Level Up Your Java Performance with TornadoVM

Juan, what is the focus of your work these days?

I'm a Research Fellow at the University of Manchester. I work in the Advanced Processor Technology Group (APT) at the University. We do research on new parallel programming models and ways to improve programmability for modern computer architectures, such as GPUs and FPGAs. We do research at the programming language level as well as the runtime system and compiler levels. One of the use cases we have is the TornadoVM, which is a high performance computing platform for the JVM. So Java developers can run their applications on heterogeneous hardware, ideally with minimal effort.

How would you describe the persona and the level of the target audience for your presentation?

I think the target audience is developers and software engineers that are interested in accelerating applications by using GPUs and FPGAs from Java, and not necessarily experts on parallel hardware. So these developers are interested in getting some hints about how this acceleration can be done from Java, where to start looking, and some ideas about how to program. My talk also targets developers who want to achieve better performance because they are running specific domains of applications, such as ML, DL, Fintech. These developers usually require high powered devices. And I think TornadoVM is a good target, if their applications are written in Java.

What do you want these people to walk away from your talk with?

I would like people to remember what TornadoVM is and the reasons to use it. Why are we creating this technology now?  What can we achieve? and how? Also, I would like people to remember that TornadoVM can offer a bit of extra performance for a specific domain of applications. So if your application is a good target for the device being used, you might get extra performance. Besides, I would like people to remember that TornadoVM is not a replacement of the existing Java VM, but rather a complement, in which developers can access heterogeneous hardware and increase performance for certain specific domains of applications like machine learning, deep learning, computer vision, and fintech, just to name a few.