Modern heterogeneous supercomputing systems are comprised of compute blades that offer CPUs and GPUs. On such systems, it is essential to move data efficiently between these different compute engines across a high-speed network. While current generation scientific applications and systems software stacks are GPU-aware, CPU threads are still required to orchestrate data moving communication operations and inter-process synchronization operations. A new GPU stream-aware MPI communication strategy called stream-triggered (ST) communication is explored to allow offloading both computation and communication control paths to the GPU. The proposed ST communication strategy is implemented on HPE Slingshot Interconnects over a new proprietary HPE Slingshot NIC (Slingshot 11) using the supported triggered operations feature. Performance of the proposed new communication strategy is evaluated using a microbenchmark kernel called Faces, based on the nearest-neighbor communication pattern in the CORAL-2 Nekbone benchmark, over a heterogeneous node architecture consisting of AMD CPUs and GPUs.
@article{arxiv.2208.04817,
title = {Exploring GPU Stream-Aware Message Passing using Triggered Operations},
author = {Naveen Namashivayam and Krishna Kandalla and Trey White and Nick Radcliffe and Larry Kaplan and Mark Pagel},
journal= {arXiv preprint arXiv:2208.04817},
year = {2022}
}