TUADC2
Tech-X, Boulder, Colorado, USA
DESY, Hamburg, Germany
KEK, Ibaraki, Japan
Over the past several years, Forest has added spin motion and related capabilities to PTC [1]. The capabilities include, via FPP [2], the computation of a spin normal form. This normal form, and the associated normalising maps for orbit and spin, provide us with immediate access to important quantities, including the invariant spin field and resonance strengths. The algorithms in PTC/FPP thus represent an alternative approach to computing significant quantities for the analysis of spin dynamics in particle accelerators. We describe the PTC algorithm, note similarities with the case of the orbital normal form, and give illustrative examples. In particular, we apply PTC to the well-understood COSY lattice, and we compare the results computed by PTC with those computed by the well-tested code EpsSLICK [3].
[1] E. Forest, F. Schmidt, and E. McIntosh, KEK Report 2002-3.
[2] E. Forest, Y. Nogiwa, and F. Schmidt, ICAP2006, p. 191-193.
[3] D. Barber, DESY Report, DESY-2009-15.
THP03
Tech-X, Boulder, Colorado, USA
DESY, Hamburg, Germany
BNL, Upton, Long Island, New York, USA
Graphics processing units (GPUs) have now become powerful tools for scientific computation. Here we present our work on using GPUs to speed the tracking of both orbital and spin degrees of freedom in particle accelerators. This work includes the development of new spin integrators that are both fast and accurate. We have also developed an integrated set of tools for analysing the results. To demonstrate the utility of these new tools, we use them to study the spin dynamics of protons in the Relativistic Heavy Ion Collider at Brookhaven National Lab.
THP10
Tech-X, Boulder, Colorado, USA
ANL, Argonne, USA
Efficient implementation of general-purpose particle tracking on GPUs can result in significant performance benefits to large scale particle tracking and tracking-based lattice optimization simulations. We present the latest results of our work on accelerating Argonne National Lab's accelerator simulation code ELEGANT* using CUDA-enabled GPUs**. We provide a list of ELEGANT's beamline elements ported to GPUs, identify performance-limiting factors, and briefly discuss optimization techniques for efficient utilization of the device memory space, with an emphasis on register usage. We also present a novel hardware-assisted technique for efficiently calculating a histogram from a large distribution of particle coordinates, and compare this to data-parallel implementations. Finally, we discuss results of simulations performed with realistic test lattices, and give a brief outline of future work on GPU-enabled version of ELEGANT.
* M. Borland, "elegant: A Flexible SDDS-compliant Code for Accel. Simulation", APS LS-287 (2000); Y. Wang, M. Borland, Proc. of PAC07, THPAN095 (2007)
** CUDA home page: http://www.nvidia.com/cuda