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1.
Compass SPMD: a SPMD vectorized tracking algorithm
Reference: Poster-2019-1018
Created: 2019. -1 p
Creator(s): Fernandez Declara, Placido

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.

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2.
CompassUT : study of a GPU track reconstruction for LHCb upgrades
Reference: Poster-2019-680
Keywords:  LHCb
Created: 2019. -1 p
Creator(s): Fernandez Declara, Placido

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.

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3.
ALICE HLT TPC Tracking of Heavy-Ion Events on GPUs / Rohr, David Michael (speaker) (Johann-Wolfgang-Goethe Univ. (DE))
The ALICE High Level Trigger (HLT) is capable of performing an online reconstruction of heavy-ion collisions. The reconstruction of particle trajectories in the Time Projection Chamber (TPC) is the most compute intensive step. The TPC online tracker implementation combines the principle of the cellular automaton and the Kalman filter. It has been accelerated by the usage of graphics cards (GPUs). A pipelined processing allows to perform the tracking on the GPU, the data transfer, and the preprocessing on the CPU in parallel. In order to use data locality, the tracking is split in multiple phases. At first, track segments are searched in local sectors of the detector, independently and in parallel. These segments are then merged at a global level. A shortcoming of this approach is that if a track contains only a very short segment in one particular sector, the local search possibly does not find this short part. The fast GPU processing allowed to add an additional step: all found tracks are extrapolated to neighboring sectors and the unassigned clusters which constitute the missing track segment are collected. For running the QA on computers without a GPU, it is important that the output of the CPU and the GPU tracker is as consistent as possible. One major challenge was to implement the tracker such that the output is not affected by concurrency, while maintaining peak performance and efficiency. For instance, a naive implementation depended on the order of the tracks which is nondeterministic when they are created in parallel. Still, due to non-associative floating point arithmetic a direct binary comparison of the CPU and the GPU tracker output is impossible. Thus, the approach chosen for evaluating the GPU tracker efficiency is to compare the cluster to track assignment of the CPU and the GPU tracker cluster by cluster. With the above comparison scheme, the output of the CPU and the GPU tracker differ by 0.00024%. The GPU tracker outperforms its CPU analog by a factor of three. Recently, the ALICE HLT cluster was upgraded with new GPUs and will be able to process central heavy ion events at a rate of approximately 200 Hz. The tracking algorithm together with the necessary modifications, a performance comparison of the CPU and the GPU version, and QA plots will be presented..
2012 - Streaming video. Conferences; Computing in High Energy and Nuclear Physics (CHEP) 2012 External links: Talk details; Event details In : Computing in High Energy and Nuclear Physics (CHEP) 2012
4.
GPU-Based Tracking Algorithms for the ATLAS High-Level Trigger / Emeliyanov, D (Rutherford) ; Howard, J (Oxford U.)
Results on the performance and viability of data-parallel algorithms on Graphics Processing Units (GPUs) in the ATLAS Level 2 trigger system are presented. [...]
ATL-DAQ-PROC-2012-006.
- 2012. - 9 p.
Original Communication (restricted to ATLAS) - Full text
5.
A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU Architectures / Fernandez Declara, Placido (CERN ; Carlos III U., Madrid) ; Campora Perez, Daniel Hugo (CERN ; Seville U.) ; Garcia-Blas, Javier (Carlos III U., Madrid) ; Vom Bruch, Dorothea (Paris U., VI-VII) ; Daniel Garcia, J (Carlos III U., Madrid) ; Neufeld, Niko (CERN)
Real-time data processing is one of the central processes of particle physics experiments which require large computing resources. The LHCb (Large Hadron Collider beauty) experiment will be upgraded to cope with a particle bunch collision rate of 30 million times per second, producing 10$^{9}$ particles/s. [...]
2019 - 15 p. - Published in : IEEE Access 7 (2019) 91612-91626 Fulltext: PDF;
6.
Cover not available Hands-on GPU computing with Python : explore the capabilities of GPUs for solving high performance computational problems / Bandyopadhyay, Avimanyu
GPU technologies are the paradigm shift in modern computing [...]
Birmingham : Packt Publishing, 2019. - 441 p.

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7.
GPU-accelerated track reconstruction in the ALICE High Level Trigger / Rohr, David (Frankfurt U., FIAS ; CERN) ; Gorbunov, Sergey (Frankfurt U., FIAS) ; Lindenstruth, Volker (Frankfurt U., FIAS ; Frankfurt U.) /ALICE
ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online compute farm which reconstructs events measured by the ALICE detector in real-time. [...]
arXiv:1712.09430.- 2017-11-22 - 8 p. - Published in : J. Phys. : Conf. Ser. 898 (2017) 032030 Fulltext: PDF; Fulltext from Publisher: PDF; Preprint: PDF;
In : 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, San Francisco, Usa, 10 - 14 Oct 2016, pp.032030
8.
The ATLAS Trigger Algorithms for General Purpose Graphics Processor Units / Tavares Delgado, Ademar (Laboratorio de Instrumenta\c{c}\~ao e F\i sica Experimental de Part\i culas) ; Emeliyanov, Dmitry (Rutherford Appleton Laboratory)
The ATLAS Trigger Algorithms for General Purpose Graphics Processor Units Type: Talk Abstract: We present the ATLAS Trigger algorithms developed to exploit General­ Purpose Graphics Processor Units. [...]
ATL-DAQ-PROC-2016-035.
- 2016. - 5 p.
Original Communication (restricted to ATLAS) - Full text
9.
GPU-Based Tracking Algorithms for the ATLAS High-Level Trigger / Emeliyanov, D (STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, UK) ; Howard, J (University of Oxford, Oxford, UK)
GPU-accelerated event processing is one of the possible options for the ATLAS High-Level Trigger (HLT) upgrade for higher LHC luminosity. This poster presents data preparation and track finding algorithms specifically designed to run on a GPU using a “client-server” solution for hybrid CPU/GPU event processing and integration of the GPU algorithms into existing ATLAS HLT software. [...]
ATL-DAQ-SLIDE-2012-225.- Geneva : CERN, 2012 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Computing in High Energy and Nuclear Physics 2012, New York, NY, USA, 21 - 25 May 2012
10.
Triggering events with GPUs at ATLAS / Kama, Sami (Southern Methodist University, Department of Physics)
The growing complexity of events produced in LHC collisions demands more and more computing power both for the online selection and for the offline reconstruction of events [...]
ATL-DAQ-PROC-2015-013.
- 2015. - 8 p.
Original Communication (restricted to ATLAS) - Full text - IOP Open Access article

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