Composing Distributed Computations Through Task and Kernel Fusion
<|reference_start|>Composing Distributed Computations Through Task and Kernel Fusion: We introduce Diffuse, a system that dynamically performs task and kernel fusion in distributed, task-based runtime systems. The key component of Diffuse is an intermediate representation of distributed computation that enables the necessary analyses for the fusion of distributed tasks to be performed in a scalable manner. We pair task fusion with a JIT compiler to fuse together the kernels within fused tasks. We show empirically that Diffuse's intermediate representation is general enough to be a target for two real-world, task-based libraries (cuNumeric and Legate Sparse), letting Diffuse find optimization opportunities across function and library boundaries. Diffuse accelerates unmodified applications developed by composing task-based libraries by 1.86x on average (geo-mean), and by between 0.93x--10.7x on up to 128 GPUs. Diffuse also finds optimization opportunities missed by the original application developers, enabling high-level Python programs to match or exceed the performance of an explicitly parallel MPI library.<|reference_end|>
@article{yadav2024composing,
    title={Composing Distributed Computations Through Task and Kernel Fusion},
    author={Rohan Yadav, Shiv Sundram, Wonchan Lee, Michael Garland, Michael
  Bauer, Alex Aiken, Fredrik Kjolstad},
    journal={arXiv preprint arXiv:2406.18109},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18109},
    primaryClass={cs.DC}
}