How execution-layer innovations are shaping blockchain

While blockchain is almost always talked about in terms of decentralization and transparency, the execution part of it is what actually makes it usable. It’s the part of the blockchain that processes transactions, runs contracts, and carries out other user-oriented activities. It determines the time your payment takes to go through or if your dApp responds smoothly.

However, as the decentralized ecosystem today supports millions of users, developers need a way to make networks more scalable without significantly increasing hardware demands, something that threatens decentralization. This has led to a lot of innovation that is now revolutionizing the future of execution and ensuring fast, cheap transactions.

What execution layers do in a blockchain

The execution layer of the blockchain is essentially the engine because it’s where transactions actually get processed and where smart contracts run. It’s still part of the same network in the traditional blockchain design, but it’s the first layer of interaction before others like securing the network and maintaining consensus. 

In practical terms, it handles the day-to-day operations of the blockchain that are responsible for functionality and responsiveness. This means that it also determines how fast transactions are processed, how efficiently a contract executes, and, quite crucially, how the network can handle growing demand.

Key innovations in execution layers

Blockchains were originally designed to offer decentralization and security, not speed. That was okay back then, but the growth of web3 has significantly changed how the blockchain is used and made speed quite crucial.

Scaling the original architecture to improve efficiency meant increasing the hardware demand and the cut-off for validators, but this sacrificed decentralization as only a few operators could support the demand.

This led to the design of several execution-layer innovations that are meant to ease the pressure and solve this blockchain trilemma. 

Layered or modular execution

A layered or modular execution blockchain designs take the various responsibilities of a blockchain and splits them. In most cases, it takes the execution part and gives it to another blockchain, known as a Layer 2 network. It’s independent, but it’s still anchored on the main chain.

These Layer 2 chains are designed for speed and can easily scale without affecting the main chain, which allows them to process large numbers of transactions quickly. They then submit cryptographic proofs or compressed summaries to the base layer, which verifies and records them.

This is the architecture mostly used on Ethereum’s dApps as it reduces congestion but still relies on the security and decentralization of the main chain.

Parallel transaction processing

Traditional blockchains like Bitcoin and Ethereum usually process transactions sequentially, one after another. In this model, every validator is involved in processing every transaction. This approach significantly limits throughput as the system can be held back by a slow sequence.

Parallel execution changes that model by processing transactions simultaneously. It still respects the need for sequential transactions, so it first identifies transactions that don’t depend on each other then processes them at the same time. 

This method allows more efficient use of computational resources, and it’s the approach used by high-throughput networks like Solana.

Optimized virtual machines and transaction engines

Modern blockchains use a virtual machine (VM) at the execution layer, and it’s the one responsible for interpreting smart contract code. It determines how instructions are executed, how the state is updated, and how the computational methods are measured. And if it’s not efficient, executing contracts starts taking more time, memory, and network resources than it needs to.

Developers are now optimizing VMs like the Ethereum Virtual Machine (EVM) by focusing on how instructions are processed, state is accessed, and fees are calculated. Things like reducing redundant computations and streamlining how opcodes are executed help improve instruction efficiency, while reducing unnecessary storage reads and writes helps stabilize latency and fees.

To accomplish this, some networks entirely redesign their EVMs. Others do it incrementally. But in both cases, the computational efficiency per validator increases without raising hardware barriers.