Understanding EVM Parallel Execution: A Deep Dive

Table of Contents

Parallel Execution has become a buzzword in the crypto world, sparking a new era of exploration into optimizing the Ethereum Virtual Machine (EVM) performance. This article dives into the evolution of parallel execution in the crypto space, the challenges faced in Ethereum's scaling journey, and Monad's innovative approach to scalability.

I. Overview:

A. Overview of Parallel Execution in Crypto:

Parallel execution has emerged as a pivotal concept in the dynamic realm of cryptocurrency, representing a transformative shift in how transactions are processed and the overall scalability of blockchain networks. As the crypto community continues to explore innovative solutions to enhance performance, parallel execution has become a cornerstone in addressing the limitations of traditional transaction processing methods.

The evolution of parallel execution within the crypto landscape is marked by its journey from relative obscurity to its current status as a widely-discussed and sought-after optimization strategy. What was once an uncharted territory in scaling strategies has now become a focal point for development teams seeking to unlock new levels of efficiency in their blockchain ecosystems.

B. Evolution of EVM Performance Optimization:

The pursuit of optimizing the Ethereum Virtual Machine (EVM) performance has been an ongoing endeavor, driven by the imperative to bolster transaction throughput and support a growing user base. Over 1.5 years ago, the Monad team embarked on a mission to redefine the boundaries of EVM capabilities, recognizing that the traditional methods of transaction execution were no longer sufficient to meet the demands of a rapidly expanding crypto ecosystem.

This evolutionary process in EVM performance optimization reflects the industry's commitment to overcoming challenges and pushing the boundaries of what is achievable. The quest for higher transaction speeds, greater scalability, and improved user experiences has led to the exploration of parallel execution as a key strategy in the ongoing evolution of blockchain technology.

C. Challenges in Ethereum's Scaling Journey:

Despite Ethereum's undisputed leadership in terms of user adoption, developer networks, and Total Value Locked (TVL) metrics, the limitations in transaction processing speed on the Ethereum Mainnet were evident. Processing only around 14 transactions per second (TPS), Ethereum faced a crucial bottleneck that needed to be addressed for the blockchain to sustain its growth and accommodate a global user base.

The challenges encountered in Ethereum's scaling journey prompted a reevaluation of traditional approaches to transaction execution. The recognition that simply increasing TPS through conventional means would not be sufficient sparked a renewed interest in parallel execution, setting the stage for a paradigm shift in how blockchain networks approach scalability.

In exploring the evolution of parallel execution, the crypto community has embarked on a journey that not only addresses the current challenges but also lays the groundwork for a more robust and scalable future for blockchain technology. As we navigate the landscape of parallel execution, it becomes increasingly clear that the fusion of ambition, innovation, and strategic decision-making is crucial in shaping the trajectory of the crypto industry.

II. Monad's Approach to Scalability:

A. Founding of Monad Labs and Ambitious Vision:

The inception of Monad Labs marked a pivotal moment in the pursuit of scalability within the crypto space. Over 1.5 years ago, Keone Hon and James Hunsaker, the visionary minds behind Monad, embarked on a mission to revolutionize the Ethereum Virtual Machine (EVM) and catapult it into an era of unprecedented efficiency. This founding moment was not merely a creation of another blockchain project; it was the establishment of a paradigm-shifting entity driven by an ambitious vision.

Monad Labs' vision transcends the conventional boundaries of blockchain technology. With a commitment to optimizing the EVM for global-scale applications, the founding members set out to create a platform capable of handling tens of thousands of transactions per second, serving millions, if not billions, of users. Their foresight recognized the need for a scalable infrastructure that could support a diverse range of applications, from personal finance to cutting-edge social platforms, central limit order books, and everything in between.

B. Choosing the "Hard Road" for Optimization:

The decision to embark on what Keone Hon and James Hunsaker fondly refer to as the "hard road" reflects the uncompromising commitment to achieving optimal scalability. Faced with the choice between preserving the decentralized ethos of Ethereum or taking a more scalable yet challenging route, Monad Labs embraced the latter. This strategic decision involved not only rethinking the traditional transaction execution methods but also reimagining the very core of the Ethereum VM.

In opting for the "hard road," Monad Labs set out to build more than just a transaction processing engine; they aspired to construct an optimized system from the ground up. This entailed going beyond superficial optimizations and delving into the intricate details of the Ethereum stack. The founders recognized that true scalability could only be achieved through a comprehensive overhaul of the system, akin to building a high-performance racecar engine rather than settling for incremental improvements.

In the interview, Keone delves into the "why" behind choosing the challenging path and elucidates on the potential implications for the broader crypto industry. This dialogue not only humanizes the ambitious project but also brings forth the depth of understanding and expertise that the Monad team possesses. Keone's insights serve as a bridge, connecting the technical intricacies of blockchain optimization with the broader vision of a scalable and inclusive crypto future.

III. Understanding the Bottleneck: State Access:

A. Importance of State in EVM:

To comprehend the intricacies of the bottleneck in Ethereum's scaling journey, it is crucial to delve into the pivotal role of state within the Ethereum Virtual Machine (EVM). The term "state" encapsulates the dynamic snapshot of the system at any given moment, encompassing essential components such as native token balances and contract-specific states. For instance, when a user deposits USDC into a lending protocol like Aave, the state of the USDC contract, Aave contract, and the user's native token balance undergo updates.

State, in essence, is the heartbeat of the EVM, reflecting the ever-evolving landscape of smart contracts and user interactions. Executing a transaction involves not only computational processes but also the dynamic interaction with this intricate web of states. This transactional heartbeat, however, presents a dual challenge—it demands both CPU-intensive computations and disk Input/Output (I/O) operations, as states are read, processed, and updated.

