Author: Song, K.
Paper Title Page
WEP150 GPU Computing for Particle Tracking 1764
 
  • H. Nishimura, S. James, K. Muriki, Y. Qin, K. Song, C. Sun
    LBNL, Berkeley, California, USA
 
  Funding: Work supported by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231
This is a feasibility study of using a modern Graphics Processing Unit (GPU) to parallelize the accelerator particle tracking code. To demonstrate the massive parallelization features provided by GPU computing, a simplified TracyGPU program is developed for dynamic aperture calculation. Performances, issues, and challenges from introducing GPU are also discussed.
 
 
WEP151 HPC Cloud Applied to Lattice Optimization 1767
 
  • C. Sun, S. James, K. Muriki, H. Nishimura, Y. Qin, K. Song
    LBNL, Berkeley, California, USA
 
  Funding: Work supported by the U.S. Department of Energy under Contract No. DE-AC02-05CH11231
As Cloud services gain in popularity for enterprise use, vendors are now turning their focus towards providing cloud services suitable for scientific computing. Recently, Amazon Elastic Compute Cloud (EC2) introduced the new Cluster Compute Instances (CCI), a new instance type specifically designed for High Performance Computing (HPC) applications. At Berkeley Lab, the physicists at the Advanced Light Source (ALS) have been running Lattice Optimization on a local cluster, but the queue wait time and the flexibility to request compute resources when needed are not ideal for rapid development work. To explore alternatives, for the first time we investigate running the Lattice Optimization application on Amazon’s new CCI to demonstrate the feasibility and trade-offs of using public cloud services for science.