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Abstract

="a5ce">5. Use of Hardware Acceleration: Some tasks can be offloaded to specialized hardware (like the GPU for graphical tasks or calculations that can be done in parallel). This can be more energy-efficient than performing these tasks on the CPU.6. Code Optimization: Compiler optimizations can improve the performance and energy efficiency of a program. These optimizations can include things like eliminating unnecessary computations, reducing memory accesses, and more. However, these are often handled by the compiler rather than the programmer.7. Resource Management: Proper management of system resources such as CPU, memory, network, and disk can also influence energy consumption. This includes properly closing resources when they’re not in use and efficiently managing threads and processes.We have outlined the factors affecting a programming language’s energy efficiency. Now, let’s delve into why energy consumption varies across different languages.<figure id="536c"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/0*907gjqJDBWKDq-dd"><figcaption>Photo by <a href="https://unsplash.com/@chrisliverani?utm_source=medium&utm_medium=referral">Chris Liverani</a> on <a href="https://unsplash.com?utm_source=medium&utm_medium=referral">Unsplash</a></figcaption></figure>Why Energy Consumption Varies Across LanguagesDifferent programming languages are better for different tasks. This is like how you might pick different shoes for running, walking, or dancing. Some shoes are more comfortable than others. Similarly, some languages are more energy-efficient. There are six characteristics of a programming language to consider.1. Level of Abstraction: Higher-level languages like Python or JavaScript often provide more abstractions to make coding easier, but this can lead to less efficient code compared to lower-level languages like C or C++. For example, automatic memory management in languages like Java and Python can be less efficient than manual memory management in C or C++. However, note that less efficient doesn’t necessarily mean “bad” — the tradeoff is often worth it for the increased development speed and ease of use.2. Runtime Environment: Some languages require a runtime environment to execute, such as Java’s Java Virtual Machine (JVM) or .NET’s Common Language Runtime (CLR). These environments can add overhead, leading to increased energy use compared to languages that compile directly to machine code.3. Optimization by the Compiler/Interpreter: Compilers and interpreters can apply various optimizations to improve the performance of the generated code. The effectiveness of these optimizations can vary between languages, affecting energy consumption.4. Language Design and Features: Certain language features can lead to increased energy consumption. For example, languages that heavily use recursion might use more energy due to the overhead of function calls and extra memory use.5. Built-in Libraries and Functions: The efficiency of built-in libraries and functions can vary between languages. For example, one language’s sorting function might be more or less efficient than another’s, which would affect the energy use of programs using that function.6. Programming Style and Practices: Different languages often encourage different styles of programming and practices that can affect energy consumption. For instance, functional programming languages like Haskell or Erlang might lead to different energy use patterns compared to imperative languages like C or object-oriented ones like Java.Next, let’s examine some of popular languages ranked from most to least energy consuming.Surprising Guzzlers: The Most Energy-Consuming LanguagesComparing the energy efficiency of programming languages directly is challenging. However, we can use the factors that affect language energy efficiency I mentioned earlier to help organize our ranked list. We can also use the same code block across these languages to measure how much energy is guzzled.JavaScriptOften used in web development and runs in a browser environment or Node.js for server-side, which adds overhead. Dynamic typing and high-level abstractions can increase resource usage. JavaScript’s event-driven model can lead to idle time if not managed correctly. <figure id="5081"> <div> <div>

            <iframe class="gist-iframe" src="/gist/jasonmpittman/6730bf5be968566a7957bdfe4d47a77a.js" allowfullscreen="" frameborder="0" height="undefined" width="undefined">
          </div>
        </div>
    </figure></iframe></div></div></figure><p id="21c5">Here, a large array is created and discarded in each iteration,

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which is very wasteful in terms of memory and CPU usage. How often do blocks such as this appear across the internet?PythonA high-level language with many abstractions. It’s interpreted, which can add overhead, and uses automatic memory management, which can be less efficient than manual memory management. However, it’s loved for its simplicity and readability. <figure id="1752"> <div> <div>

