Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing [0.03%]
利用GPU加速ProtoDUNE数据处理中的机器学习推理
Tejin Cai,Kenneth Herner,Tingjun Yang et al.
Tejin Cai et al.
We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission t...
Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science [0.03%]
面向数据密集型科学的云计算存储缓存的模拟与评估
Tobias Wegner,Mario Lassnig,Peer Ueberholz et al.
Tobias Wegner et al.
A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further an...
Advances in Computing in High Energy and Nuclear Physics-Invited Papers from vCHEP 2021 [0.03%]
高能与核物理计算进展：vCHEP 2021 会议特邀报告集锦
Ian Bird,Simone Campana,Graeme A Stewart
Ian Bird
C Chen,O Cerri,T Q Nguyen et al.
C Chen et al.
We present a fast-simulation application based on a deep neural network, designed to create large analysis-specific datasets. Taking as an example the generation of W + jet events produced in s = 13 TeV proton-proton collisions, we train ...
Software Training in HEP [0.03%]
高能物理中的软件培训
Sudhir Malik,Samuel Meehan,Kilian Lieret et al.
Sudhir Malik et al.
The long-term sustainability of the high-energy physics (HEP) research software ecosystem is essential to the field. With new facilities and upgrades coming online throughout the 2020s, this will only become increasingly important. Meeting ...
R Aaij,J Albrecht,M Belous et al.
R Aaij et al.
We describe a fully GPU-based implementation of the first level trigger for the upgrade of the LHCb detector, due to start data taking in 2021. We demonstrate that our implementation, named Allen, can process the 40 Tbit/s data rate of the ...
A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution [0.03%]
基于深度神经网络的b夸克_jet能量估计及分辨方法研究
We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of s...