Evaluation of likelihood functions on CPU and GPU devices

Sverre Jarp, Alfio Lazzaro, Julien Leduc, Andrzej Nowak, Yngve Sneen Lindal
CERN openlab, Geneva, Switzerland
Journal of Physics: Conference Series, Volume 368, conference 1, 2012


   title={Evaluation of likelihood functions on CPU and GPU devices},

   author={Jarp, S. and Lazzaro, A. and Leduc, J. and Nowak, A. and Lindal, Y.S.},

   booktitle={Journal of Physics: Conference Series},





   organization={IOP Publishing}


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We describe parallel implementations of an algorithm used to evaluate the likelihood function used in data analysis. The implementations run, respectively, on CPU and GPU, and both devices cooperatively (hybrid). CPU and GPU implementations are based on OpenMP and OpenCL, respectively. The hybrid implementation allows the application to run also on multi-GPU systems (not necessarily of the same type). The hybrid case uses a scheduler so that the workload needed for the evaluation of function is split and balanced in corresponding sub-workloads to be executed in parallel on each device, i. e. CPU-GPU or multi-CPUs. We present the results of the scalability when running on CPU. Then we show the comparison of the performance of the GPU implementation on different hardware systems from different vendors, and the performance when running in the hybrid case. The tests are based on likelihood functions from real data analysis carried out in the high energy physics community.
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