PhD graduated
Team : ALSOC
    Sorbonne Université - LIP6
    Boîte courrier 169
    Couloir 24-25, Étage 4, Bureau 417
    4 place Jussieu
    75252 PARIS CEDEX 05

Tel: +33 1 44 27 54 15, Arthur.Hennequin (at) nulllip6.fr

Supervision : Lionel LACASSAGNE

Co-supervision : Vladimir GLIGOROV (LPNHE) Benjamen COUTURIER (CERN)

Performance optimisation for the LHCb experiment

The LHCb experiment, at CERN, is preparing a major upgrade of its detector and a change from an hardware-based to a fully software-based trigger system. It is now facing the challenge of being able to process incoming events at a rate of 30 million events per second. To cope with this massive data input, the software must be optimized to use the processing power of the filtering farm more efficiently. This thesis focus on the first algorithm of LHCb's High Level Trigger software: the Vertex Locator (VELO) reconstruction algorithm. The VELO is the first detector encountered by particles, directly surrounding the interaction region. Its goal is to find the initial track candidate that are then followed through the other layers of the LHCb detector with a good enough resolution that they could also be used to locate the location of the collisions. The first step of this algorithm is to prepare the data by grouping pixels of the silicon sensors into hits; this process is called connected component analysis (CCA). This thesis presents multiple new CCA algorithms for both CPU and GPU architectures. The first algorithm, HA4, was developed at the very start of this thesis and improved the state-of-the-art in connected component labeling on GPUs, as well as being the first efficient implementation of connected component analysis on GPUs. The second algorithm is a GPU port of the FLSL SIMD CPU algorithm, inspired by the LSL algorithm. FLSL on GPUs improved upon HA4 by reducing the memory accesses conflicts that are especially presents on new hardware with a lot of cores. Along with FLSL, two other optimisations aimed at further reducing conflicts are presented and evaluated. On CPU, two new algorithms were made for this thesis. The first one is a modification of the classic Rosenfeld algorithm to use SIMD. The second one is a new algorithm, named SparseCCL, which takes advantage of the sparsity of the input images. A new VELO reconstruction algorithm using SIMD is presented, that enable LHCb to process events in real time and improve the quality of the reconstruction. The SIMDWrapper library, developed for the new VELO algorithm, is now part of LHCb's software and is used in other algorithms.

Defence : 01/31/2022 - 14h - https://us02web.zoom.us/j/85400575437?pwd=N0p3elA0ZGV4UFVWaVpHWU9mZkVadz09

Jury members :

François Irigoin (CRI, Mines ParisTech) [Rapporteur]
Denis Barthou (INRIA Bordeaux) [Rapporteur]
Lionel Lacassagne (LIP6, Sorbonne Université)
Stef Graillat (LIP6, Sorbonne Université)
Caroline Collange (INRIA Rennes)
Vladimir Gligorov (LPNHE, Sorbonne Université)

2018-2022 Publications