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The longitudinal tracking engine of the particle accelerator simulationapplication PyHEADTAIL shows a heavy potential for parallelisation. For basicbeam circulation, the tracking functionality with the leap-frog algorithm isextracted and compared between a sequential C and a concurrent CUDA C APIimplementation for 1 million revolutions. Including the sequential data I/O inboth versions, a pure speedup of up to S = 100 is observed which is in theorder of magnitude of what is expected from Amdahl's law. From O(100)macro-particles on the overhead of initialising the GPU CUDA device appearsoutweighed by the concurrent computations on the 448 available CUDA cores.

Parallelisation of PyHEADTAIL, a Collective Beam Dynamics Code for Particle Accelerator Physics