GPU-accelerated Monte Carlo simulations of dense stellar systems

Bharath Pattabiraman*, Stefan Umbreit, Wei-Keng Liao, Frederic A Rasio, Vicky Kalogera, Gokhan Memik, Alok Nidhi Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Computing the interactions between the stars within dense stellar clusters is a problem of fundamental importance in theoretical astrophysics. However, simulating realistic sized clusters of about 106 stars is computationally intensive and often takes a long time to complete. This paper presents the parallelization of a Monte Carlo method-based algorithm for simulating stellar cluster evolution on programmable Graphics Processing Units (GPUs). The kernels of this algorithm involve numerical methods of root-bisection and von Neumann rejection. Our experiments show that although these kernels exhibit data dependent decision making and unavoidable non-contiguous memory accesses, the GPU can still deliver substantial near-linear speed-ups which is unlikely to be achieved on a CPU-based system. For problem sizes ranging from 106 to 7 × 106 stars, we obtain up to 28x speedups for these kernels, and a 2x overall application speedup on an NVIDIA GTX280 GPU over the sequential version run on an AMD

Original languageEnglish (US)
Title of host publication2012 Innovative Parallel Computing, InPar 2012
StatePublished - 2012
Event2012 Innovative Parallel Computing, InPar 2012 - San Jose, CA, United States
Duration: May 13 2012May 14 2012

Publication series

Name2012 Innovative Parallel Computing, InPar 2012


Other2012 Innovative Parallel Computing, InPar 2012
Country/TerritoryUnited States
CitySan Jose, CA


  • CUDA
  • Graphics processing unit (GPU)
  • Monte Carlo simulation
  • bisection method
  • multi-scale simulation
  • parallel random number generator

ASJC Scopus subject areas

  • Software


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