Abstract

Recent revisions to the ISO C++ standard have added specifications for parallel algorithms. These additions cover common use-cases, including sequence traversal, reduction, and even sorting, many of which are highly applicable in HPC, and thus represent a potential for increased performance and productivity. This study evaluates the state of the art for implementing het- erogeneous HPC applications using the latest built-in ISO C++17 parallel algorithms. We implement C++17 ports of representative HPC mini-apps that cover both compute-bound and memory bandwidth-bound applications. We then conduct benchmarks on CPUs and GPUs, comparing our ports to other widely-available parallel programming models, such as OpenMP, CUDA, and SYCL. Finally, we show that C++17 parallel algorithms are able to achieve competitive performance across multiple mini-apps on many platforms, with some notable exceptions. We also discuss several key topics, including productivity, and describe workarounds for a number of remaining issues, including index- based traversal and accelerator device/memory management.

@inproceedings{pmbs22-cpp,
  author = {Lin, Wei-Chen and Deakin, Tom and McIntosh-Smith, Simon},
  title = {{Evaluating ISO C++ Parallel Algorithms on Heterogeneous HPC Systems}},
  booktitle = {{International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems held in conjunction with Supercomputing (PMBS)}},
  year = {2022},
  publisher = {{IEEE}},
  keywords = {Conferences and Workshops},
  doi = {10.1109/PMBS56514.2022.00009},
  pdf = {https://research-information.bris.ac.uk/en/publications/evaluating-iso-c-parallel-algorithms-on-heterogeneous-hpc-systems}
}