Presentation
Lagrangian Particle-Tracking in GPU-Enabled Extreme Scale Turbulence Simulations
DescriptionMany practical turbulent flow phenomena are naturally studied using a Lagrangian approach that treats the fluid medium as a collection of infinitesimal fluid particles. We present a GPU-accelerated algorithm for tracking particles in direct numerical simulations of isotropic turbulence, scaling up to 32768^3 using the world's first exascale computer (Frontier). Cubic spline interpolation is used to compute the particle velocity as the particles wander among sub-domains held by different parallel processes. We use a programming model that minimizes host-device data transfer by leveraging
memory parity between the CPU and GPU, reduces communication costs through a local decomposition for the particles, and uses OpenMP offloading on the GPU to accelerate the computation of cubic spline coefficients. The result is an algorithm shown to attain good weak scaling and strong scaling at problem sizes close to the capacity supported by the machine, at a cost nearly independent of the particle count.
memory parity between the CPU and GPU, reduces communication costs through a local decomposition for the particles, and uses OpenMP offloading on the GPU to accelerate the computation of cubic spline coefficients. The result is an algorithm shown to attain good weak scaling and strong scaling at problem sizes close to the capacity supported by the machine, at a cost nearly independent of the particle count.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX