explains the core problem, details traditional optimization methods, and sets the stage for a deeper dive into ZisK's specific solution.

### Next Steps & Goals for Next Week
With a solid foundational understanding, the focus will now turn to the practical application and generalization of these concepts.

1.  **Explore the ZisK Codebase:** I will move from a high-level analysis to a hands-on review of the ZisK team's open-source implementation to understand its architecture and nuances.
2.  **Feasibility Study for Generalization:** My main goal is to begin formulating a strategy on how the principles of AOT-based trace generation can be adapted and made generic for other zkVMs to adopt.
3.  **Continue Technical Writing:** I will write the follow-up article that provides a detailed technical walkthrough of ZisK's AOT compiler and emulator, as promised in the initial post.
4.  **Proof-of-Concept Scoping:** I will start outlining a small-scale proof-of-concept to test the core ideas of this approach in a controlled, isolated environment.

### Challenges & Learnings
* **The AOT vs. JIT Trade-off:** A key learning was understanding precisely why dynamic recompilation (JIT), while powerful, is unsuitable for zkVMs. Its tendency to reorder or optimize away instructions breaks the semantic link between the bytecode and the execution trace, making the proof invalid. This solidified my understanding of why ZisK's AOT approach is so fitting.
* **Hardware and Performance:** This week provided a much clearer insight into the direct impact of single-threaded CPU performance and memory speed on proving latency. It's evident that hardware advancements, when combined with software innovation, can make real-time proving an attainable reality.
* **Assembly-Level Optimization:** The ZisK team's work is a powerful reminder of the performance gains achievable through low-level, architecture-specific optimizations. Understanding their approach requires a deeper appreciation for both RISC-V and x86 instruction sets.

I wrote an article exploring other methods that could be explored to reduce trace generation time;

Link: [here](https://hackmd.io/@0xdeveloperuche/r1oOhdWNel).