B. Limitations of General-Purpose Databases:

In Ethereum and other EVM-compatible blockchains, state data is traditionally stored in general-purpose databases like LevelDB or PebbleDB. While these databases offer convenience and are widely used, they grapple with a fundamental challenge—they lack support for asynchronous Input/Output (IO). As a result, even with the implementation of parallel execution, the multithreaded read and write requests to the database become a bottleneck, hindering the seamless parallel access that is critical for optimal performance.

The state, stored in these databases, becomes a point of contention as multiple transactions seek to access or modify it concurrently. Without the ability to handle asynchronous IO, these databases impede the potential gains from parallel execution, leading to only marginal improvements in performance. The limitations in state access emerge as a significant roadblock in achieving the scalability goals set by projects seeking to enhance transaction processing speed.

C. Monad's Research Phase and Key Findings:

The Monad team, during the research phase, undertook a comprehensive exploration of optimizations and algorithmic enhancements for the EVM. It quickly became evident that merely focusing on parallel execution would not yield the substantial performance improvements desired. The crux of the issue lay in state access—the bottleneck that hampered the scalability aspirations.

Monad's research journey unveiled a critical insight: state access in EVM required a paradigm shift. The need to move beyond the constraints of existing databases became apparent. This realization set the stage for Monad Labs to make a strategic decision—one that not only acknowledged the challenges but also paved the way for groundbreaking solutions. Understanding the intricacies of state access was the catalyst for Monad's pursuit of a custom state backend—a pioneering step toward overcoming the fundamental bottleneck in EVM scalability.

IV. Monad's Innovative Solutions:

Source: Monad docs

A. Choosing the Hard Road: Custom State Backend:

In the quest for scalability, Monad Labs faced a pivotal decision: to either opt for the easier path of keeping all state in memory (RAM) or to tread the more challenging but ultimately scalable route of building a custom state backend. The easier road, while offering the advantage of reduced latency, came at a significant cost—exponentially increasing RAM requirements for running a node. This approach, reminiscent of Solana's strategy requiring over 200 GB of RAM, contradicted Ethereum's core decentralization ethos, posing potential barriers for broader network participation.

Monad Labs, however, embraced the "hard road" — a decision grounded in the understanding that a scalable solution necessitated more than incremental adjustments. The team recognized that achieving optimal scalability without compromising on decentralization required redefining the foundational architecture of the Ethereum stack. The chosen path involved building a custom state backend, an innovative database solution that would introduce efficiency in SSD storage and crucially, support Asynchronous IO.

This strategic move showcased Monad's commitment to addressing not only the immediate challenges but also the long-term sustainability of blockchain networks. By opting for a custom state backend, Monad Labs demonstrated a profound understanding of the nuanced trade-offs between ease of implementation and the fundamental scalability required for the EVM to support applications on a global scale.

B. Core Features of Monad DB:

1. Asynchronous IO Support:

Central to Monad's approach in overcoming the state access bottleneck is the integration of asynchronous Input/Output (IO) support in the custom Monad Database (Monad DB). Unlike traditional general-purpose databases, Monad DB allows for parallel access to state by enabling asynchronous IO. This breakthrough feature ensures that read and write requests can occur simultaneously, mitigating the blocking issues prevalent in conventional databases. As a result, the transaction processing pipeline experiences a significant boost in efficiency, setting the stage for genuine scalability.

2. Parallel Execution:

In tandem with asynchronous IO support, Monad DB harnesses the power of parallel execution. This feature empowers multiple Virtual Machines (VMs) to run in parallel, seamlessly accessing and processing state concurrently. The synergy between parallel execution and asynchronous IO transforms Monad's solution from a mere performance enhancement to a comprehensive optimization strategy, addressing the multifaceted challenges of EVM scalability.

3. Deferred Execution:

Monad DB introduces the concept of deferred execution, offering a nuanced approach to managing complex transactions. By allowing certain actions within a transaction to be executed at a later stage, Monad mitigates bottlenecks and optimizes the processing pipeline. This innovative feature ensures that the system maintains efficiency even during periods of heightened transactional activity, providing a dynamic and adaptive approach to transaction processing.

4. High-Performance Consensus Mechanism:

Monad's commitment to scalability extends beyond the database layer, encompassing a high-performance consensus mechanism. The integration of a consensus mechanism tailored for optimal performance complements the innovations within Monad DB, creating a holistic ecosystem that strives for unparalleled transaction processing speed without compromising on security and decentralization.

Collectively, these core features of Monad DB constitute a sophisticated suite of innovations, each addressing a specific facet of the challenges inherent in scaling the Ethereum Virtual Machine. The integration of asynchronous IO support, parallel execution, deferred execution, and a high-performance consensus mechanism underscores Monad Labs' holistic approach toward fundamentally optimizing the EVM at scale.

Users who choose to run Monad nodes are poised to experience the amalgamation of these features, reaping the benefits of a blockchain that not only delivers state efficiently but does so without imposing exorbitant RAM requirements. Monad's pioneering solutions underscore that true scalability necessitates a multifaceted strategy, with each feature contributing to the fundamental optimization of the EVM, ensuring a seamless and efficient processing pipeline for applications on a global scale.

Conclusion:

In conclusion, Monad's journey in parallel EVM optimization provides a glimpse into the future of scalable blockchain solutions. The article has shed light on the challenges, the hard road chosen for optimization, and the innovative solutions that Monad brings to the table. As the crypto community embraces the concept of parallel execution, Monad stands as a trailblazer in redefining the landscape of EVM scalability.

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