Determination of quantum numbers for several excited charmed mesons observed in B- -> D*(+)pi(-) pi(-) decays

A four-body amplitude analysis of the B − → D * + π − π − decay is performed, where fractions and relative phases of the various resonances contributing to the decay are measured. Several quasi-model-independent analyses are performed aimed at searching for the presence of new states and establishing the quantum numbers of previously observed charmed meson resonances. In particular the resonance parameters and quantum numbers are determined for the D 1 ( 2420 ) , D 1 ( 2430 ) , D 0 ( 2550 ) , D ∗ 1 ( 2600 ) , D 2 ( 2740 ) and D ∗ 3 ( 2750 ) states. The mixing between the D 1 ( 2420 ) and D 1 ( 2430 ) resonances is studied and the mixing parameters are measured. The dataset corresponds to an integrated luminosity of 4.7     fb − 1 , collected in proton-proton collisions at center-of-mass energies of 7, 8 and 13 TeV with the LHCb detector

Updated measurement of decay-time-dependent CP asymmetries in D-0 -> K+ K- and D-0 -> pi(+)pi(-) decays

A search for decay-time-dependent charge-parity (CP) asymmetry in D0 \u2192 K+ K 12 and D0 \u2192 \u3c0+ \u3c0 12 decays is performed at the LHCb experiment using proton-proton collision data recorded at a center-of-mass energy of 13 TeV, and corresponding to an integrated luminosity of 5.4 fb^ 121. The D0 mesons are required to originate from semileptonic decays of b hadrons, such that the charge of the muon identifies the flavor of the neutral D meson at production. The asymmetries in the effective decay widths of D0 and anti-D0 mesons are determined to be A_\u393(K+ K 12) = ( 124.3 \ub1 3.6 \ub1 0.5) 7 10^ 124 and A_\u393(\u3c0+ \u3c0 12) = (2.2 \ub1 7.0 \ub1 0.8) 7 10^ 124 , where the uncertainties are statistical and systematic, respectively. The results are consistent with CP symmetry and, when combined with previous LHCb results, yield A_\u393(K+ K 12) = ( 124.4 \ub1 2.3 \ub1 0.6) 7 10^ 124 and A_\u393(\u3c0+ \u3c0 12) = (2.5 \ub1 4.3 \ub1 0.7) 7 10^ 124

Updated measurement of decay-time-dependent CP asymmetries in D-0 -> K+ K- and D-0 -> pi(+)pi(-) decays

A search for decay-time-dependent charge-parity (CP) asymmetry in D-0 -> K+ K- and D-0 -> pi(+)pi(-) eff decays is performed at the LHCb experiment using proton-proton collision data recorded at a center-of-mass energy of 13 TeV, and corresponding to an integrated luminosity of 5.4 fb(-1). The D-0 mesons are required to originate from semileptonic decays of b hadrons, such that the charge of the muon identifies the flavor of the neutral D meson at production. The asymmetries in the effective decay widths of D-0 and (D) over bar (0) mesons are determined to be A(Gamma)(K+ K-) = (-4.3 +/- 3.6 +/- 0.5) x 10(-4) and A(Gamma) (K+ K- ) = (2.2 +/- 7.0 +/- 0.8) x 10(-4), where the uncertainties are statistical and systematic, respectively. The results are consistent with CP symmetry and, when combined with previous LHCb results, yield A(Gamma) (K+ K-) = (-4.4 +/- 2.3 +/- 0.6) x 10(-4) and A(Gamma) (pi(+)pi(-))= (2.5 +/- 4.3 +/- 0.7) x 10(-4)

CompassUT : study of a GPU track reconstruction for LHCb upgrades

We present a fast, data-oriented GPU tracking algorithm, CompassUT, as a potential option to cope with the expected throughput of 40Tbit/s for LHCb upgrade. We present a parallel version of the raw input decoding, optimized for SIMD architectures. We sort the hits by X and Y into group sectors while decoding, to have a fast sorting and searching of the hits. We implement the tracking by reducing the memory footprint, reducing branching to a minimum and making the algorithm data-oriented for SIMD architectures. We show the achieved throughput in a variety of consumer and server GPUs, and present the impact on both the computing and physics performance for different configurations of the algorithm

