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Fast Parallel Tracking Algorithm for the Muon Detector of the CBM Experiment at Fair

机译:展会中CBM实验MUON检测器的快速并行跟踪算法

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Particle trajectory recognition is an important and challenging task in the Compressed Baryonic Matter (CBM) experiment at the future FAIR accelerator at Darmstadt. The tracking algorithms have to process terabytes of input data produced in particle collisions. Therefore, the speed of the tracking software is extremely important for data analysis. In this contribution, a fast parallel track reconstruction algorithm which uses available features of modern processors is presented. These features comprise a SIMD instruction set (SSE) and multithreading. The first allows one to pack several data items into one register and to operate on all of them in parallel thus achieving more operations per cycle. The second feature enables the routines to exploit all available CPU cores and hardware threads. This parallel version of the tracking algorithm has been compared to the initial serial scalar version which uses a similar approach for tracking. A speed-up factor of 487 was achieved (from 730 to 1.5 ms/event) for a computer with 2 × Intel Core i7 processors at 2.66 GHz.
机译:粒子轨迹识别是Darmstadt未来公平加速器的压缩式齐静电物质(CBM)实验中的一个重要而挑战性的任务。跟踪算法必须处理在颗粒碰撞中产生的输入数据的Tberabytes。因此,跟踪软件的速度对于数据分析来说非常重要。在这一贡献中,呈现了一种使用现代处理器的可用功能的快速并行轨道重建算法。这些功能包括SIMD指令集(SSE)和多线程。首先允许人们将若干数据项包装到一个寄存器中,并并行地在所有这些寄存器上操作,从而实现每个周期的更多操作。第二个功能使程序能够利用所有可用的CPU内核和硬件线程。跟踪算法的该并行版本已经与初始串行标量版进行了比较,它使用类似方法进行跟踪。在2.66GHz的计算机上实现了2倍英特尔核心I7处理器的计算机实现了487的加速因子(从730到1.5 ms /事件)。

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