Extended particle-in-cell schemes for physics in ultrastrong laser fields: Review and developments.

We review common extensions of particle-in-cell (PIC) schemes which account for strong field phenomena in laser-plasma interactions. After describing the physical processes of interest and their numerical implementation, we provide solutions for several associated methodological and algorithmic problems. We propose a modified event generator that precisely models the entire spectrum of incoherent particle emission without any low-energy cutoff, and which imposes close to the weakest possible demands on the numerical time step. Based on this, we also develop an adaptive event generator that subdivides the time step for locally resolving QED events, allowing for efficient simulation of cascades. Further, we present a unified technical interface for including the processes of interest in different PIC implementations. Two PIC codes which support this interface, PICADOR and ELMIS, are also briefly reviewed

Efficient Strict-Binning Particle-in-Cell Algorithm for Multi-Core SIMD Processors

International audienceParticle-in-Cell (PIC) codes are widely used for plasma simulations. On recent multi-core hardware, performance of these codes is often limited by memory bandwidth. We describe a multi-core PIC algorithm that achieves close-to-minimal number of memory transfers with the main memory, while at the same time exploiting SIMD instructions for numerical computations and exhibiting a high degree of OpenMP-level parallelism. Our algorithm keeps particles sorted by cell at every time step, and represents particles from a same cell using a linked list of fixed-capacity arrays, called chunks. Chunks support either sequential or atomic insertions, the latter being used to handle fast-moving particles. To validate our code, called Pic-Vert, we consider a 3d electrostatic Landau-damping simulation as well as a 2d3v transverse instability of magnetized electron holes. Performance results on a 24-core Intel Sky-lake hardware confirm the effectiveness of our algorithm, in particular its high throughput and its ability to cope with fast moving particles

Dynamic Load Balancing Based on Rectilinear Partitioning in Particle-in-Cell Plasma Simulation

This paper considers load balancing in Particle-in-Cell plasma simulation on cluster systems. We propose a dynamic load balancing scheme based on rectilinear partitioning and discuss implementation of efficient imbalance estimation and rebalancing. We analyze the impact of load balancing on performance and accuracy. On a test plasma heating problem dynamic load balancing yields nearly 2 times speedup and better scaling. On the real-world plasma target irradiation simulation load balancing allows to mitigate particle resampling and thus improve accuracy of the simulation without increasing the runtime. Balancing-related overhead in both cases are under 1.5% of total run time

Load balancing for particle-in-cell plasma simulation on multicore systems

Particle-in-cell plasma simulation is an important area of computational physics. The particle-in-cell method naturally allows parallel processing on distributed and shared memory. In this paper we address the problem of load balancing on multicore systems. While being well-studied for many traditional applications of the method, it is a relevant problem for the emerging area of particle-in-cell simulations with account for effects of quantum electrodynamics. Such simulations typically produce highly non-uniform, and sometimes volatile, particle distributions, which could require custom load balancing schemes. In this paper we present a computational evaluation of several standard and custom load balancing schemes for the particle-in-cell method on a high-end system with 96 cores on shared memory. We use a test problem with static non-uniform particle distribution and a real problem with account for quantum electrodynamics effects, which produce dynamically changing highly non-uniform distributions of particles and workload. For these problems the custom schemes result in increase of scaling efficiency by up to 20% compared to the standard OpenMP schemes

Hybrid CPU + Xeon Phi implementation of the Particle-in-Cell method for plasma simulation

This paper presents experimental results of Particle-in-Cell plasma simulation on a hybrid system with CPUs and Intel Xeon Phi coprocessors. We consider simulation of two relevant laserdriven particle acceleration regimes using the Particle-in-Cell code PICADOR. On a node of a cluster with 2 CPUs and 2 Xeon Phi coprocessors the hybrid CPU + Xeon Phi configuration allows to fully utilize the computational resources of the node. It outperforms both CPU-only and Xeon Phi-only configurations with the speedups between 1.36 x and 1.68 x

Performance aspects of collocated and staggered grids for particle-in-cell plasma simulation

We present a computational comparison of collocated and staggered uniform grids for particle-in-cell plasma simulation. Both types of grids are widely used, and numerical properties of the corresponding solvers are well-studied. However, for large-scale simulations performance is also an important factor, which is the focus of this paper. We start with a baseline implementation, apply widely-used techniques for performance optimization and measure their efficacy for both grids on a high-end Xeon CPU and a second-generation Xeon Phi processor. For the optimized version the collocated grid outperforms the staggered one by about 1.5x on both Xeon and Xeon Phi. The speedup on the Xeon Phi processor compared to Xeon is about 1.9x

Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: A first look at Knights landing

Three dimensional particle-in-cell laser-plasma simulation is an important area of computational physics. Solving state-of-the-art problems requires large-scale simulation on a supercomputer using specialized codes. A growing demand in computational resources inspires research in improving efficiency and co-design for supercomputers based on many-core architectures. This paper presents first performance results of the particle-in-cell plasma simulation code PICADOR on the recently introduced Knights Landing generation of Intel Xeon Phi. A straightforward rebuilding of the code yields a 2.43 x speedup compared to the previous Knights Corner generation. Further code optimization results in an additional 1.89 x speedup. The optimization performed is beneficial not only for Knights Landing, but also for high-end CPUs and Knights Corner. The optimized version achieves 100 GFLOPS double precision performance on a Knights Landing device with the speedups of 2.35 x compared to a 14-core Haswell CPU and 3.47 x compared to a 61-core Knights Corner Xeon Phi

Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: A first look at Knights landing

Three dimensional particle-in-cell laser-plasma simulation is an important area of computational physics. Solving state-of-the-art problems requires large-scale simulation on a supercomputer using specialized codes. A growing demand in computational resources inspires research in improving efficiency and co-design for supercomputers based on many-core architectures. This paper presents first performance results of the particle-in-cell plasma simulation code PICADOR on the recently introduced Knights Landing generation of Intel Xeon Phi. A straightforward rebuilding of the code yields a 2.43 x speedup compared to the previous Knights Corner generation. Further code optimization results in an additional 1.89 x speedup. The optimization performed is beneficial not only for Knights Landing, but also for high-end CPUs and Knights Corner. The optimized version achieves 100 GFLOPS double precision performance on a Knights Landing device with the speedups of 2.35 x compared to a 14-core Haswell CPU and 3.47 x compared to a 61-core Knights Corner Xeon Phi