The Julia language is designed for high-performance computing. It does not come for free, however. When it comes to performance, it takes practice to write code that is more compiler-friendly, thus making it more likely to translate the program into optimized machine code.

For the past few decades, computers have seemed to become faster and faster every year. What used to be performance bottlenecks are more easily solved using today's hardware. At the same time, we are also facing more challenges due to the explosion of data. A good example is the field of big data and data science. As the amount of data grows, we need even more computing power to handle these new use cases.

Unfortunately, the speed of computers has not grown as rapidly as it did in the past. Moore's Law states that the number of transistors on a microchip doubles roughly every 18 months, and since 1960 it has been correlated with the growth in CPU speed. However, it is well known that Moore's Law will no longer be applicable soon due to a physical limitation: the number of transistors that can be fitted to a chip and the precision of the fabrication process.

In order to address today's computational needs, especially in the world of artificial intelligence, machine learning, and data science, practitioners have been gearing toward a scale-out strategy that utilizes multiple CPU cores across many servers, and looking at exploiting the efficiency of GPUs and TPUs.