In today’s ever-evolving coding landscape, where efficiency, performance and sustainability are paramount, the impact of inefficient code on CPU utilization is a constant debate among software developers.
Businesses are constantly looking for new ways to be more sustainable and optimize their compute power usage. With the increased focus being placed on green coding and streamlining compute power to help contribute towards meeting sustainability targets, software developers are now at a crossroads – how do they make their code more efficient and sustainable, without impacting the desired outcome?
Senior Director for Architecture at SS&C Blue Prism.
Balancing CPU power
Every line of code needs to contribute to a business’s software development since all lines of code that are executed consume valuable computational resources, so when code is not optimized, this leads to excessive CPU cycles, increasing operational costs and wasted compute power.
From a code-writing perspective, developers must ensure they are writing code that is efficient and not wasteful from day one. Outdated methods and inefficient coding often lead to wasteful code, and ultimately, wasteful code ends up wasting compute power on unnecessary tasks.
One way to balance your compute power usage is by ensuring developers are applying lean coding practices to ensure that they use compute power effectively and efficiently.
The shift to public cloud ‘hyperscalers’ such as AWS allows companies to take advantage of ‘elastic scale’ – only paying for the compute power you actually need, rather than buying hardware that may be largely under-utilized – not to mention the associated expense and environmental impact of keeping that hardware cool. This enhances efficiency, cuts operational costs, and allows more sustainable code execution.
The hardware and virtual resources selected when designing cloud deployments also have implications on energy use. Cloud platform vendors are moving to energy-saving technologies to reduce costs and align with their own sustainability goals, making new choices available to businesses 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 focus within team
Modern cloud software development has moved on a long way from a time when developers would deploy code to bare metal servers hosted on-premises, where the impact of inefficient application logic, suboptimal database queries and excessive network calls on energy use 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 of hosting costs and energy use.
In the pursuit of sustainable software development, leveraging code observability to gain insights into performance can be a pivotal strategy. This approach involves implementing tools and practices to gain deep insights into the performance and behavior of software applications.
By harnessing 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 energy consumption and enhanced sustainability.
The more data that can be extrapolated about a business’s coding practices, the better. Businesses can then make data-driven decisions to rationalize their consumption, reduce their cloud allocation and reallocate workloads to optimize their cloud usage across the business.
Code observability serves as a cornerstone in the pursuit of more resource-efficient and environmentally conscious software solutions. These increased insights provide software development teams with the necessary insight to consistently evolve and adapt to new coding best practices, to help keep their code green and sustainable.
The most important part of the journey to green code is to build a culture of performance enhancement, and this requires collaboration across many teams. By empowering everyone to take ownership of performance makes it a foundational aspect of day-to-day work – positioning it as important as quality and security.
The evolution of programming languages
The prospect of refactoring a business’s code into a different programming language presents an alternative avenue for improvement, but is it necessary?
Developers may question whether transitioning to a different programming language could significantly enhance performance and reduce resource costs, but there is a practical discussion of whether businesses will see a return on investment and reap the savings benefits. Software development teams are constantly looking at new applications to improve efficiency. A decision to transition to a new language hinges on the existing codebases’ shortcomings, the benefits of adopting a more suitable language and whether the development teams are adept at the new coding language.
While low-level languages such as C and C++ are traditionally 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 area than more popular, higher-level languages like Python or JavaScript. Both Python and JavaScript are relatively inefficient as they are interpreted languages, rather than compiled – however advances in modern CPU technology may make any perceived advantages invalid. Therefore, a decision to switch to a more efficient coding language has multiple factors, including the possibility of employing new software developers who are experienced in the new language.
Overall, the pursuit of efficient and sustainable software development demands a multifaceted approach that encompasses code observability, strategic resource allocation, and consideration of programming languages.
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