# Exploring Fully Offloaded GPU Stream-Aware Message Passing

arXiv: https://arxiv.org/abs/2306.15773 · HTML: https://mlsanity.com/paper/2306.15773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [GPU-centric Communication Schemes for HPC and ML Applications](https://mlsanity.com/paper/2503.24230/) (2025)

## Abstract (arXiv)

Modern heterogeneous supercomputing systems are comprised of CPUs, GPUs, and high-speed network interconnects. Communication libraries supporting efficient data transfers involving memory buffers from the GPU memory typically require the CPU to orchestrate the data transfer operations. A new offload-friendly communication strategy, stream-triggered (ST) communication, was explored to allow offloading the synchronization and data movement operations from the CPU to the GPU. A Message Passing Interface (MPI) one-sided active target synchronization based implementation was used as an exemplar to illustrate the proposed strategy. A latency-sensitive nearest neighbor microbenchmark was used to explore the various performance aspects of the implementation. The offloaded implementation shows significant on-node performance advantages over standard MPI active RMA (36%) and point-to-point (61%) communication. The current multi-node improvement is less (23% faster than standard active RMA but 11% slower than point-to-point), but plans are in progress to purse further improvements.