Unraveling code inefficiencies to optimize the CPU
In today’s ever-changing coding landscape, where efficiency, performance, and sustainability are of utmost importance, the impact of inefficient code on CPU usage is an ongoing debate among software developers.
Businesses are constantly looking for new ways to be more sustainable and optimize their computing power usage. With the increasing focus on green coding and streamlining computing power to help achieve sustainability goals, software developers are now at a crossroads: how do they make their code more efficient and sustainable, without affecting the desired outcome?
Senior Director of Architecture at SS&C Blue Prism.
Balancing CPU power
Every line of code must contribute to a company’s software development, as all code that is executed consumes valuable computing resources. When code is not optimized, it leads to excessive CPU cycles, higher operational costs, and wasted computing power.
From a code writing perspective, developers need to ensure that they are writing efficient and non-wasteful code from day one. Outdated methods and inefficient coding often lead to wasteful code, and ultimately wasteful code leads to wasting computational resources on unnecessary tasks.
One way to balance the use of compute is to ensure that developers adopt lean coding practices, which ensures that they use compute effectively and efficiently.
The move to public cloud “hyperscalers” like AWS allows enterprises to take advantage of “elastic scale” – paying only for the compute power you actually need, rather than buying hardware that may be largely underutilized – not to mention the associated cost and environmental impact of keeping that hardware cool. This improves efficiency, reduces operational costs, and enables more sustainable code execution.
The hardware and virtual resources selected when designing cloud deployments also impact energy consumption. Cloud platform vendors are moving to energy-saving technologies to reduce costs and align with their own sustainability goals, opening up new choices for companies hosting their software in the cloud. For example, Amazon’s new Graviton processor-based AWS virtual machine (EC2) instances use up to 60% less energy than comparable EC2 instances (ARM processor – AWS Graviton processor – AWS (amazon.com)).
Leveraging observability and a culture of performance-driven focus within the team
Modern cloud software development has come a long way from the days when developers deployed code to bare metal servers hosted on-premises, where the impact of inefficient application logic, suboptimal database queries, and excessive network calls on energy consumption would not be a consideration. In a cloud-hosted solution with a larger user base, the impact of inefficient design and implementation is measurable and highly visible in terms of the impact on hosting costs and energy consumption.
In the pursuit of sustainable software development, leveraging code observability to gain insight into performance can be a crucial strategy. This approach involves implementing tools and practices to gain deep insight into the performance and behavior of software applications.
By leveraging observability, developers can effectively monitor how inefficient code impacts CPU utilization and overall resource consumption. This not only facilitates the identification of performance bottlenecks, but also enables proactive measures to optimize code for reduced power consumption and improved durability.
The more data that can be extrapolated about a company’s coding practices, the better. Companies can then make data-driven decisions to rationalize their consumption, reduce their cloud allocation, and reallocate workloads to optimize their cloud usage across the entire company.
Code observability serves as a cornerstone in the drive towards more resource-efficient and environmentally conscious software solutions. This increased visibility provides software development teams with the necessary insight to consistently evolve and adapt to new coding best practices, keeping their code green and sustainable.
The most important part of the green code journey is building a culture of performance improvement, and this requires collaboration between many teams. By empowering everyone to take ownership of performance, it becomes a fundamental aspect of daily work – and positions it as important as quality and security.
The evolution of programming languages
The idea of refactoring a company’s code to another programming language offers an alternative way to make improvements, but is it necessary?
Developers may wonder whether switching to a different programming language can significantly improve performance and reduce resource costs, but there is a practical debate about whether companies will see a return on investment and reap the savings benefits. Software development teams are constantly looking for new applications to improve efficiency. A decision to switch to a new language depends on the shortcomings of existing code bases, the benefits of adopting a more suitable language, and whether development teams are proficient in the new programming language.
While low-level languages like C and C++ have traditionally been the most efficient, recent trends in performance optimization have seen languages like Go, Rust, and Zig emerge as new options in this space. However, there are fewer skilled developers in this space than more popular, higher-level languages like Python or JavaScript. Both Python and JavaScript are relatively inefficient because they are interpreted languages rather than compiled languages. However, advances in modern CPU technology can negate any perceived benefits. Therefore, the decision to switch to a more efficient programming language will depend on multiple factors, including the ability to hire new software developers who are experienced in the new language.
In general, the pursuit of efficient and sustainable software development requires a multifaceted approach that includes code observation, strategic resource allocation, and attention to programming languages.
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