Over the last 50 years, the world has witnessed a remarkable growth in computing power. Moore's Law, which stipulates that computing power doubles roughly every two years, has held true for several decades. This incredible progress has allowed software engineers to create and employ higher-level abstractions, leading to a trade-off between efficiency and ease of development. However, with the emergence of distributed blockchains, we now face a new era where computation efficiency is crucial once again. This article explores the full circle journey of computation efficiency, from the rapid advances of Moore's Law to the resource-constrained runtime environments of distributed blockchains.
The Early Days of Programming and the Importance of Computation Efficiency
In the early days of programming, computation efficiency was of paramount importance due to the limited capabilities of the hardware available at the time. Computers were large, expensive, and had restricted processing power, memory, and storage capacity. As a result, developers had to be highly conscious of the resources they consumed while writing their programs. They often employed assembly languages and other low-level programming techniques to ensure that their code ran as efficiently as possible. Optimizing code for efficient execution was crucial not only to fit within the constraints of the hardware, but also to maximize the value of these costly machines. This focus on computation efficiency laid the foundation for the development of algorithms, data structures, and other optimization techniques that continue to be relevant in today's programming landscape.
Moore's Law and the Rise of High-Level Abstractions
As predicted by Gordon Moore in 1965, the number of transistors on a microprocessor has doubled approximately every two years. This exponential growth has led to significant advancements in processing power, memory, and storage. Consequently, software engineers have been able to develop more complex and powerful applications using high-level programming languages and abstraction layers.
These high-level abstractions, such as object-oriented programming and virtual machines, have greatly simplified software development. They have allowed developers to focus on functionality and user experience rather than low-level performance details. While this has resulted in significant progress in software capabilities, it has also led to a decrease in computation efficiency. As processing power continued to grow, the trade-off seemed reasonable; however, with the introduction of distributed blockchain technology, this balance has shifted.
Distributed Blockchains and the Need for Computation Efficiency
Distributed blockchains, like Ethereum and others, have introduced a new runtime environment where each unit of computation has an associated cost. In these systems, participants must pay a fee to execute their code on a decentralized network of computers. This fee, usually paid in cryptocurrency, is determined by the complexity and resource requirements of the code being executed.
This new paradigm has created a strong incentive for developers to optimize their code for efficiency. Writing complex, convoluted code on a blockchain not only increases execution time but also results in higher fees. In a world where efficiency is directly tied to cost, developers must pay closer attention to low-level performance details.
The Return to Low-Level Performance Optimization
To meet the demands of distributed blockchains, software engineers must revisit the principles of low-level performance optimization. Techniques such as algorithmic optimization, efficient data structures, and careful memory management are becoming increasingly important. The focus is shifting from abstract, high-level programming paradigms to a more pragmatic approach that emphasizes the efficient use of resources.
Moreover, developers must balance the need for computational efficiency with security, as many blockchain applications involve sensitive data and financial transactions. This requires a combination of expertise in both performance optimization and secure programming practices.
The computing landscape has come full circle, from the rapid advancements of Moore's Law and the rise of high-level abstractions to the resource-constrained runtime environments of distributed blockchains. As developers grapple with the challenges of optimizing code for efficiency and cost-effectiveness, we may see a resurgence in the importance of low-level performance optimization. The era of taking computational resources for granted is over; today's software engineers must once again prioritize efficiency as they navigate the world of distributed blockchain technology.
“This will be the first of many articles I will write with the help from OpenAI software such as ChatGPT. I believe most articles you will read anywhere going forward will be co-authored by an AI. The art of literacy no longer resides with the eloquent and articulate author, but rather the author with ingenuity and imagination.
I would like to set a standard for publishers that utilize AI for their articles, to show full transparency to their readers by including the AI as a co-author and explaining the idea behind the article and which prompts to feed the AI.
The idea for this article stemmed from a 2-hour conversation I had with one of my smartest friends regarding Solidity programming on Ethereum. I had recently introduced him to one of my traditional programming colleges at work and my friend found it difficult communicating and explaining his thinking process when writing code for blockchains. After a while we realized it had to do with the difference in detail towards computation efficiency. Being the hobby software historians that we are we made the full circle connection from blockchain programming back to assembly code and how Moore’s law has let programmers “slack off” in regards towards writing code that is computational efficient. I thought this would make for a great article and could not find anything similar online. I therefore went ahead and prompted ChatGPT to write me an article based on our conversation and coding constraint epiphany.
“Write an article about how computation efficiency has come full circle in regard to:
- The early days of programming, computation efficiency was important due to hardware constraints.
- Over the last 50 years, computing power has doubled roughly every 2nd year (Moore's law).
- This has made up for and enabled software engineers to use higher level abstractions that results in less efficient computations.
- With the new resource constrained runtime environment of distributed blockchains where each unit of computation has an associated cost, software engineers must once again pay attention to low level performance details to ensure that their code isn't overly convoluted or complex.”