OpenFPM: A scalable open framework for particle and particle-mesh codes on parallel computers

TL;DR

OpenFPM is an open, scalable software framework that simplifies the development of particle and particle-mesh simulations on heterogeneous parallel computers, enhancing performance and usability for complex scientific models.

Contribution

It introduces a high-level abstraction layer for particle and mesh-based simulations, supporting shared and distributed memory systems, with portable routines and third-party library interfaces.

Findings

Demonstrated scalability in various applications

Compared favorably to existing frameworks in benchmarks

Supported diverse simulation types including SPH, MD, and DEM

Abstract

Scalable and efficient numerical simulations continue to gain importance, as computation is firmly established as the third pillar of discovery, alongside theory and experiment. Meanwhile, the performance of computing hardware grows through increasing heterogeneous parallelism, enabling simulations of ever more complex models. However, efficiently implementing scalable codes on heterogeneous, distributed hardware systems becomes the bottleneck. This bottleneck can be alleviated by intermediate software layers that provide higher-level abstractions closer to the problem domain, hence allowing the computational scientist to focus on the simulation. Here, we present OpenFPM, an open and scalable framework that provides an abstraction layer for numerical simulations using particles and/or meshes. OpenFPM provides transparent and scalable infrastructure for shared-memory and distributed-memory implementations of particles-only and hybrid particle-mesh simulations of both discrete and continuous models, as well as non-simulation codes. This infrastructure is complemented with portable implementations of frequently used numerical routines, as well as interfaces to third-party libraries. We present the architecture and design of OpenFPM, detail the underlying abstractions, and benchmark the framework in applications ranging from Smoothed-Particle Hydrodynamics (SPH) to Molecular Dynamics (MD), Discrete Element Methods (DEM), Vortex Methods, stencil codes, high-dimensional Monte Carlo sampling (CMA-ES), and Reaction-Diffusion solvers, comparing it to the current state of the art and existing software frameworks.