Interview: Why Java is the future of cloud applications

Nvidia has seen huge demand for its graphics processing units (GPUs) to power artificial intelligence (AI) workloads in the datacentre. GPUs pack hundreds of relatively simple processor cores into a single device, which can then be programmed to run in parallel, achieving the acceleration required in AI inference and machine learning workloads.

Sellers describes AI as an “embarrassingly parallel” problem, which can be solved using a high number of GPU processing cores, each running a relatively simple set of instructions. This is why the GPU has become the engine of AI. But this does not make it suitable for all applications that require a high degree of parallelism, where several complex tasks are programmed to run simultaneously.

For one of Azul’s customers, financial exchange LMAX Group, Sellers says GPUs would never work. “They would be way too slow and the LMAX use case is nowhere near as inherently parallel as AI.”

GPUs, he says, are useful in accelerating a very specific type of application, where a relatively simple piece of processing can be distributed across many processor cores. But a GPU is not suitable for enterprise applications that require complex code to be run in parallel across multiple processors.

Beyond the hardware debate over whether to use GPUs in enterprise applications, Sellers believes the choice of programming language is an important consideration when coding AI software that targets GPUs. 

While people are familiar with programming AI applications in Python, he says: “What people don’t recognise is that Python code is not really doing anything. Python is just the front end to offload work to the GPUs.”

Sellers says Java is better suited than other programming languages for developing and running traditional enterprise applications that require a high degree of parallelism.

While Nvidia offers the GPU language CUDA, when writing traditional enterprise applications, Sellers says Java is the only programming language that has true vector capabilities and massive multithreading capabilities. According to Sellers, these make Java a better language for programming applications requiring parallel computing capabilities. With virtual threading, which came out with Java 21, it becomes easier to write, maintain and debug high-throughput concurrent applications.

“Threading, as well as vectorisation, which enables more than one computer operation to be run simultaneously, have become a lot better over the last few Java releases,” he adds.

Given Azul’s product offerings, Sellers is clearly going to exalt the virtues of the Java programming language. However, there is one common thread in the conversation that IT decision-makers should consider. Assuming the future of enterprise IT is one dominated by a cloud-native architecture, even when some workloads must be run on-premise, IT leaders need to address a new reality that x86 is not the only game in town.