Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment

Abstract

The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of $$\sim10^8$$ ∼ 10 8 stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.

OpenMP offload at the Exascale using Intel® GPU Max 1550: evaluation of STREAmS compressible solver

“…The standard paradigms offer the optimal solution in perspective, but currently their implementation in the various compilers is limited/optimized only for some devices, making portability effective only on paper. Nevertheless, several studies already demonstrate the potential effectiveness of standard approaches in different fields: for example, SYCL used for biological sequence alignment [15], PSTL for astrophysical applications [16], or OpenACC for CFD applications [17].…”