Performance assessment of CUDA and OpenACC in large scale combustion simulations

Abstract

GPUs have climbed up to the top of supercomputer systems making life harder to many legacy scientific codes. Nowadays, many recipes are being used in such codes' portability, without any clarity of which is the best option. We present a comparative analysis of the two most common approaches, CUDA and OpenACC, into the multi-physics CFD code Alya. Our focus is the combustion problems which are one of the most computing demanding CFD simulations. The most computing-intensive parts of the code were analyzed in detail. New data structures for the matrix assembly step have been created to facilitate a SIMD execution that benefits vectorization in the CPU and stream processing in the GPU. As a result, the CPU code has improved its performance by up to 25%. In GPU execution, CUDA has proven to be up to 2× faster than OpenACC for the assembly of the matrix. On the contrary, similar performance has been obtained in the kernels related to vector operations used in the linear solver, where there is minimal memory reuse.

Porting incompressible flow matrix assembly to FPGAs for accelerating HPC engineering simulations

“…We undertook performance and power consumption experiments of our approach against the 24-core Xeon Platinum (Cascade Lake) 8260M CPU (threaded via OpenMP) and Nvidia V100 GPU (using the existing Alya Ope-nACC implementation [19]). We explored two FPGA configurations, the two engine approach where the calculate cartesian derivatives function is resident on the FPGA, and the three engine approach where that function is running on the host CPU instead.…”