I am a Research Fellow at the University of Manchester with specialization on heterogeneous architectures and reconfigurable accelerators. I have authored more than 20 research articles in the field of hardware acceleration, system software and programming languages. Currently my work involves heterogeneous architectures ranging from low-power devices to high-end cloud deployments. I am one of the lead developers of TornadoVM and have been part of the team for the last eight years. I have also led the project's communication and dissemination efforts, helping to articulate its goals and advancements to both academic and industrial audiences through talks, documentation, and outreach activities.
In the AI era, accessing GPUs from Java is essential - but it still remains a challenge. TornadoVM bridges this gap by enabling developers to accelerate Java applications on GPUs, CPUs, and FPGAs without writing any GPU code. Originally born as a PhD research project at the University of Manchester, TornadoVM has evolved into a mature open-source framework supported by both academia and a growing global community.
This talk will present the key innovations that are offered by the TornadoVM runtime platform, and will focus on practical ways to optimize existing Java applications through its high-level API. Through hands-on examples, we will demonstrate how to offload computations, manage data efficiently, and achieve significant performance gains - entirely from Java.
We will also showcase GPULlama3.java, a pure Java large language model (LLM) library accelerated with TornadoVM, illustrating how modern AI workloads can run efficiently within the JVM. Finally, a live-coding demo using IntelliJ IDEA will highlight TornadoVM’s integrated profiling and tooling ecosystem for seamless development experience.
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