RAFI framework simplifies multi-GPU ray/work forwarding across nodes

A team of researchers from multiple institutions has released RAFI, a new software framework designed to simplify the development of distributed, data-parallel applications spanning multiple GPUs and nodes. Built on CUDA and MPI, RAFI provides a high-level interface that allows CUDA kernels to easily forward work items—such as rays or other computational tasks—between different GPUs without requiring developers to manage the underlying communication complexities. The framework handles all necessary CUDA memory transfers, MPI messaging, and synchronization, enabling developers to focus on application logic rather than distributed system plumbing.

RAFI is especially relevant for fields like computer graphics and scientific visualization, where workloads often require migrating rays or intermediate results across nodes. The authors demonstrate RAFI's potential in several example applications, showing how it can reduce development effort for multi-GPU rendering pipelines. While the paper does not benchmark raw performance gains, the abstraction layer promises to make distributed GPU programming more accessible. The full source code is not yet linked, but the arXiv paper (cs.DC) provides implementation details for researchers and engineers looking to adopt or extend the framework.