SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

TL;DR

This paper introduces SoAx, a C++ library that efficiently manages particles in HPC by combining the performance benefits of Structure of Arrays with the ease of use of Array of Structures, optimized across CPUs, MICs, and GPUs.

Contribution

The paper presents SoAx, a generic C++ library that achieves high performance in particle handling while maintaining user-friendly data structures, using advanced template meta programming techniques.

Findings

SoA implementation outperforms AoS by several times on CPUs.

Performance gap reaches a factor of ten on MIC co-processors.

SoAx provides optimal performance across CPUs, MICs, and GPUs.

Abstract

The numerical study of physical problems often require integrating the dynamics of a large number of particles evolving according to a given set of equations. Particles are characterized by the information they are carrying such as an identity, a position other. There are generally speaking two different possibilities for handling particles in high performance computing (HPC) codes. The concept of an Array of Structures (AoS) is in the spirit of the object-oriented programming (OOP) paradigm in that the particle information is implemented as a structure. Here, an object (realization of the structure) represents one particle and a set of many particles is stored in an array. In contrast, using the concept of a Structure of Arrays (SoA), a single structure holds several arrays each representing one property (such as the identity) of the whole set of particles. The AoS approach is often implemented in HPC codes due to its handiness and flexibility. For a class of problems, however, it is know that the performance of SoA is much better than that of AoS. We confirm this observation for our particle problem. Using a benchmark we show that on modern Intel Xeon processors the SoA implementation is typically several times faster than the AoS one. On Intel's MIC co-processors the performance gap even attains a factor of ten. The same is true for GPU computing, using both computational and multi-purpose GPUs. Combining performance and handiness, we present the library SoAx that has optimal performance (on CPUs, MICs, and GPUs) while providing the same handiness as AoS. For this, SoAx uses modern C++ design techniques such template meta programming that allows to automatically generate code for user defined heterogeneous data structures.