Parallel PIC plasma simulation through particle decomposition techniques

Open Boundary Condition for Particle Simulation in Magnetic Reconnection Research

“…PASMO is parallelized with High Performance Fortran [39]. For data layout, all field data are duplicated in each parallel process, but particle data are distributed among them [44]. We invent an algorithm for the open boundary of particles, in which an operation for outgoing and incoming particles is performed in each processor, and the only reduction operation for the number of particles is executed during data transfer.…”

Hierarchical MPI+OpenMP Implementation of Parallel PIC Applications on Clusters of Symmetric MultiProcessors

“…This approach yields benefits and problems that are complementary to those yielded by the particle-decomposition one [5]: on the one hand, the memory resources required to each node are approximately reduced by the number of nodes; an almost linear scaling of the attainable physical-space resolution (i.e., the maximum size of the spatial grid) with the number of nodes is then obtained. On the other hand, inter-node communication is required to update the fields at the boundary between two different portions of the domain, as well as to transfer those particles that migrate from one domain portion to another.…”

“…Two main strategies have been developed for the workload decomposition related to porting PIC codes on parallel systems: the particle decomposition strategy [5] and the domain decomposition one [7,6]. Domain decomposition consists in assigning different portions of the physical domain and the corresponding portions of the grid to different processes, along with the particles that reside on them.…”

“…Such workload decomposition strategies can be applied both for distributedmemory parallel systems [6,5] and shared-memory ones [4]. They can also be combined, when porting a PIC code on a hierarchical distributed-shared memory system (e.g., a cluster of SMPs), in two-level strategies: a distributed-memory level decomposition (among the n node computational nodes), and a sharedmemory one (among the n proc processors of each node).…”

A Performance‐Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

“…It is generally accepted, however, that such an approach, based on manual partition of data, insertion of communication library calls, handling of boundary cases, is very complicated, time consuming and error prone, and af-fects the portability of the resulting program. In order to avoid these features, it is worth to resort, for distributed architectures, to the particle decomposition [5] technique, which is suited to be implemented, with relatively little effort, by the use of high-level programming languages, such as the High Performance Fortran (HPF) [8]. Particle decomposition consists in statically distributing the particle population among the computational nodes, while replicating the data relative to grid quantities.…”