Contiguous Storage of Grid Data for Heterogeneous Computing

TL;DR

This paper introduces a new GPU-optimized storage architecture for structured Cartesian grids, enhancing performance and portability in heterogeneous computing environments, especially for sparse-domain numerical simulations.

Contribution

It presents a unified, SYCL-based data structure that simplifies GPU programming and improves scalability for sparse grid computations across heterogeneous platforms.

Findings

Enhanced GPU performance for sparse grid data handling

Improved scalability and portability in heterogeneous computing

Simplified GPU programming with a unified SYCL-based model

Abstract

Structured Cartesian grids are a fundamental component in numerical simulations. Although these grids facilitate straightforward discretization schemes, their na\"{i}ve use in sparse domains leads to excessive memory overhead and inefficient computation. Existing frameworks address are primarily optimized for CPU execution and exhibit performance bottlenecks on GPU architectures due to limited parallelism and high memory access latency. This work presents a redesigned storage architecture optimized for GPU compatibility and efficient execution across heterogeneous platforms. By abstracting low-level GPU-specific details and adopting a unified programming model based on SYCL, the proposed data structure enables seamless integration across host and device environments. This architecture simplifies GPU programming for end-users while improving scalability and portability in sparse-grid and gird-particle coupling numerical simulations.