Sheffield MHD Accelerated Using GPUs

Using athena as a basis for implementation of SAC and SMAUG

Introduction:

Parallelization techniques have been exploited most successfully by the gaming/graphics industry with the adoption of graphical processing units (GPUs), possessing hundreds of processor cores. The opportunity has been recognized by the computational sciences and engineering communities, who have recently harnessed successfully the numerical performance of GPUs. For example, parallel magnetohydrodynamic (MHD) algorithms are important for numerical modelling of highly inhomogeneous solar, astrophysical and geophysical plasmas. Here, we describe the implementation of SMAUG, the Sheffield Magnetohydrodynamics Algorithm Using GPUs. SMAUG is a 1–3D MHD code capable of modelling magnetized and gravitationally stratified plasma. The objective of this paper is to present the numerical methods and techniques used for porting the code to this novel and highly parallel compute architecture. The methods employed are justified by the performance benchmarks and validation results demonstrating that the code successfully simulates the physics for a range of test scenarios including a full 3D realistic model of wave propagation in the solar atmosphere.

Objectives:

  1. Use GPUs to accelerate the SAC code
  2. Develop using multiple GPUs
  3. Integrate with the CPU based SAC code
  4. Use multi-architectural approach (hybrid multi-core accelerator approach) targetDP

Requirements:

CUDA-Enabled Tesla GPU Computing Product with at least compute capability 1.3. See

CUDA toolkit

The SMAUG has been developed and tested on a range of different 64 bit (and 32 bit ) linux platforms.