Speaker
Description
In ALICE, LHC Run 3 marks a major step toward GPU-centric data processing.
During the synchronous (online) phase, GPUs are fully dedicated to Time Projection Chamber reconstruction and compression. During the asynchronous (offline) phase, additional reconstruction tasks can be offloaded to GPUs to improve overall computing efficiency and throughput.
We report the porting of the ITS2 reconstruction chain to AMD and NVIDIA GPUs and its integration into the mainline ALICE GPU reconstruction framework.
The GPU algorithms are currently being commissioned for the upgraded Inner Tracking System (ITS2); we present integration details, performance characterization, validation against the CPU baseline and the outstanding challenges.
Results for representative physics datasets demonstrate an overall speed-up in full asynchronous production of $>26\%$, highlighting the benefit of heterogeneous acceleration for Run 3.
Finally, we address a time-dependent detector effect in the continuous ITS2 readout: due to the finite rise time of the ALPIDE chip, charge deposits created near a readout frame boundary can be time-shifted so that their clusters appear entirely in the subsequent frame.
This migration leads to missing clusters in the readout frame and, consequently, a loss of tracks in events close to frame borders.
We describe the procedure implemented in the reconstruction chain to compensate for this effect and demonstrate that it recovers the affected tracks, thereby improving the overall reconstruction performance and extending ALICE’s physics reach in Run 3.