GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
复制标题
GeantV：HEP 中并发矢量粒子输运模拟原型的结果
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Y. Zhang
中科院分区：
文献类型：
--
作者：
G. Amadio;A. Ananya;J. Apostolakis;M. Bandieramonte;S. Banerjee;A. Bhattacharyya;Calebe P. Bianchini;G. Bitzes;P. Canal;F. Carminati;O. Chaparro;G. Cosmo;J. D. F. Licht;V. Drogan;L. Duhem;D. Elvira;J. Fuentes;A. Gheata;M. Gheata;M. Gravey;I. Goulas;F. Hariri;S. Jun;D. Konstantinov;H. Kumawat;J. Lima;A. Maldonado;J. Mart'inez;P. Mato;T. Nikitina;S. Novaes;M. Novak;K. Pedro;W. Pokorski;A. Ribon;R. Schmitz;R. Seghal;O. Shadura;E. Tcherniaev;S. Vallecorsa;S. Wenzel;Y. Zhang
在大型强子对撞机 (LHC) 的前两次运行中，完整探测器模拟是所有 CERN 实验软件堆栈中最大的 CPU 消耗之一。 2010 年初，预测模拟需求将随着亮度的增加而线性扩展，仅通过计算资源的增加来部分补偿。由于固有的精度限制，将快速仿真方法扩展到更多用例（覆盖仿真预算的较大部分）只是解决方案的一部分。其余部分对应于通过几个因素加速模拟软件，这是使用当前代码库的简单优化无法实现的。在此背景下，GeantV研发项目启动，旨在重新设计遗留的粒子传输代码，以使它们受益于矢量化等细粒度并行功能，同时也受益于增加的代码和数据局部性。本文广泛介绍了本次研发的成果和成就，以及从测试原型中得到的结论和经验教训。
Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.