The Solution: Staging Compilation and Decomposition

To overcome these challenges, researchers from ZeroSync and Alpen Labs developed an innovative strategy called staging compilation. This approach tackles the size and complexity issues by breaking down the problem into manageable subtasks. Let’s go through the main steps in this staging compilation process:

1. Layout Process (Fitting the Elephant into the Car Shape)

   • Imagine the original Rust-style program as a computational graph, which we’ll call G. This graph represents the flow of operations and data in the program.

   • The layout process rearranges this graph and transforms it into an equivalent graph G', which is optimized to work within Bitcoin’s constraints. The goal of this rearrangement is twofold:

     • Data and Subgraph Reuse: This step aims to identify parts of the graph that repeat or can be shared (reduce redundancy), compressing the program to make it smaller.

     • Fit within Bitcoin’s 4MB Block Size: By reshuffling the graph, layout creates small “chunks” of code that fit within Bitcoin’s block limits, ensuring deployability.

   • The result is a compressed version of the original program that’s been reorganized to fit Bitcoin’s size constraints.

   • Analogy: This process is similar to disassembling a large object into smaller, modular pieces that can fit into a series of containers. It's like dismantling the elephant and fitting its parts into several cars, ensuring each piece is within the size limit.

2. Optimization (Making the Elephant Thinner)

   • Now that we have our program broken down into manageable parts, the next step is to make each part as efficient as possible. Think of this step as “slimming down” each chunk to make it less resource-intensive.

   • Optimization here means rewriting parts of the computation to reduce the number of operations needed. This is similar to simplifying algebraic expressions in math class. For instance, if the program involves multiple multiplications, optimization might find ways to reduce them, drawing on cryptographic insights and tricks.

   • By minimizing the number of costly operations, optimization ensures each part of the program is not only smaller but also faster.

   • Analogy: This is akin to trimming excess weight from each part of the elephant, making them lighter and easier to transport.

3. Code Generation (Getting the Elephant on Board)

   • Finally, once the layout and optimization steps are complete, we need to convert our compacted graph G' into actual Bitcoin script code.

   • This step involves an algorithm that traverses every node in G', converting each optimized part into a sequence of low-level Bitcoin instructions.

   • The end result is a compact, Bitcoin-compatible version of the program that’s ready to be deployed on the Bitcoin blockchain.

   • Analogy: This step is like carefully loading the trimmed and disassembled parts of the elephant into the vehicles, ready for transport.

Together, these steps allow BitVM to take a program that was initially impractical and break it down until it’s manageable within Bitcoin’s space limitations.