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A new parallel algorithm utilizing partitioned global address space (PGAS)programming model to achieve high scalability is reported for particle trackingin direct numerical simulations of turbulent flow. The work is motivated by thedesire to obtain Lagrangian information necessary for the study of turbulentdispersion at the largest problem sizes feasible on current and next-generationmulti-petaflop supercomputers. A large population of fluid particles isdistributed among parallel processes dynamically, based on instantaneousparticle positions such that all of the interpolation information needed foreach particle is available either locally on its host process or neighboringprocesses holding adjacent sub-domains of the velocity field. With cubicsplines as the preferred interpolation method, the new algorithm is designed tominimize the need for communication, by transferring between adjacent processesonly those spline coefficients determined to be necessary for specificparticles. This transfer is implemented very efficiently as a one-sidedcommunication, using Co-Array Fortran (CAF) features which facilitate smalldata movements between different local partitions of a large global array.Detailed benchmarks are obtained on the Cray petascale supercomputer BlueWaters at the University of Illinois, Urbana-Champaign. For operations on theparticles in a $8192^3$ simulation ($0.55$ trillion grid points) on $262,144$Cray XE6 cores, the new algorithm is found to be orders of magnitude fasterrelative to a prior algorithm in which each particle is tracked by the sameparallel process at all times. Improving support of PGAS models on majorcompilers suggests that this algorithm will be of wider applicability on mostupcoming supercomputers.

A highly scalable particle tracking algorithm using partitioned global address space (PGAS) programming for extreme-scale turbulence simulations