Symplectic multi-particle tracking on GPUs

Liu, Zhicong; Qiang, Ji

doi:10.1016/j.cpc.2018.02.001

Title: Symplectic multi-particle tracking on GPUs

Full Record
Other Related Research

Abstract

A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.

Authors:

^[1]; Qiang, Ji ^[2]

Chinese Academy of Sciences (CAS), Beijing (China). Inst. of High Energy Physics (IHEP), Key Lab. of Particle Acceleration Physics and Technology; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of Chinese Academy of Sciences (CAS), Beijing (China)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Publication Date:: Tue May 01 00:00:00 EDT 2018

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Lawrence Berkeley National Laboratory, Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

OSTI Identifier:: 1526520

Alternate Identifier(s):: OSTI ID: 1548755

Grant/Contract Number:: AC02-05CH11231; 2014CB845501; 201604910876

Resource Type:: Accepted Manuscript

Journal Name:: Computer Physics Communications

Additional Journal Information:: Journal Volume: 226; Journal Issue: C; Journal ID: ISSN 0010-4655

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 43 PARTICLE ACCELERATORS; Particle accelerator; Symplectic; Multi-particle tracking; GPU

Citation Formats


                    Liu, Zhicong, and Qiang, Ji. Symplectic multi-particle tracking on GPUs.  United States: N. p., 2018. 
Web.  doi:10.1016/j.cpc.2018.02.001.

Copy to clipboard


                    Liu, Zhicong, & Qiang, Ji. Symplectic multi-particle tracking on GPUs.  United States.  https://doi.org/10.1016/j.cpc.2018.02.001

Copy to clipboard


                    Liu, Zhicong, and Qiang, Ji. Tue .  
"Symplectic multi-particle tracking on GPUs".  United States.  https://doi.org/10.1016/j.cpc.2018.02.001.  https://www.osti.gov/servlets/purl/1526520.

Copy to clipboard


                    
@article{osti_1526520,

  title        = {Symplectic multi-particle tracking on GPUs},

  author       = {Liu, Zhicong and Qiang, Ji},

  abstractNote = {A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.},

  doi          = {10.1016/j.cpc.2018.02.001},

  journal      = {Computer Physics Communications},

  number       = C,

  volume       = 226,

  place        = {United States},

  year         = {Tue May 01 00:00:00 EDT 2018},

  month        = {Tue May 01 00:00:00 EDT 2018}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1016/j.cpc.2018.02.001

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 2 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Accelerating dissipative particle dynamics simulations on GPUs: Algorithms, numerics and applications

Journal Article Tang, Yu-Hang ; Karniadakis, George Em - Computer Physics Communications

We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combinedmore »« less
Cited by 33
https://doi.org/10.1016/j.cpc.2014.06.015

Full Text Available
Parallel beamlet dose calculation via beamlet contexts in a distributed multi‐GPU framework

Journal Article Neph, Ryan ; Ouyang, Cheng ; Neylon, John ; ... - Medical Physics

Purpose Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model‐based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units ( GPU s), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under‐utilizing the potentialmore »« less
Cited by 7
https://doi.org/10.1002/mp.13651
Collaborating CPU and GPU for large-scale high-order CFD simulations with complex grids on the TianHe-1A supercomputer

Journal Article Xu, Chuanfu ; Deng, Xiaogang ; Zhang, Lilun ; ... - Journal of Computational Physics

Programming and optimizing complex, real-world CFD codes on current many-core accelerated HPC systems is very challenging, especially when collaborating CPUs and accelerators to fully tap the potential of heterogeneous systems. In this paper, with a tri-level hybrid and heterogeneous programming model using MPI + OpenMP + CUDA, we port and optimize our high-order multi-block structured CFD software HOSTA on the GPU-accelerated TianHe-1A supercomputer. HOSTA adopts two self-developed high-order compact definite difference schemes WCNS and HDCS that can simulate flows with complex geometries. We present a dual-level parallelization scheme for efficient multi-block computation on GPUs and perform particular kernel optimizations formore »« less
https://doi.org/10.1016/J.JCP.2014.08.024
Latent uncertainties of the precalculated track Monte Carlo method

Journal Article Renaud, Marc-André ; Seuntjens, Jan ; Roberge, David - Medical Physics

Purpose: While significant progress has been made in speeding up Monte Carlo (MC) dose calculation methods, they remain too time-consuming for the purpose of inverse planning. To achieve clinically usable calculation speeds, a precalculated Monte Carlo (PMC) algorithm for proton and electron transport was developed to run on graphics processing units (GPUs). The algorithm utilizes pregenerated particle track data from conventional MC codes for different materials such as water, bone, and lung to produce dose distributions in voxelized phantoms. While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from the limited numbermore »« less
https://doi.org/10.1118/1.4903502
Comparisons of dosimetric accuracy and calculation time of ARCHER and MCNP5 codes for the Ir-192 brachytherapy case - Paper 1

Conference Huo, Wanli ; Chen, Zhi ; Liu, Tianyu ; ...

In the last decade, the graphics processing unit (GPU) technology has emerged as a viable player in high-performance computing. A GPU card is designed to carry out thousands of single instructions simultaneously, showing unprecedented computing power when compared with the traditional CPU technology. It has been proved that some computationally intensive programs can be accelerated drastically on the GPUs including Monte Carlo simulations. Unfortunately, existing production Monte Carlo codes cannot be easily ported to the GPU environment. To address the needs, a new Monte Carlo code ARCHER (Accelerated Radiation-transport Computations in Heterogeneous Environment) is being developed at Rensselaer Polytechnic Institute,more »« less

Similar Records