Engineering topics
Sun, Changchun
Publications and source records attributed to Sun, Changchun.
Physics Design Progress towards a Diffraction Limited Upgrade of the ALS
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Electron Beam Polarization Measurement Using Touschek Lifetime Technique
Electron beam loss due to intra-beam scattering, the Touschek effect, in a storage ring depends on the electron beam polarization. The polarization of an electron beam can be determined from the difference in the Touschek lifetime compared with an unpolarized beam. In this paper, we report on a systematic experimental procedure recently developed at Duke FEL laboratory to study the radiative polarization of a stored electron beam. Using this technique, we have successfully observed the radiative polarization build-up of an electron beam in the Duke storage ring, and determined the equilibrium degree of polarization and the time constant of the polarization build-up process.
GPU COMPUTING FOR PARTICLE TRACKING
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. General purpose Computation on Graphics Processing Units (GPGPU) bring massive parallel computing capabilities to numerical calculation. However, the unique architecture of GPU requires a comprehensive understanding of the hardware and programming model to be able to well optimize existing applications. In the field of accelerator physics, the dynamic aperture calculation of a storage ring, which is often the most time consuming part of the accelerator modeling and simulation, can benefit from GPU due to its embarrassingly parallel feature, which fits well with the GPU programming model. In this paper, we use the Tesla C2050 GPU which consists of 14 multi-processois (MP) with 32 cores on each MP, therefore a total of 448 cores, to host thousands ot threads dynamically. Thread is a logical execution unit of the program on GPU. In the GPU programming model, threads are grouped into a collection of blocks Within each block, multiple threads share the same code, and up to 48 KB of shared memory. Multiple thread blocks form a grid, which is executed as a GPU kernel. A simplified code that is a subset of Tracy++ [2] is developed to demonstrate the possibility of using GPU to speed up the dynamic aperture calculation by having each thread track a particle.
HPC CLOUD APPLIED TO LATTICE OPTIMIZATION
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.