            <iframe class="gist-iframe" src="/gist/jasonmpittman/0959821d3c8be43741b1f30265ba8208.js" allowfullscreen="" frameborder="0" height="undefined" width="undefined">
          </div>
        </div>
    </figure></iframe></div></div></figure><p id="5cc4">Similar to the JavaScript example, this Python code creates and discards a large list in each iteration.</p><p id="504d"><i>Java and C#</i></p><p id="9270">Java runs on the Java Virtual Machine, which adds some overhead. However, Java’s strong typing and static memory management can lead to more efficient code than dynamically-typed languages. Its built-in multithreading support can be used to improve efficiency on multi-core processors. C# is similar to Java in many ways, including its use of a runtime environment (.NET CLR). It offers many high-level features and abstractions, but also provides low-level access when needed.</p>
    <figure id="24db">
        <div>
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            <iframe class="gist-iframe" src="/gist/jasonmpittman/5d70ac2ffc719d60ba4b9f3e8f896935.js" allowfullscreen="" frameborder="0" height="undefined" width="undefined">
          </div>
        </div>
    </figure></iframe></div></div></figure><p id="a232">Not all programming languages are energy guzzlers. Some are more like energy sippers. Let’s meet a few of them.</p><p id="3ed7"><i>Go (Golang)</i></p><p id="58b0">Go is a statically-typed compiled language developed by Google that prioritizes simplicity and efficiency. Its design provides the convenience of garbage collection and built-in support for concurrent programming, which can be useful for creating efficient, distributed, and scalable systems. It’s also lower-level than languages like Python and JavaScript, providing more control over system resources.</p>
    <figure id="fc3e">
        <div>
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            <iframe class="gist-iframe" src="/gist/jasonmpittman/82ec51271842cd10ba57c4038be7f3ec.js" allowfullscreen="" frameborder="0" height="undefined" width="undefined">
          </div>
        </div>
    </figure></iframe></div></div></figure><p id="78b6"><i>Rust</i></p><p id="afd4">Rust is a system programming language that aims to provide the performance of C and C++ but with memory safety and less complexity. It uses a unique approach to memory management, with concepts of ownership and borrowing instead of garbage collection or manual memory management, which can lead to more efficient use of resources. Its static typing and efficient compiler also allow for optimized code execution.</p>
    <figure id="674b">
        <div>
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            <iframe class="gist-iframe" src="/gist/jasonmpittman/c10af60f7c4a3478e163e9bdfcfddfb2.js" allowfullscreen="" frameborder="0" height="undefined" width="undefined">
          </div>
        </div>
    </figure></iframe></div></div></figure><p id="6420"><i>C and C++</i></p><p id="6e26">The lowest-level languages in this list, C and C++ allow for direct memory management and hardware access. They lack many of the abstractions of higher-level languages, but this can lead to more efficient code. However, writing efficient C or C++ code often requires a deep understanding of the system’s hardware and OS.</p>
    <figure id="aae9">
        <div>
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            <iframe class="gist-iframe" src="/gist/jasonmpittman/73a4c6c93abb16c229bb0e73da71e2d6.js" allowfullscreen="" frameborder="0" height="undefined" width="undefined">
          </div>
        </div>
    </figure></iframe></div></div></figure><p id="e0ef"><b>Conclusion</b></p><p id="554b">We’ve seen how some programming languages are more energy-efficient than others. We also have outlined seven factors affecting energy consumption as well as six characteristics of any programming language related to such. Lastly, we have a consistent code block we can experiment with to find the high side of energy waste in each language.</p><p id="a3dd">I suggest everyone begin to include energy consumption into their unit testing and solution architectures. The future of software engineering must adapt to consider sustainability and green computing. Major infrastructure and service providers such as AWS are already headed in this direction. Doing so at our level as engineers and developers will help us understand how much electricity our computer uses when writing or running code.</p></article></body>

Unseen Power Drain: The Shocking Energy Secrets of Your Programming Language

Have you ever wondered how much electricity your computer uses when you’re coding? Well, the answer might surprise you. It’s not about the power it takes to run your computer. Rather, it is the energy consumed by the programming language you’re using. Don’t take my word for it though. We can pull back the curtain on this unseen power drain. We can identify seven factors affecting energy consumption in code. We can also outline six characteristics of every programming language related to energy consumption. Doing so will unveil some shocking secrets about the energy cost of different programming languages.

