The Julia programming language: a missed opportunity for AI
The use of Python is growing at lightning speed. This language is benefiting enormously from the AI march, despite criticism about disappointing performance. Meanwhile, Julia, designed to be better equipped for handling large data volumes, can be a lot faster. Nevertheless, the latter language is barely growing. Why?
Julia has been around since 2009 and is developed on an open-source basis. Its creators, in a 2012 blog post, stated they aimed to combine the speed of C with the ease of use of Python, while adopting additional positive qualities from various other leading programming languages. They admitted that was a “greedy” desire, but Julia has been considered up for the task for a while. The syntax has been stable since 2018 with version 1.0 – in essence, it was primed for a breakthrough should the opportunity arrive.
Julia is highly parallelized, meaning it is set up to perform many computations simultaneously. This distributed approach to compute is crucial for handling large amounts of data in a timely manner. The language thus responds to the current trend within CPUs and GPUs to provide more and more cores. This applies to products intended for individual users as well as High Performance Computing (HPC) and data centers. If you need to scale your workload, Julia should offer a strong basis to keep growing.
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