Uploaded March 2026 | Updated September 2026, 3 weeks ago
#VoxxedDaysCERN26
In this talk we will present TornadoVM (www.tornadovm.org); a state-of-the-art programming framework that enables native GPU acceleration of Java programs. TornadoVM allows developers to program GPUs from within Java while the system handles compilation, execution, and optimization completely transparently.
TornadoVM is an open-source project (github.com/beehive-lab/TornadoVM) and it is being used by the European Space Agency GAIA mission to accelerate its data processing pipelines resulting in significant reduction of execution times. In addition, it offers a rich toolset ecosystem to help developers throughout their journey (coding, debugging, performance optimizations) and it recently added to its ecosystem GPULlama3.java (github.com/beehive-lab/GPULlama3.java); the first GPU-accelerated native Java implementation for AI inference on a plethora of LLM models which has been integrated with Lanchain4J and Quarkus.
The target audience is developers who wish to make a first transition into the GPU programming world, or just practitioners who wish to use the high performance Java AI libraries that TornadoVM provides.
The key takeaways are:
1) Java is AI ready since we can harness GPU power natively for LLM inference
2) Programming GPUs from within Java is easier with the mature ecosystem that TornadoVM provides
3) Having a native Java platfrom for AI and general purpose GPU acceleration enhances both productivity and integration
#VoxxedDaysCERN26
In this talk we will present TornadoVM (www.tornadovm.org); a state-of-the-art programming framework that enables native GPU acceleration of Java programs. TornadoVM allows developers to program GPUs from within Java while the system handles compilation, execution, and optimization completely transparently.
TornadoVM is an open-source project (github.com/beehive-lab/TornadoVM) and it is being used by the European Space Agency GAIA mission to accelerate its data processing pipelines resulting in significant reduction of execution times. In addition, it offers a rich toolset ecosystem to help developers throughout their journey (coding, debugging, performance optimizations) and it recently added to its ecosystem GPULlama3.java (github.com/beehive-lab/GPULlama3.java); the first GPU-accelerated native Java implementation for AI inference on a plethora of LLM models which has been integrated with Lanchain4J and Quarkus.
The target audience is developers who wish to make a first transition into the GPU programming world, or just practitioners who wish to use the high performance Java AI libraries that TornadoVM provides.
The key takeaways are:
1) Java is AI ready since we can harness GPU power natively for LLM inference
2) Programming GPUs from within Java is easier with the mature ecosystem that TornadoVM provides
3) Having a native Java platfrom for AI and general purpose GPU acceleration enhances both productivity and integration
![[VDBUH2026] Roberto Cortez - How difficult can it be to write efficient code?](https://i.ytimg.com/vi/qSJf8yKoFhY/mqdefault.jpg "[VDBUH2026] Roberto Cortez - How difficult can it be to write efficient code?
When we write code, we often prioritize completing our feature over performance. However, performance may become a bottleneck, requiring additional resources and time. When we reach that point, how should we proceed?
In this session, we will explore tools that can help us identify performance problems and focus our code improvements in the most beneficial areas. Some of these tools include AsyncProfiler, JMH, JFR, and others. Expect a session with lots of demos, live coding, real-life examples, and tips and tricks to help you get started on improving your code’s performance.")
![[VDBUH2026] Andra Ghibutiu & Alex Proca - Opening thoughts](https://i.ytimg.com/vi/qSzhiRLFcwY/mqdefault.jpg "[VDBUH2026] Andra Ghibutiu & Alex Proca - Opening thoughts
[VDBUH2026] Andra Ghibutiu & Alex Proca - Opening thoughts")