Powering Up: The Basics of Programming Language Energy Use

Like light bulbs and refrigerators, programming languages use energy, too. But how does typing on a keyboard translate to power use? More specifically, how does the act of programming use energy? Let’s start by exploring how code translates to energy consumption.

How Code Translates to Energy Consumption

You might be wondering how a bunch of letters and numbers on a screen could use electricity. Well, your computer has to work to understand and run that code. This work requires energy. We can take a hardware and up perspective as we dive a little deeper here.

The CPU gets its power through a specific connector from the PSU. Inside the CPU, this power is further regulated to precise voltages needed by the various parts of the chip. This is often in the range of 1.0V to 1.3V. The exact voltage can depend on the model and current operating conditions. The CPU uses electricity to power the billions of transistors in its architecture. These transistors are electrically-controlled switches. Each switch has two operations: open and close. These allow or stop the flow of electricity.

The state of each transistor (open/closed, on/off, or in binary terms, 1 or 0) represents binary data. The change between states is computation. In a CPU, these transistors exist in complex circuits. The circuits perform logical and arithmetic operations, manage data transfer, and execute instructions.

When we execute a program, it’s broken down into a set of machine code instructions. Each of these instructions map to a set of high and low voltages (1s and 0s) that control the state of the transistors. In reality, a program is a specific pattern of open and closed switches. The electrical energy is also used to maintain the information in registers. These are small storage areas in the CPU. Electricity propagates signals across the processor too. Most importantly, electricity drives the clock that synchronizes the operations of the CPU.

With that we understand the basic connection between code and energy. Next, let’s take a closer look at what happens behind the scenes when we run a program.

Factors that Affect Language Energy Efficiency

Some programming languages make the CPU work harder than others. This can depend on how the language was designed, what it’s being used for, and how well the code is written. We can organize this into seven factors.

1. Algorithmic Efficiency: The choice of algorithms and data structures can have a significant impact on a program’s energy consumption. Algorithms with lower time complexity (such as O(1) or O(log n)) typically consume less energy because they can process data more efficiently, resulting in less CPU usage and fewer memory accesses compared to less efficient algorithms.

2. Parallelism: Well-implemented parallel computing can distribute computation across multiple cores, reducing the total computation time and potentially lowering energy use, particularly for multi-core processors. However, it’s important to note that not all tasks can be parallelized effectively, and poor implementation of parallelism can result in increased energy usage due to overhead.

3. Idle Time/Waits: Code that causes the processor to idle, such as waiting for input/output operations, can waste energy, especially if the system doesn’t transition into a low-power state during these idle periods.

4. Memory Usage: Frequent memory access, particularly with large data structures, can lead to increased energy consumption. This is because accessing RAM consumes more energy than performing computations in the CPU. Optimizing data structures to minimize unnecessary memory access can help reduce energy consumption.

5. Use of Hardware Acceleration: Some tasks can be offloaded to specialized hardware (like the GPU for graphical tasks or calculations that can be done in parallel). This can be more energy-efficient than performing these tasks on the CPU.

6. Code Optimization: Compiler optimizations can improve the performance and energy efficiency of a program. These optimizations can include things like eliminating unnecessary computations, reducing memory accesses, and more. However, these are often handled by the compiler rather than the programmer.

7. Resource Management: Proper management of system resources such as CPU, memory, network, and disk can also influence energy consumption. This includes properly closing resources when they’re not in use and efficiently managing threads and processes.

We have outlined the factors affecting a programming language’s energy efficiency. Now, let’s delve into why energy consumption varies across different languages.

Why Energy Consumption Varies Across Languages

Different programming languages are better for different tasks. This is like how you might pick different shoes for running, walking, or dancing. Some shoes are more comfortable than others. Similarly, some languages are more energy-efficient. There are six characteristics of a programming language to consider.

