MSCCL++: Rethinking GPU Communication Abstractions for AI Inference
Published in ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2026
Recommended citation: Changho Hwang, Peng Cheng, Roshan Dathathri, Abhinav Jangda, Saeed Maleki, Madan Musuvathi, Olli Saarikivi, Aashaka Shah, Ziyue Yang, Binyang Li, Caio Rocha, Qinghua Zhou, Mahdieh Ghazimirsaeed, Sreevatsa Anantharamu, Jithin Jose, “MSCCL++: Rethinking GPU Communication Abstractions for AI Inference,” Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2026. https://dl.acm.org/doi/10.1145/3779212.3790188
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Best Paper - Honorable Mention at ASPLOS’26
Abstract
AI applications increasingly run on fast-evolving, heterogeneous hardware to maximize performance, but general-purpose libraries lag in supporting these features. Performance-minded programmers often build custom communication stacks that are fast but error-prone and non-portable.
This paper introduces MSCCL++, a design methodology for developing high-performance, portable communication kernels. It provides (1) a low-level, performance-preserving primitive interface that exposes minimal hardware abstractions while hiding the complexities of synchronization and consistency, (2) a higher-level DSL for application developers to implement workload-specific communication algorithms, and (3) a library of efficient algorithms implementing the standard collective API, enabling adoption by users with minimal expertise.
Compared to state-of-the-art baselines, MSCCL++ achieves geomean speedups of 1.7× (up to 5.4×) for collective communication and 1.2× (up to 1.38×) for AI inference workloads. MSCCL++ is in production of multiple AI services provided by Microsoft Azure, and has also been adopted by RCCL, the GPU collective communication library maintained by AMD. MSCCL++ is open source and available at https://github.com/microsoft/mscclpp. Our two years of experience with MSCCL++ suggests that its abstractions are robust, enabling support for new hardware features, such as multimem, within weeks of development.