Description
This work presents the integration of the Alpaka library into the ALICE O² framework to enable portable, high-performance execution of the ITS clustering algorithm across heterogeneous hardware. By refactoring the original CPU-based code to a Struct of Arrays (SoA) layout and implementing an Alpaka kernel for masked-pixel filtering, we achieved consistent results and up to 20% faster execution on CPU accelerators. Ongoing efforts focus on extending the kernel to full clustering and deploying it on GPU environments such as CINECA’s Leonardo for large-scale performance benchmarking.
Author
Dr
Leonardo Cristella
(INFN)
