Algorithms, Efficiency
Algorithm optimization to improve energy efficiency and cost
Did you know that data centers consume more energy than some small countries? According to the International Energy Agency, they use more than 1% of the world’s energy.
In the business world, algorithm optimization isn’t just a matter of reducing execution times to improve performance. Efficiency can also translate into lower power consumption and a lower environmental impact.
Let’s see how we can make a difference by adapting our algorithms and leveraging the latest technologies, such as those provided by AWS.
How can we understand efficiency?
When we talk about efficiency in algorithms, we have to take into account two main factors: time efficiency (speed at which an algorithm is processed) and space efficiency (memory used for it).
Algorithmic design with respect to software
It consists of reducing the number of operations used to solve a problem (complexity). To represent it, we use Big-O notation, which describes how the cost of an algorithm scales by ignoring small details and focusing on large-scale behavior.
Let’s look at some examples:
O(1) – Constant: It always takes the same time, regardless of the input size.
Example: Accessing the i-th element of an array.
O(log n) – Logarithmic: Every time the input size doubles, you only need one extra operation.
Example: Searching in a balanced binary tree.
O(n) – Linear: The execution time grows proportionally to the size of the input.
Example: Searching for an element in an unsorted list.
O(n log n) – Quasilinear: Slower than linear, but common in efficient sorting algorithms, as it is more efficient than quadratic cost.
Example: The well-known Merge Sort or Quick Sort algorithms.
O(n²) – Quadratic: Each element of the input interacts with all the others.
Example: Comparing every pair of elements in a list.
It is very important to look for ways to use the lowest possible complexity in our code, since this will not only reduce execution time, but also optimize the amount of memory and energy used.
For example:
In a data search system, if we have a linear search algorithm (O(n)), it is worth spending time sorting the list first, so that we can then use, for instance, a binary search — making all our searches now O(log n).
Another practical example is when sorting large datasets — always look for the best possible method, replacing algorithms such as Bubble Sort (O(n²)) with more efficient ones like Merge Sort (O(n log n)).
When possible, we can also rely on data structures such as HashMap, taking advantage of the fact that, on average, data access is constant (O(1)), allowing us, for example, to transform a process that counts the occurrences of each element in a list from O(n²) to O(n) (since it only needs to be traversed once).
Algorithmic design adapted to hardware
Not all processors are created equal. With the rise of architectures like Amazon’s ARM-Graviton, designed for energy efficiency, it’s vital to optimize algorithms considering the hardware on which they will run.
ARM Processors: These chips predominate in cloud computing thanks to their low power consumption. Algorithms that leverage vector calculations and parallelism tend to perform better on these processors.
Traditional x86 Processors: While more powerful for high-performance tasks, their energy consumption is higher. Therefore, for simple and repetitive tasks, ARM processors are a more sustainable choice.
Let’s look at an example:
Suppose we’re training a machine learning model in the cloud. ARM Graviton processors (like AWS EC2 Graviton) will give us performance similar to an x86 processor, but consuming less power (around 40% less for certain workloads).
If we can adapt our code to use SIMD (Single Instructions on Multiple Data) to take advantage of these processors’ ability to divide the workload into simultaneous operations (parallelism), we can make a significant difference in cost and sustainability.
Driving Efficiency and Sustainability in Technology
Algorithmic optimization: We can decrease the computational complexity of our algorithms and use appropriate data structures to improve our performance in terms of time and space, while also reducing energy consumption.
Hardware adaptation: By using optimized hardware (such as ARM-Graviton) for certain operations, we can maximize performance with lower power consumption. Adjusting our algorithms to take advantage of hardware capabilities (for example, parallelism) can make a significant difference.
Sustainability: Improved efficiency not only enhances the quality of our solutions, but also reduces costs and minimizes our carbon footprint by requiring less energy and computing power for the same operations.