Compass SPMD: a SPMD vectorized tracking algorithm

The LHCb detector will be upgraded in 2021, where the hardware-level trigger will be replaced by a High Level Trigger 1 software trigger that needs to process the full 30 MHz data-collision rate. As part of the efforts to create a GPU High Level Trigger 1, tracking algorithms need to be optimized for SIMD architectures in order to achieve high-throughput. We present a SPMD (Single Program, Multiple Data) version of Compass, a tracking algorithm optimized for SIMD architectures, vectorized using the Intel SPMD Program Compiler. This compiler and model allows to execute program instances in parallel, and allows to use exploit the SIMD lanes of CPUs using GPU-like source code, without the need of low-level details knowledge. It is able to target different vector widths, vector instructions sets and combine different levels of parallelism. We design the algorithm focusing on highly parallel architectures in mind, minimizing divergence and memory footprint while creating a data-oriented algorithm that is efficient for SIMD architectures. We vectorize the algorithm using the SPMD programming model, preserving the algorithm design and delivering the same physics efficiency as its GPU counterpart. We study the physics performance and throughput of the algorithm. We discuss the impact with different vector widths and instructions sets and compare it with the GPU implementation

Inverted CERN School of Computing 2018

To increase in both performance and efficiency, our programming models need to adapt to better exploit modern processors. The classic idioms and patterns for programming such as loops, branches or recursion are the pillars of almost every code and are well known among all programmers. These patterns all have in common that they are sequential in nature. Embracing parallel programming patterns, which allow us to program for multi- and many-core hardware in a natural way, greatly simplifies the task of designing a program that scales and performs on modern hardware, independently of the used programming language, and in a generic way

Compass SPMD: a SPMD vectorized tracking algorithm

Compass is a SPMD (Single Program Multiple Data) tracking algorithm for the upcoming LHCb upgrade in 2021. 40 Tb/s need to be processed in real-time to select events. Alternative frameworks, algorithms and architectures are being tested to cope with the deluge of data. Allen is a research and development project aiming to run the full HLT1 (High Level Trigger) on GPUs (Graphics Processing Units). Allen’s architecture focuses on data-oriented layout and algorithms to better exploit parallel architectures. GPUs already proved to exploit the framework efficiently with the algorithms developed for Allen, implemented and optimized for GPU architectures. We explore opportunities for the SIMD (Single Instruction Multiple Data) paradigm in CPUs through the Compass algorithm. We use the Intel SPMD Program Compiler (ISPC) to achieve good readability, maintainability and performance writing “GPU-like” source code, preserving the main design of the algorithm

Compass SPMD: a SPMD vectorized tracking algorithm

Compass is a SPMD (Single Program Multiple Data) tracking algorithm for the upcoming LHCb upgrade in 2021. 40 Tb/s need to be processed in real-time to select events. Alternative frameworks, algorithms and architectures are being tested to cope with the deluge of data. Allen is a research and development project aiming to run the full HLT1 (High Level Trigger) on GPUs (Graphics Processing Units). Allen’s architecture focuses on data-oriented layout and algorithms to better exploit parallel architectures. GPUs already proved to exploit the framework efficiently with the algorithms developed for Allen, implemented and optimized for GPU architectures. We explore opportunities for the SIMD (Single Instruction Multiple Data) paradigm in CPUs through the Compass algorithm. We use the Intel SPMD Program Compiler (ISPC) to achieve good readability, maintainability and performance writing “GPU-like” source code, preserving the main design of the algorithm

EDM4hep - a common event data model for HEP experiments

A shared, common event data model, EDM4hep, is an integral part of the Key4hep project. EDM4hep aims to be usable by all future collider projects, despite their different collision environments and the different detector technologies that are under discussion. This constitutes a major challenge that EDM4hep addresses by using podio, a C++ toolkit for the creation and handling of event data models, developed in the context of the AIDA R&D program. This approach allows for quick prototyping of new data types and provides a streamlined framework for updates. After presenting an overview of the basic features of EDM4hep and podio, we will discuss the current experience with an initial version of EDM4hep in different physics studies. Additionally, we will present the planned developments that are necessary for a first stable version of EDM4hep, addressing in particular backward compatibility aspects and schema evolution. We will conclude with an outlook on the future developments directions beyond this first stable version

Of Frames and schema evolution - The newest features of podio

The podio event data model (EDM) toolkit provides an easy way to generate a performant implementation of an EDM from a high level description in yaml format. We present the most recent developments in podio, most importantly the inclusion of a schema evolution mechanism for generated EDMs as well as the "Frame", a thread safe, generalized event data container. For the former we discuss some of the technical aspects in relation with supporting different I/O backends and leveraging potentially existing schema evolution mechanisms provided by them. Regarding the Frame we introduce the basic concept and highlight some of the functionality as well as important aspects of its implementation. We also present some other, smaller new features, which have been inspired by the usage of podio for generating different EDMs for future collider projects, most importantly EDM4hep, the common EDM for the Key4hep project. We end with a brief overview on current developments towards a first stable version as well as an outlook on future developments beyond that