Exploring Fine-grained Task Parallelism on Simultaneous Multithreading Cores
<|reference_start|>Exploring Fine-grained Task Parallelism on Simultaneous Multithreading Cores: Nowadays, latency-critical, high-performance applications are parallelized even on power-constrained client systems to improve performance. However, an important scenario of fine-grained tasking on simultaneous multithreading CPU cores in such systems has not been well researched in previous works. Hence, in this paper, we conduct performance analysis of state-of-the-art shared-memory parallel programming frameworks on simultaneous multithreading cores using real-world fine-grained application kernels. We introduce a specialized and simple software-only parallel programming framework called Relic to enable extremely fine-grained tasking on simultaneous multithreading cores. Using Relic framework, we increase performance speedups over serial implementations of benchmark kernels by 19.1% compared to LLVM OpenMP, by 31.0% compared to GNU OpenMP, by 20.2% compared to Intel OpenMP, by 33.2% compared to X-OpenMP, by 30.1% compared to oneTBB, by 23.0% compared to Taskflow, and by 21.4% compared to OpenCilk.<|reference_end|>
@article{los2024exploring,
    title={Exploring Fine-grained Task Parallelism on Simultaneous Multithreading
  Cores},
    author={Denis Los, Igor Petushkov},
    journal={International Journal of Open Information Technologies, vol. 12,
  no. 10, pp. 144-151, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01222},
    primaryClass={cs.DC}
}