---
# System prepended metadata

title: 'Week 10: From Ethereum''s Frontier to the Code''s Core'

---

# Week 10: From Ethereum's Frontier to the Code's Core

This week was all about bridging the gap between the highest-level applications of the `rust-kzg` library and the lowest-level implementation details that make it all possible. I focused entirely on the tutorial track, writing two of the most critical chapters in the entire series: one on the future of Ethereum's data scaling (PeerDAS), and another on the high-performance engine that powers it all (the BLST backend).

As always, you can find the new chapters and all project progress in the repository: [https://github.com/only4sim/rust-kzg-tutorial](https://github.com/only4sim/rust-kzg-tutorial).

## New Tutorial Content

I completed two cornerstone chapters that together provide a complete picture of how theory becomes practice in the world of KZG.

### Chapter 7: Data Availability Sampling (EIP-7594 DAS)

This chapter explains the "why" behind all this work: Ethereum's next major scaling upgrade, PeerDAS. It’s a practical guide to one of the most important applications of the `rust-kzg` library. The chapter covers:
*   **The Big Picture**: It explains the evolution from EIP-4844 to EIP-7594, detailing how PeerDAS solves the data bottleneck for validators.
*   **The Core Mechanics**: A breakdown of the key concepts, including how blobs are extended with Reed-Solomon encoding and split into "cells."
*   **Practical Implementation**: The chapter is full of code examples showing how to use the library's `DAS` trait to compute cells, generate proofs, recover missing data, and perform optimized batch verification.
*   **Real-World Challenges**: It also dives into performance optimization strategies (parallelism, memory pooling) and the complex network-level considerations for building a robust P2P sampling system.

### Chapter 8: A Deep Dive into the BLST Backend

If Chapter 7 was about the application, this chapter is all about the engine. It’s a deep explanation of the `BLST` backend, showing why it’s the recommended choice for production environments. This highly technical chapter explores:
*   **The Performance Edge**: It starts with benchmark data and explains how BLST achieves its speed through hand-written, assembly-level optimizations for modern CPUs.
*   **The Binding Layer**: A detailed walkthrough of the Rust code that safely and efficiently wraps the BLST C library, showing how the core `Fr` and `G1` traits are implemented.
*   **Algorithm Optimization**: An analysis of how key algorithms like FFT and MSM are implemented, including multi-threading with Rayon, the optimized Pippenger algorithm, and even hooks for GPU acceleration with `sppark`.
*   **Production-Ready Code**: The chapter covers crucial best practices for error handling, memory management, and writing robust, secure cryptographic code.

## Week 10 Achievements

This week's work connects the two most important aspects of the project:
*   **Completed Chapter 7 (EIP-7594 DAS):** A new [chapter](https://github.com/only4sim/rust-kzg-tutorial/blob/main/docs/chapter07_eip7594_das.md) is now available, providing a comprehensive guide to implementing Ethereum's Data Availability Sampling.
*   **Completed Chapter 8 (BLST Backend):** A second [new chapter](https://github.com/only4sim/rust-kzg-tutorial/blob/main/docs/chapter08_blst_backend.md) offers a deep dive into the high-performance BLST backend that makes DAS feasible.
*   **Forged a Critical Link:** Together, these chapters provide a complete, end-to-end understanding—from the Ethereum specification all the way down to the assembly code that executes it.

## Next Steps

With this major section of the tutorial now complete, the path forward is clear.
1.  **Tutorial Track**: The next logical step is to explore the **GPU acceleration** mentioned in Chapter 8. I will begin writing **Chapter 9**, which will be a hands-on guide to setting up and using `sppark` for even greater performance.
2.  **Research Track**: The deep dive into BLST's performance provides the perfect context to resume the optimization work. I will start by building the large-scale benchmark suite I planned in Week 9 to test these production-level workloads.

***

## References and Further Reading

1.  **EIP-7594: PeerDAS Specification**: The official Ethereum Improvement Proposal for Peer Data Availability Sampling. [https://github.com/ethereum/EIPs/blob/master/EIPS/eip-7594.md](https://github.com/ethereum/EIPs/blob/master/EIPS/eip-7594.md)
2.  **BLST GitHub Repository** (`supranational/blst`): The high-performance, assembly-optimized library that serves as the primary backend for `rust-kzg`. [https://github.com/supranational/blst](https://github.com/supranational/blst)
3.  **Reed-Solomon Codes (Wikipedia)**: For those interested in the data extension part of Data Availability Sampling, this article provides background on the error-correcting codes used. [https://en.wikipedia.org/wiki/Reed%E2%80%93Solomon_error_correction](https://en.wikipedia.org/wiki/Reed%E2%80%93Solomon_error_correction)
4.  **sppark: GPU Acceleration for MSM**: The library for GPU-accelerated Multi-Scalar Multiplication, which will be the focus of the next chapter. [https://github.com/supranational/sppark](https://github.com/supranational/sppark)