GPU-Portable Real-Space Density Functional Theory Implementation on Unified-Memory Architectures

TL;DR

This paper introduces a GPU-portable real-space DFT code called QUMASUN, optimized for unified-memory architectures, achieving significant speedups on modern CPUs and GPUs, and demonstrating broad applicability to plasma-fusion simulations.

Contribution

A novel GPU-portable implementation of real-space DFT that simplifies porting across architectures and achieves high performance on unified-memory systems.

Findings

Achieves 2.0-2.8x speedup on MI300A and 2.3-2.4x on GH200 over Xeon.

Substantial acceleration of FFT, GEMM, and eigenvalue kernels on GPUs.

Demonstrates broad applicability to plasma-fusion simulation codes.

Abstract

We present a GPU-portable implementation of a real-space density functional theory (DFT) code ``QUMASUN'' and benchmark it on the new Plasma Simulator featuring Intel Xeon 6980P CPUs, and AMD MI300A GPUs. Additional tests were performed on an NVIDIA GH200 GPU. In particular MI300A supports unified memory and GH200 supports coherent memory interconnect, simplifying GPU porting. A lightweight C++ lambda-based layer enables CPU, CUDA, and HIP execution without OpenMP/OpenACC preprocessor directives. For diamond (216 atoms) and tungsten (128 atoms) systems, MI300A and GH200 achieve 2.0-2.8 $\times$ and 2.3-2.4 $\times$ speedups over a 256-core Xeon node. The compute-bound kernels, which are fast Fourier transforms (FFT), dense matrix-matrix multiplications (GEMM) and eigenvalue solver, show substantial acceleration on both GPUs, indicating that the present GPU-portable approach can benefit a wide range of plasma-fusion simulation codes beyond DFT.