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of the ROBERTA research project with the aim of: to explore the potential of GPU programming for treatment planning using randomized optimization approaches and the development of optimization models and
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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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energy efficiency bounds of modern CPU, GPU and FPGA devices at performing set operations in the context of combinatorial applications; Investigation of current trends in programming FPGA accelerators and
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main activities: ; 1. Exploration of the applicability of eBPF and its ecosystem: review and exploration of the use of eBPF in different domains (e.g., GPU), of the various libraries available for its
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techniques that enable the application of eBPF to areas that currently lack direct support (e.g., GPUs, HPC systems, etc.); 2. Development of new eBPF functionalities: exploration and development of new
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(LLMs); Configure and optimize cloud computing solutions or on-premise infrastructures that ensure high availability and scalability; Implement tools for efficient resource management, such as GPU