What is green coding?
The green revolution is driving industries worldwide to embrace more sustainable practices. Similarly, software development is recognizing its role in curbing environmental impact. With data processing estimated to already account for over 2% of U.S. emissions, a figure projected to rise without the implementation of mitigation measures, the need for "green coding" techniques is evident. Green coding refers to programming practices and languages that optimize efficiency to reduce resource usage and emissions. Two such languages, Erlang and Elixir, leverage capabilities in concurrency and parallelism to boost performance far beyond what more common languages allow - combating escalating impacts through optimized code.
The Push for Simultaneous Processing On Hardware
For decades, the hardware chips exponentially increased in technical capability primarily by raising their clock speed - the rate at which a processor's central component ticks. Faster clocks allowed chips to perform more calculations per second. However, increasing clocks much beyond 4GHz presented physical limitations as chips began to overheat dangerously from the higher power demands. To keep delivering faster processors, chip designers instead turned to multiplying the number of processing cores on each chip rather than accelerating single cores. Modern CPUs can contain dozens of cores, each running at a slower clock than alone would provide sufficient overall computing power. For software to fully leverage this parallel processing prowess across multiple cores simultaneously, capabilities for concurrency and parallelism are essential, which are not present in many of the common languages.
Understanding Concurrency and Parallelism
Concurrency allows performing multiple tasks simultaneously, while parallelism refers to executing multiple tasks at the exact same time using multiple processing units. Effectively utilizing all available CPU cores through concurrency and parallelism is essential for software to achieve maximum efficiency on modern multi-core hardware.
The Impact of Concurrency & Parallelism Usage
Consider a large-scale web application running on a data center server farm. Using only a single core, it could process 1,000 requests per second and consume 100 watts of power. By spreading processing across the server's full complement of 32 cores through concurrent asynchronous tasks, throughput could be increased exponentially to over 32,000 rps while using the same 100 watts thanks to parallelism. The energy cost per request would drop over 30-fold, translating to huge reductions in electricity usage and carbon emissions at scale.
As hardware continues evolving more cores as a path to higher performance, software built without concurrency and parallelism in mind leaves vast processing power untapped. For sustainable computing, techniques that allow software to keep pace with multi-core advancement are imperative.
The Power of Erlang and Elixir
The shift in hardware design towards multiplying processing cores instead of accelerating single cores created new challenges for software. Traditional languages like C, Java, Python and C++ were developed prior to the emergence of multi-core CPUs and are primarily optimized to use a single core, leaving others idle yet still consuming energy. To maximize efficiency on modern parallel hardware, software must support true concurrency through spreading work across all available cores simultaneously.
Erlang and Elixir were designed from the ground up with the hardware's new multi-core reality in mind. Their lightweight process models and built-in support for asynchronous message passing make concurrency and parallelism seamless. Applications transparently scale to utilize all CPU resources through automatically partitioning work among concurrent processes.
Are there other solutions?
Other languages may offer solutions such as threads but these approaches are prone to issues like deadlocks and race conditions. Significant extra coding effort is required to achieve reliability at scale. In contrast, Erlang and Elixir abstract away such complexities through their virtual machine abstraction layer. Developers can focus on solving business problems while the runtime orchestrates parallel execution optimally.
By optimizing for modern chip architectures from the start, Erlang and Elixir allow software to keep pace with hardware evolution. Their approach unlocks vastly improved efficiency that traditional single-core optimized languages can't match. This positions them as uniquely enabling the sustainable computing demands of a multi-core powered future.
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