1. Level of Abstraction: Higher-level languages like Python or JavaScript often provide more abstractions to make coding easier, but this can lead to less efficient code compared to lower-level languages like C or C++. For example, automatic memory management in languages like Java and Python can be less efficient than manual memory management in C or C++. However, note that less efficient doesn’t necessarily mean “bad” — the tradeoff is often worth it for the increased development speed and ease of use.

2. Runtime Environment: Some languages require a runtime environment to execute, such as Java’s Java Virtual Machine (JVM) or .NET’s Common Language Runtime (CLR). These environments can add overhead, leading to increased energy use compared to languages that compile directly to machine code.

3. Optimization by the Compiler/Interpreter: Compilers and interpreters can apply various optimizations to improve the performance of the generated code. The effectiveness of these optimizations can vary between languages, affecting energy consumption.

4. Language Design and Features: Certain language features can lead to increased energy consumption. For example, languages that heavily use recursion might use more energy due to the overhead of function calls and extra memory use.

5. Built-in Libraries and Functions: The efficiency of built-in libraries and functions can vary between languages. For example, one language’s sorting function might be more or less efficient than another’s, which would affect the energy use of programs using that function.

6. Programming Style and Practices: Different languages often encourage different styles of programming and practices that can affect energy consumption. For instance, functional programming languages like Haskell or Erlang might lead to different energy use patterns compared to imperative languages like C or object-oriented ones like Java.

Next, let’s examine some of popular languages ranked from most to least energy consuming.

Surprising Guzzlers: The Most Energy-Consuming Languages

Comparing the energy efficiency of programming languages directly is challenging. However, we can use the factors that affect language energy efficiency I mentioned earlier to help organize our ranked list. We can also use the same code block across these languages to measure how much energy is guzzled.

JavaScript

Often used in web development and runs in a browser environment or Node.js for server-side, which adds overhead. Dynamic typing and high-level abstractions can increase resource usage. JavaScript’s event-driven model can lead to idle time if not managed correctly.

Here, a large array is created and discarded in each iteration, which is very wasteful in terms of memory and CPU usage. How often do blocks such as this appear across the internet?

Python

A high-level language with many abstractions. It’s interpreted, which can add overhead, and uses automatic memory management, which can be less efficient than manual memory management. However, it’s loved for its simplicity and readability.

Similar to the JavaScript example, this Python code creates and discards a large list in each iteration.

Java and C#

Java runs on the Java Virtual Machine, which adds some overhead. However, Java’s strong typing and static memory management can lead to more efficient code than dynamically-typed languages. Its built-in multithreading support can be used to improve efficiency on multi-core processors. C# is similar to Java in many ways, including its use of a runtime environment (.NET CLR). It offers many high-level features and abstractions, but also provides low-level access when needed.

Not all programming languages are energy guzzlers. Some are more like energy sippers. Let’s meet a few of them.

Go (Golang)

Go is a statically-typed compiled language developed by Google that prioritizes simplicity and efficiency. Its design provides the convenience of garbage collection and built-in support for concurrent programming, which can be useful for creating efficient, distributed, and scalable systems. It’s also lower-level than languages like Python and JavaScript, providing more control over system resources.

Rust

Rust is a system programming language that aims to provide the performance of C and C++ but with memory safety and less complexity. It uses a unique approach to memory management, with concepts of ownership and borrowing instead of garbage collection or manual memory management, which can lead to more efficient use of resources. Its static typing and efficient compiler also allow for optimized code execution.

C and C++

The lowest-level languages in this list, C and C++ allow for direct memory management and hardware access. They lack many of the abstractions of higher-level languages, but this can lead to more efficient code. However, writing efficient C or C++ code often requires a deep understanding of the system’s hardware and OS.

Conclusion

We’ve seen how some programming languages are more energy-efficient than others. We also have outlined seven factors affecting energy consumption as well as six characteristics of any programming language related to such. Lastly, we have a consistent code block we can experiment with to find the high side of energy waste in each language.

I suggest everyone begin to include energy consumption into their unit testing and solution architectures. The future of software engineering must adapt to consider sustainability and green computing. Major infrastructure and service providers such as AWS are already headed in this direction. Doing so at our level as engineers and developers will help us understand how much electricity our computer uses when writing or running code.