scholarly journals A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU Architectures

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 91612-91626 ◽  
Author(s):  
Placido Fernandez Declara ◽  
Daniel Hugo Campora Perez ◽  
Javier Garcia-Blas ◽  
Dorothea Vom Bruch ◽  
J. Daniel Garcia ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Particle Tracking ◽  
High Energy Physics ◽  
High Energy ◽  
Computing Algorithm ◽  
Gpu Architectures ◽  
Energy Physics

scholarly journals General-purpose parallel computing in a high-energy physics experiment at CERN

1996 ◽  
pp. 251-257 ◽  
Author(s):  
J. Apostolakis ◽  
L. M. Bertolotto ◽  
C. E. Bruschini ◽  
P. Calafiura ◽  
F. Gagliardi ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Energy Physics ◽  
High Energy ◽  
General Purpose ◽  
Physics Experiment ◽  
Energy Physics

TrackML: A High Energy Physics Particle Tracking Challenge

2018 ◽  
Author(s):  
Polo Calafiura ◽  
Victor Estrade ◽  
Cecile Germaint ◽  
Isabelle Guyon ◽  
Ed Moyse ◽  
...  
Keyword(s):  
Particle Tracking ◽  
High Energy Physics ◽  
High Energy ◽  
Energy Physics

scholarly journals PARALLEL COMPUTING: SOME ACTIVITIES IN HIGH ENERGY PHYSICS

1990 ◽  
Vol 01 (04) ◽  
pp. 343-353
Author(s):  
IAN WILLERS
Keyword(s):  
Complex System ◽  
Parallel Computing ◽  
High Energy Physics ◽  
High Energy ◽  
General Purpose ◽  
Front End ◽  
Computer Centers ◽  
Energy Physics

This paper examines some activities in High Energy Physics that utilise parallel computing. The topic includes all computing from the proposed SIMD front end detectors, the farming applications, high-powered RISC processors and the large machines in the computer centers. We start by looking at the motivation behind using parallelism for general purpose computing. The developments around farming are then described from its simplest form to the more complex system in Fermilab. Finally, there is a list of some developments that are happening close to the experiments.

scholarly journals Dimension Reduction Using Quantum Wavelet Transform on a High-Performance Reconfigurable Computer

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Naveed Mahmud ◽  
Esam El-Araby
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Spatial Resolution ◽  
Particle Tracking ◽  
High Performance ◽  
High Energy Physics ◽  
High Spatial Resolution ◽  
High Energy ◽  
Resolution Data ◽  
Energy Physics

The high resolution of multidimensional space-time measurements and enormity of data readout counts in applications such as particle tracking in high-energy physics (HEP) is becoming nowadays a major challenge. In this work, we propose combining dimension reduction techniques with quantum information processing for application in domains that generate large volumes of data such as HEP. More specifically, we propose using quantum wavelet transform (QWT) to reduce the dimensionality of high spatial resolution data. The quantum wavelet transform takes advantage of the principles of quantum mechanics to achieve reductions in computation time while processing exponentially larger amount of information. We develop simpler and optimized emulation architectures than what has been previously reported, to perform quantum wavelet transform on high-resolution data. We also implement the inverse quantum wavelet transform (IQWT) to accurately reconstruct the data without any losses. The algorithms are prototyped on an FPGA-based quantum emulator that supports double-precision floating-point computations. Experimental work has been performed using high-resolution image data on a state-of-the-art multinode high-performance reconfigurable computer. The experimental results show that the proposed concepts represent a feasible approach to reducing dimensionality of high spatial resolution data generated by applications such as particle tracking in high-energy physics.

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