Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers

Bei Wang, Stephane Ethier, William Tang, Khaled Ibrahim, Kamesh Madduri, Samuel Williams, Leonid Oliker
Princeton Institute of Computational Science and Engineering, Princeton University, Princeton, NJ, USA
arXiv:1510.05546 [cs.DC], (19 Oct 2015)


   title={Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers},

   author={Wang, Bei and Ethier, Stephane and Tang, William and Ibrahim, Khaled and Madduri, Kamesh and Williams, Samuel and Oliker, Leonid},






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The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P’s multiple levels of parallelism, including inter-node 2D domain decomposition and particle decomposition, as well as intra-node shared memory partition and vectorization have enabled pushing the scalability of the PIC method to extreme computational scales. In this paper, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) co-processors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of Ion-Temperature-Gradient (ITG) driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects and network topologies. These performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide.
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